bluuebunny/crossref_metadata_2025 huggingface dataset:
Depolarization of Vector Light Beams on Propagation in Free Space
Authors: Petrov Nikolai
Published: March 2022

Nonparaxial propagation of the vector vortex light beams in free space was investigated theoretically. Propagation-induced polarization changes in vector light beams with different spatial intensity distributions were analyzed. It is shown that the hybrid vector Bessel modes with polarization-OAM (orbital angular momentum) entanglement are the exact solutions of the vector Helmholtz equation. Decomposition of arbitrary vector beams in the initial plane z = 0 into these polarization-invariant beams with phase and polarization singularities was used to analyze the evolution of the polarization of light within the framework of the 2 × 2 coherency matrix formalism. It is shown that the 2D degree of polarization decreases with distance if the incident vector beam is not the modal solution. The close relationship of the degree of polarization with the quantum-mechanical purity parameter is emphasized.
Numerical Relativity as a New Tool for Fundamental Cosmology
Authors: Ijjas Anna
Published: March 2022

Advances in our understanding of the origin, evolution, and structure of the universe have long been driven by cosmological perturbation theory, model building, and effective field theory. In this review, numerical relativity is introduced as a powerful new complementary tool for fundamental cosmology. To illustrate its power, applications of numerical relativity are discussed to studying the robustness of slow contraction and inflation in homogenizing, isotropizing, and flattening the universe beginning from generic unsmooth initial conditions. In particular, it is described how recent numerical relativity studies of slow contraction have revealed a novel, non-linear smoothing mechanism based on ultralocality that challenges the conventional view on what is required to explain the large-scale homogeneity and isotropy of the observable universe.
Analysis of the Orchidaceae Diversity in the Pululahua Reserve, Ecuador: Opportunities and Constraints as Regards the Biodiversity Conservation of the Cloud Mountain Forest
Authors: Mites Mariana , García-Mozo Herminia , Galán Carmen , Oña Edwin
Published: March 2022

The Pululahua Geobotanical Reserve is a protected natural area in the cloud mountain forest of Ecuador, so rich in orchid species despite being a volcanic area still under threat of volcanic activity. A comparative biodiversity study of orchids was carried out in two different sectors, Chaupisacha (CH) and La Reventazón (LR). Data were collected in 1 ha plots in each sector, in which all the orchids found were counted and two individuals of each species were retained. Immature individuals were conserved in a plant nursery until flowering. In CH, there were 922 individuals grouped into 24 genera and 55 species, while LR had 9196 individuals grouped into 26 genera and 46 species; only 14 species were found in both sectors. Different density and diversity indexes were calculated. The density (ind./100 m2) of CH was 0.96, while that of LR was 185.92. Simpson’s diversity (1 − λ) attained CH 0.903 ± 0.01 and LR 0.85 ± 0.01. The orchid diversity measured by the Shannon-Wiener diversity index (H′) was 1.29 for CH, differing significantly from that of LR (H′ 1.02). The medium equity (Jaccard’s J′) found was 0.61 in CH and 90.78 in LR. Limitations as regards the natural dispersion of orchids seemed to favor endemism. Some species, such as Dracula felix and Restrepia guttulate, are threatened with disappearance from the wild or are vulnerable, as is the case for Epidendrum polyanthogastrium. A lack of information on the phenology and anthropic impacts in the area limit the conservation of species, signifying that new protected figures and seed banks are necessary, especially in CH, owing to its high diversity of orchids.
Adaptive Algorithm for the Generation of Superconfigurations in Hot-Plasma Opacity Calculations
Authors: Pain Jean-Christophe
Published: March 2022

In hot plasmas, such as the ones encountered in astrophysics or laser-fusion studies, the number of ionic excited states may become huge, and the relevant electron configurations cannot always be handled individually. The Super Transition Array approach enables one to calculate the massic photo-absorption cross-section (or radiative opacity) in a statistical manner consisting of grouping configurations close in energy into superconfigurations. One of the main issues of the method, beyond its spectral resolution, is the determination of the most relevant configurations that contribute to opacity. In this work, we discuss different aspects of the generation of superconfigurations in a hot plasma and propose a new adaptive algorithm.
EGDMA- and TRIM-Based Microparticles Imprinted with 5-Fluorouracil for Prolonged Drug Delivery
Authors: Cegłowski Michał , Kurczewska Joanna , Lusina Aleksandra , Nazim Tomasz , Ruszkowski Piotr
Published: March 2022

Imprinted materials possess designed cavities capable of forming selective interactions with molecules used in the imprinting process. In this work, we report the synthesis of 5-fluorouracil (5-FU)-imprinted microparticles and their application in prolonged drug delivery. The materials were synthesized using either ethylene glycol dimethacrylate (EGDMA) or trimethylolpropane trimethacrylate (TRIM) cross-linkers. For both types of polymers, methacrylic acid was used as a functional monomer, whereas 2-hydroxyethyl methacrylate was applied to increase the final materials’ hydrophilicity. Adsorption isotherms and adsorption kinetics were investigated to characterize the interactions that occur between the materials and 5-FU. The microparticles synthesized using the TRIM cross-linker showed higher adsorption properties towards 5-FU than those with EGDMA. The release kinetics was highly dependent upon the cross-linker and pH of the release medium. The highest cumulative release was obtained for TRIM-based microparticles at pH 7.4. The IC50 values proved that 5-FU-loaded TRIM-based microparticles possess cytotoxic activity against HeLa cell lines similar to pure 5-FU, whereas their toxicity towards normal HDF cell lines was ca. three times lower than for 5-FU.
Experimental and Digimat-FE Based Representative Volume Element Analysis of Dye-Mixed Colored Resin and Carbon Fiber
Authors: Sim Jee-Hyun , Yeo Dong-Hyeon , Yoon Hyun-Sung , Yu Seong-Hun , Lee Do-Hyun , Bae Jin-Seok
Published: March 2022

Recently, the automobile industry has demanded weight reduction, so research on materials is being actively conducted. Among this research, carbon fiber-reinforced composite materials are being studied a lot in the automobile industry due to their excellent mechanical properties, chemical resistance, and heat resistance. However, carbon fiber-reinforced composite materials have disadvantages, in that they are not free from color selection, and have weak interfacial bonding strength. In this study, a colored epoxy resin was prepared by mixing epoxy—which is a thermosetting resin according to the pigment concentration (0.1, 0.3, 0.5, 1.0 wt%)—and curing shrinkage. Thermal expansion characteristics were analyzed and the concentration of 0.5 wt% pigment showed the lowest shrinkage and thermal expansion characteristics. In addition, to measure the interfacial shear strength (IFSS) of the carbon fiber and the colored epoxy resin, the IFSS was obtained by performing a microdroplet debonding test, and the strength of the pigment concentration of 0.5 wt% was reduced to a relatively low level. Through these experiments, it was determined that an epoxy resin in which 0.5 wt% pigment is mixed is the optimal condition. Finally, using the composite material modeling software (Digimat 2020.0), the representative volume element (RVE) of the meso-scale was set, and interfacial properties of carbon fibers and colored epoxy resins were analyzed by interworking with general-purpose finite element analysis software (Abaqus CAE).
Optimization of the Spinneret Rotation Speed and Airflow Parameters for the Nozzleless Forcespinning of a Polymer Solution
Authors: Skrivanek Josef , Holec Pavel , Batka Ondrej , Bilek Martin , Pokorny Pavel
Published: March 2022

This paper addresses the changing of the process parameters of nozzleless centrifugal spinning (forcespinning). The primary aim of this study was to determine the dependence of the final product on the dosing of the polymer, the rotation speed of the spinneret and the airflow in order to determine the extent of the technological applicability of aqueous polyvinyl alcohol (PVA) and its modifications. PVA was chosen because it is a widely used polymeric solution with environmentally friendly properties and good biodegradability. It is used in the health care and food packaging sectors. The nanofibrous layers were produced by means of a mobile handheld spinning device of our own construction. This mobile application of the spinning machine has several limitations compared to stationary laboratory equipment, mainly due to dimensional limitations. The uniqueness of our device lies in the possibility of its actual use outside the laboratory. In addition to improved mobility, another exciting feature is the combination of nozzleless forcespinning and fiber application using airflow. Dosing, the rotation speed of the spinnerets and the targeted and controlled use of air comprise the fundamental technological parameters for many devices that operate on a centrifugal force system. The rotation rate of the spinnerets primarily affects the production of fibers and their quality, while the airflow acts as a fiber transport and drying medium. The quality of the fibers was evaluated following the preparation of a testing set for the fiber layers. The most suitable combinations of rotation speed and airflow were then used in subsequent experiments to determine the ideal settings for the device. The solution was then modified by reducing the concentration to 16% and adding a surfactant, thus leading to a reduction in the diameters of the resulting fibers. The nanofiber layers so produced were examined using a scanning electron microscope (SEM) in order to analyze the number of defects and to statistically evaluate the fiber diameters.
Durability and Performance of Encapsulant Films for Bifacial Heterojunction Photovoltaic Modules
Authors: Baiamonte Marilena , Colletti Claudio , Ragonesi Antonino , Gerardi Cosimo , Dintcheva Nadka Tz.
Published: March 2022

Energy recovery from renewable sources is a very attractive, and sometimes, challenging issue. To recover solar energy, the production of photovoltaic (PV) modules becomes a prosperous industrial certainty. An important material in PV modules production and correct functioning is the encapsulant material and it must have a good performance and durability. In this work, accurate characterizations of performance and durability, in terms of photo- and thermo-oxidation resistance, of encapsulants based on PolyEthylene Vinyl Acetate (EVA) and PolyOlefin Elastomer (POE), containing appropriate additives, before (pre-) and after (post-) lamination process have been carried out. To simulate industrial lamination processing conditions, both EVApre-lam and POEpre-lam sheets have been subjected to prolonged thermal treatment upon high pressure. To carry out an accurate characterization, differential scanning calorimetry, rheological and mechanical analysis, FTIR and UV-visible spectroscopy analyses have been performed on pre- and post-laminated EVA and POE. The durability, in terms of photo- and thermo-oxidation resistance, of pre-laminated and post-laminated EVA and POE sheets has been evaluated upon UVB exposure and prolonged thermal treatment, and the progress of degradation has been monitored by spectroscopy analysis. All obtained results agree that the lamination process has a beneficial effect on 3D-structuration of both EVA and POE sheets, and after lamination, the POE shows enhanced rigidity and appropriate ductility. Finally, although both EVA and POE can be considered good candidates as encapsulants for bifacial PV modules, it seems that the POE sheets show a better resistance to oxidation than the EVA sheets.
Study on Properties of High-Vanadium Full-Locked Cable with Alloy Coating with Defects
Authors: Fang Zhou , Fu Junjie , Wang Zhe , Chen Binlei , Shi Puan , Ma Jiahao
Published: March 2022

The Zn-5%Al-rare earth alloy coating has been used in High-Vanadium Full-Locked Cable (HVFLC) that is made in China. This application has shown excellent protective performance. However, during practical use, the coating may appear to be deteriorated in various degrees, which may further worsen the corrosion resistance and mechanical properties of the cable. This paper focuses on High-Vanadium Full-Locked Cable (HVFLC). Firstly, different types of prefabricated defect treatments have been applied into the wires of the cable, followed by corrosion paste accelerated corrosion test and mechanical tensile properties test. The aim of this act is to figure out the effects on the corrosion resistance and mechanical properties of the wire due to different types of defects in alloy plating. The results show that the corrosion resistance of the wires has been greatly affected because of the solder joint defects. Furthermore, the tensile strength of the wire indicates a drop of about 40%. This research makes up the gap in the related fields in China. In addition, basic data has been provided for the development of the HVFLC in China.
Proof-of-Concept of Continuous Transfection for Adeno-Associated Virus Production in Microcarrier-Based Culture
Authors: Ladd Brian , Bowes Kevin , Lundgren Mats , Gräslund Torbjörn , Chotteau Veronique
Published: March 2022

Adeno-associated virus vectors (AAV) are reported to have a great potential for gene therapy, however, a major bottleneck for this kind of therapy is the limitation of production capacity. Higher specific AAV vector yield is often reported for adherent cell systems compared to cells in suspension, and a microcarrier-based culture is well established for the culture of anchored cells on a larger scale. The purpose of the present study was to explore how microcarrier cultures could provide a solution for the production of AAV vectors based on the triple plasmid transfection of HEK293T cells in a stirred tank bioreactor. In the present study, cells were grown and expanded in suspension, offering the ease of this type of operation, and were then anchored on microcarriers in order to proceed with transfection of the plasmids for transient AAV vector production. This process was developed in view of a bioreactor application in a 200 mL stirred-tank vessel where shear stress aspects were studied. Furthermore, amenability to a continuous process was studied. The present investigation provided a proof-of-concept of a continuous process based on microcarriers in a stirred-tank bioreactor.
Theoretical Study of a Novel Power Cycle for Enhanced Geothermal Systems
Authors: Geng Changyou , Lu Xinli , Yu Hao , Zhang Wei , Zhang Jiaqi , Wang Jiansheng
Published: March 2022

As obtained geofluids from enhanced geothermal systems usually have lower temperatures and contain chemicals and impurities, a novel power cycle (NPC) with a unit capacity of several hundred kilowatts has been configured and developed in this study, with particular reference to the geofluid temperature (heat source) ranging from 110 °C to 170 °C. Using a suitable CO2-based mixture working fluid, a transcritical power cycle was developed. The novelty of the developed power cycle lies in the fact that an increasing-pressure endothermic process was realized in a few-hundred-meters-long downhole heat exchanger (DHE) by making use of gravitational potential energy, which increases the working fluid’s pressure and temperature at the turbine inlet and, hence, increases the cycle’s power output. The increasing-pressure endothermic process in the DHE has a better match with the temperature change of the heat source (geofluid), as does the exothermic process in the condenser with the temperature change of the sink (cooling water), which reduces the heat transfer irreversibility and improves the cycle efficiency. Power cycle performance has been analyzed in terms of the effects of mass fraction of the mixture working fluids, the working fluid’s flowrate and its DHE inlet pressure, geofluid flowrate, and the length of the DHE. Results show that, for a given geofluid’s temperature and mass flowrate, the cycle’s net power output is a strong function of the working-fluid’s flowrate, as well as of its DHE inlet pressure. Too high or too low of a DHE inlet pressure results in a lower power output. When geofluid temperature is 130 °C, the optimum DHE inlet pressure is found to be 11 MPa, corresponding to an optimum working-fluid flowrate of 6.5 kg/s. The longer the DHE, the greater the corresponding working-fluid flowrate and the higher the net power output. For geofluid temperature ranging from 110 °C to 170 °C, the developed NPC has a better thermodynamic performance than the conventional ORC. The advantage of using the developed NPC becomes obvious when geofluid temperature is low. The maximum net power output difference between the NPC and the ORC happens when the geofluid temperature is 130 °C and NPC’s working fluid mass fraction (R32/CO2) is 0.5/0.5.
From Hops to Craft Beers: Production Process, VOCs Profile Characterization, Total Polyphenol and Flavonoid Content Determination and Antioxidant Activity Evaluation
Authors: Iannone Matteo , Ovidi Elisa , Vitalini Sara , Laghezza Masci Valentina , Marianelli Andrea , Iriti Marcello , Tiezzi Antonio , Garzoli Stefania
Published: March 2022

In this work, selections of seven international hop varieties and three craft beers obtained from them were analyzed by SPME-GC/MS techniques with the aim to describe their volatile chemical profile. The brewing process was also reported. Furthermore, the hop extracts and beers were investigated to determine their flavonoid and polyphenol content and to evaluate their antioxidant power by DPPH and ABTS assays. The findings showed the presence of compounds belonging to different chemical classes such as monoterpenes, sesquiterpenes, alcohols, esters and fatty acids. In particular, sesquiterpenes were the main compounds with β-caryophyllene (from 1.7 to 16.2%) and humulene (from 10.8 to 43.9%) as the major components in all varieties of dried hop cones investigated. On the contrary, with the exception for the Pacific sample, monoterpenes were the class of compounds that were more abundant in the hop extracts and, among these, β-myrcene appeared to be the predominant constituent (from 31.8 to 71.4%). Regarding the craft beers obtained by adding these hop varieties, some differences in the qualitative and quantitative volatile composition have been found. All hop samples showed a high scavenging potential against both radicals. In the case of DPPH, the obtained IC50 values ranged from 0.027 to 0.047 mg/mL while they varied between 0.023 and 0.134 mg/mL by the ABTS assay. A positive correlation was found with polyphenol and flavonoid contents. Among beer samples, ACD was the richest one in polyphenols (292.0 mg GAE/100 mL beer) and flavonoids (5.8 mg QE/100 mL beer) and the most powerful against DPPH• and ABTS•+ radicals with IC50 values equal to 4.969 and 0.198 v/v%, respectively.
Hot-Wire Investigation of Turbulence Topology behind Blades at Different Shape Qualities
Authors: Yanovych Vitalii , Duda Daniel , Uruba Václav , Tomášková Tetjana
Published: March 2022

The scope of this paper is to perform a detailed experimental investigation of the shape error effect on the turbulence evolution behind NACA 64-618 airfoil. This airfoil is 3D-printed with predefined typical shape inaccuracies. A high-precision optical 3D scanner was used to assess the shape and surface quality of the manufactured models. The turbulent flow was studied using hot-wire anemometry. The developed force balance device was provided to measure the aerodynamic characteristics of the airfoil. Experimental studies were carried out for three angles of attack, +10∘, 0∘, −10∘, and different chord-based Reynolds numbers from 5.3×104 to 2.1×105. The obtained results show that the blunt trailing edge and rough surface decline the aerodynamic performance of the blades. In addition, the experimental results revealed a strong sensitivity of the Taylor microscale Reynolds number to the type of shape inaccuracy, especially at Re≈1.7×105. We also discuss the evolution of the Reynolds stress components, the degree of flow anisotropy, and the power spectrum distributions depending on the airfoil inaccuracies.
Modeling and Optimization of Heavy Metals Biosorption by Low-Cost Sorbents Using Response Surface Methodology
Authors: Fertu Daniela Ionela , Bulgariu Laura , Gavrilescu Maria
Published: March 2022

This paper exploits, through modeling and optimization, the experimental laboratory data on the biosorption of heavy metal ions Pb(II), Cd(II), and Zn(II) from aqueous media using soybean and soybean waste biomasses. The biosorption modeling was performed using the Response Surface Methodology, followed by optimization based on numerical methods. The aim of the modeling was to establish the most probable mathematical relationship between the dependent variables (the biosorption efficiency of the biosorbents when adsorbing metal ions, R(%), and the biosorption capacity of sorbents, q(mg/g)) and the process parameters (pH; sorbent dose, DS (g/L); initial metal ion concentration in solution, c0 (mg/L); contact time, tc (min); temperature, T (°C)), validated by methodologies specific to the multiple regression analysis. Afterward, sets of solutions were obtained through optimization that correlate various values of the process parameters to maximize the objective function. These solutions also confirmed the performance of soybean waste biomass in the removal of heavy metal ions from polluted aqueous effluents. The results were validated experimentally.
Factors That Affect Methane Yield Using Raw Olive Alperujo (Unhydrolyzed) as Substrate in BMP Assays
Authors: Ortega Valentina , Donoso-Bravo Andrés , Chamy-Maggy Rolando , Campos José Luis , Mosquera-Corral Anuska , Belmonte Marisol
Published: March 2022

The olive alperujo (OA) corresponds to the solid waste generated in the olive oil extraction process using the two-phase centrifugation method. OA is produced in large quantities (800 kg OA/ton olives processed) and is characterized by its high moisture content, organic matter, and low pH. In Chile, the olive oil industry is recent, and one of its main challenges is to be able to manage OA to reduce the impact caused by its disposal. In this sense, its valorization as biogas by means of anaerobic digestion is an economically attractive option. For this, it is previously necessary to evaluate the biomethane potential (BMP) of the raw OA using batch assays. This study was focused on evaluating the factors that most affect the methane yield (MY) when using OA as substrate in BMP tests. First, a sweep analysis (Plackett–Burman) was applied to determine those factors that, according to the literature, would have an influence on the BMP tests. Among the factors studied, the most significant were preincubation, OA concentration, and agitation level. Subsequently, a 23 factorial experimental design was applied to evaluate the effect of these factors on MY at different levels. Results show that the OA concentration was the most significant factor affecting MY.
Evaluation of the Performance of Two Australian Waste-Plastic-Modified Hot Mix Asphalts
Authors: Mashaan Nuha S. , Chegenizadeh Amin , Nikraz Hamid
Published: March 2022

The construction of hundreds of kilometres of roads around the world every year results in the consumption of large amounts of raw materials and the depletion of natural resources. In addition, technologically advanced countries such as Australia are currently facing a major issue regarding the waste materials produced daily by their citizens. The disposal of these waste materials is a critical issue faced by municipalities in modern cities. Currently, using waste materials in civil and construction engineering is of great interest to researchers and industry. This study investigates the impact of using waste polyethylene terephthalate to modify asphalt mixtures following Australian design guidelines and criteria. Different types of asphalt are used to investigate and determine the mechanical properties of modified asphalt mixtures. The Marshall stability, Marshall flow, Marshall quotient, and wheel-tracking tests were tested. The Marshall stability, Marshall flow, and MQ of the Marshall test results exhibited significant improvements when using PET in modified SMA and AC mixtures. It can be seen that the 8% PET produced a mixture with the highest stability of 19.78 kN. The lowest rut depth was about 2.08 mm for samples modified with 8% PET.
Buddhist Modernism in the Philippines: Emerging Localization of Humanistic Buddhism
Authors: Dy Aristotle Chan
Published: March 2022

Mahayana Buddhism is well known for being successfully implanted in various cultures. Chinese Buddhism, considered one of the three great religions of China along with Confucianism and Taoism, is a classic example. From China, Buddhism traveled further and, in the twentieth century, developed a particular way of engaging the world. Humanistic Buddhism, a particular form of engaged Buddhism that grew out of twentieth-century Chinese Buddhism, has been present in the Philippines since the 1990s and signaled a new phase in the growth of Buddhism in the country. In particular, the Philippine initiators of Foguangshan and Ciji did not limit themselves to the ethnic Chinese community from the outset, and both movements have achieved modest success in the last thirty years. By building on previous research with new inputs from key informants, this article explores the emerging localization of Foguangshan and Ciji in the predominantly Catholic Philippines, identifying the particular ways in which the two groups adapted their missions in the country.
An Analysis of Power Consumption of Fluid-Driven Robotic Arms Using Isotropy Index: A Proof-of-Concept Simulation-Based Study
Authors: Maddahi Yaser , Zareinia Kourosh
Published: March 2022

The manipulability of a robotic arm may be defined based on ease of motion in different directions or ease of applying force/torque. In this study, we use manipulability measures to investigate how the kinematics of robots can be employed to calculate the optimal power required to drive the actuation systems of their arms. We hypothesize that the isotropy measure is related to the power consumption of the robotic arm. In addition to theoretical aspects, we consider practical applications that can minimize power consumption in robotic systems. Since the method is simple to implement and has no assumption on the robot’s work environment or dependence on information on the main power supply, manipulability measures can be used as a tool to predict the power consumption of robotic manipulators.
Radar Signal Intrapulse Modulation Recognition Based on a Denoising-Guided Disentangled Network
Authors: Zhang Xiangli , Zhang Jiazhen , Luo Tianze , Huang Tianye , Tang Zuping , Chen Ying , Li Jinsheng , Luo Dapeng
Published: March 2022

Accurate recognition of radar modulation mode helps to better estimate radar echo parameters, thereby occupying an advantageous position in the radar electronic warfare (EW). However, under low signal-to-noise ratio environments, recent deep-learning-based radar signal recognition methods often perform poorly due to the unsuitable denoising preprocess. In this paper, a denoising-guided disentangled network based on an inception structure is proposed to simultaneously complete the denoising and recognition of radar signals in an end-to-end manner. The pure radar signal representation (PSR) is disentangled from the noise signal representation (NSR) through a feature disentangler and used to learn a radar signal modulation recognizer under low-SNR environments. Signal noise mutual information loss is proposed to enlarge the gap between the PSR and the NSR. Experimental results demonstrate that our method can obtain a recognition accuracy of 98.75% in the −8 dB SNR and 89.25% in the −10 dB environment of 12 modulation formats.
Study on the Classification and Change Detection Methods of Drylands in Arid and Semi-Arid Regions
Authors: Zhu Zijuan , Zhang Zengxiang , Zuo Lijun , Pan Tianshi , Zhao Xiaoli , Wang Xiao , Sun Feifei , Xu Jinyong , Liu Ziyuan
Published: March 2022

The aim of this study was to clarify the distribution of irrigated drylands in arid and semi-arid areas, where complex terrain, diverse crops and staggered cultivated lands exist. This paper studied the classification methods of irrigated drylands based on temperature, precipitation, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Landsat data in the one-harvest area of the northern Loess Plateau of China by using the Google Earth Engine (GEE) platform. An extraction method was proposed for irrigated drylands in arid and semi-arid regions of northwest China. In addition, the change types of irrigated and rainfed drylands in the two periods were classified, and a method was also put forward to directly classify the change types by using the image differences between the two periods combined with the classification results of each period. It was found that combining the ratio of NDVI and NDWI with the accumulated values of temperature and precipitation of the 30 days before imaging could effectively improve the classification accuracy. Moreover, directly classifying the gaps of remote sensing factors in the time dimension before combining spatial clustering information could yield a more accurate type of change, because the accumulation of errors in the change maps obtained from the overlay analysis of distribution maps of the two periods could be avoided. The accuracy of classification could be improved by introducing the dynamic information of time dimension into the classification of historical periods. This study complements the extraction method for this type of irrigated dryland, and the classification results can improve the accuracy of existing products in terms of spatial resolution, which can fill the shortage of detailed distribution data for irrigated and rainfed drylands in this region.
Development of Semantic Maps of Vegetation Cover from UAV Images to Support Planning and Management in Fine-Grained Fire-Prone Landscapes
Authors: Trenčanová Bianka , Proença Vânia , Bernardino Alexandre
Published: March 2022

In Mediterranean landscapes, the encroachment of pyrophytic shrubs is a driver of more frequent and larger wildfires. The high-resolution mapping of vegetation cover is essential for sustainable land planning and the management for wildfire prevention. Here, we propose methods to simplify and automate the segmentation of shrub cover in high-resolution RGB images acquired by UAVs. The main contribution is a systematic exploration of the best practices to train a convolutional neural network (CNN) with a segmentation network architecture (U-Net) to detect shrubs in heterogeneous landscapes. Several semantic segmentation models were trained and tested in partitions of the provided data with alternative methods of data augmentation, patch cropping, rescaling and hyperparameter tuning (the number of filters, dropout rate and batch size). The most effective practices were data augmentation, patch cropping and rescaling. The developed classification model achieved an average F1 score of 0.72 on three separate test datasets even though it was trained on a relatively small training dataset. This study demonstrates the ability of state-of-the-art CNNs to map fine-grained land cover patterns from RGB remote sensing data. Because model performance is affected by the quality of data and labeling, an optimal selection of pre-processing practices is a requisite to improve the results.
Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network
Authors: Yang Haibo , Du Yao , Zhao Hongling , Chen Fei
Published: March 2022

The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.
Continuous, High-Resolution Mapping of Coastal Seafloor Sediment Distribution
Authors: Innangi Sara , Innangi Michele , Di Febbraro Mirko , Di Martino Gabriella , Sacchi Marco , Tonielli Renato
Published: March 2022

Seafloor topography and grain size distribution are pivotal features in marine and coastal environments, able to influence benthic community structure and ecological processes at many spatial scales. Accordingly, there is a strong interest in multiple research disciplines to obtain seafloor geological and/or habitat maps. The aim of this study was to provide a novel, automatic and simple model to obtain high-resolution seafloor maps, using backscatter and bathymetric multibeam system data. For this purpose, we calibrated a linear regression model relating grain size distribution values, extracted from samples collected in a 16 km2 area near Bagnoli–Coroglio (southern Italy), against backscatter and depth-derived covariates. The linear model achieved excellent goodness-of-fit and predictive accuracy, yielding detailed, spatially explicit predictions of grain size. We also showed that a ground-truth sample size as large as 40% of that considered in this study was sufficient to calibrate analogous regression models in different areas. Regardless of some limitations (i.e., inability to predict rocky outcrops and/or seagrass meadows), our modeling approach proved to be a flexible tool whose main advantage is the rendering of a continuous map for sediment size, in lieu of categorical mapping approaches which usually report sharp boundaries or rely on a few sediment classes.
Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image—Enhancing UAV-Based Phenotyping
Authors: Zhao Jiangsan , Kumar Ajay , Banoth Balaji Naik , Marathi Balram , Rajalakshmi Pachamuthu , Rewald Boris , Ninomiya Seishi , Guo Wei
Published: March 2022

Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture.
A Cost-Effective Method for Reconstructing City-Building 3D Models from Sparse Lidar Point Clouds
Authors: Kulawiak Marek
Published: March 2022

The recent popularization of airborne lidar scanners has provided a steady source of point cloud datasets containing the altitudes of bare earth surface and vegetation features as well as man-made structures. In contrast to terrestrial lidar, which produces dense point clouds of small areas, airborne laser sensors usually deliver sparse datasets that cover large municipalities. The latter are very useful in constructing digital representations of cities; however, reconstructing 3D building shapes from a sparse point cloud is a time-consuming process because automatic shape reconstruction methods work best with dense point clouds and usually cannot be applied for this purpose. Moreover, existing methods dedicated to reconstructing simplified 3D buildings from sparse point clouds are optimized for detecting simple building shapes, and they exhibit problems when dealing with more complex structures such as towers, spires, and large ornamental features, which are commonly found e.g., in buildings from the renaissance era. In the above context, this paper proposes a novel method of reconstructing 3D building shapes from sparse point clouds. The proposed algorithm has been optimized to work with incomplete point cloud data in order to provide a cost-effective way of generating representative 3D city models. The algorithm has been tested on lidar point clouds representing buildings in the city of Gdansk, Poland.
Ground-Penetrating Radar and Photogrammetric Investigation on Prehistoric Tumuli at Parabita (Lecce, Italy) Performed with an Unconventional Use of the Position Markers
Authors: Persico Raffaele , Colica Emanuele , Zappatore Tiziana , Giardino Claudio , D’Amico Sebastiano
Published: March 2022

In this contribution, we propose ground-penetrating radar (GPR) investigation performed close and on some prehistoric tumuli, locally called “piccole specchie”, in the countryside around the town of Parabita (Lecce), within the Salento peninsula (southern Italy). In order to perform the GPR investigation on the tumuli, an unconventional method of data acquisition was exploited, involving, consequently, some non-conventional data processing steps. Photogrammetric survey was also performed, and 3D digital models of the prehistoric tumuli were created. The investigations have revealed some anomalies under two out of three investigated tumuli, which were interpreted as prehistoric tombs.
Satellite-Based Diagnosis and Numerical Verification of Ozone Formation Regimes over Nine Megacities in East Asia
Authors: Lee Hyo-Jung , Chang Lim-Seok , Jaffe Daniel A. , Bak Juseon , Liu Xiong , Abad Gonzalo González , Jo Hyun-Young , Jo Yu-Jin , Lee Jae-Bum , Yang Geum-Hee , Kim Jong-Min , Kim Cheol-Hee
Published: March 2022

Urban photochemical ozone (O3) formation regimes (NOx- and VOC-limited regimes) at nine megacities in East Asia were diagnosed based on near-surface O3 columns from 900 to 700 hPa, nitrogen dioxide (NO2), and formaldehyde (HCHO), which were inferred from measurements by ozone-monitoring instruments (OMI) for 2014–2018. The nine megacities included Beijing, Tianjin, Hebei, Shandong, Shanghai, Seoul, Busan, Tokyo, and Osaka. The space-borne HCHO–to–NO2 ratio (FNR) inferred from the OMI was applied to nine megacities and verified by a series of sensitivity tests of Weather Research and Forecasting model with Chemistry (WRF-Chem) simulations by halving the NOx and VOC emissions. The results showed that the satellite-based FNRs ranged from 1.20 to 2.62 and the regimes over the nine megacities were identified as almost NOx-saturated conditions, while the domain-averaged FNR in East Asia was >2. The results of WRF–Chem sensitivity modeling show that O3 increased when the NOx emissions reduced, whereas VOC emission reduction showed a significant decrease in O3, confirming the characteristics of VOC-limited conditions in all of the nine megacities. When both NOx and VOC emissions were reduced, O3 decreased in most cities, but increased in the three lowest-FNRs megacities, such as Shanghai, Seoul, and Tokyo, where weakened O3 titration caused by NOx reduction had a larger enough effect to offset O3 suppression induced by the decrease in VOCs. Our model results, therefore, indicated that the immediate VOC emission reduction is a key controlling factor to decrease megacity O3 in East Asia, and also suggested that both VOC and NOx reductions may not be of broad utility in O3 abatement in megacities and should be considered judiciously in highly NOx-saturated cities in East Asia.
HRV Monitoring Using Commercial Wearable Devices as a Health Indicator for Older Persons during the Pandemic
Authors: Rodrigues Eujessika , Lima Daniella , Barbosa Paulo , Gonzaga Karoline , Guerra Ricardo Oliveira , Pimentel Marcela , Barbosa Humberto , Maciel Álvaro
Published: March 2022

Remote monitoring platforms based on advanced health sensors have the potential to become important tools during the COVID-19 pandemic, supporting the reduction in risks for affected populations such as the elderly. Current commercially available wearable devices still have limitations to deal with heart rate variability (HRV), an important health indicator of human aging. This study analyzes the role of a remote monitoring system designed to support health services to older people during the complete course of the COVID-19 pandemic in Brazil, since its beginning in Brazil in March 2020 until November 2021, based on HRV. Using different levels of analysis and data, we validated HRV parameters by comparing them with reference sensors and tools in HRV measurements. We compared the results obtained for the cardiac modulation data in time domain using samples of 10 elderly people’s HRV data from Fitbit Inspire HR with the results provided by Kubios for the same population using a cardiac belt, with the data divided into train and test, where 75% of the data were used for training the models, with the remaining 25% as a test set for evaluating the final performance of the models. The results show that there is very little difference between the results obtained by the remote monitoring system compared with Kubios, indicating that the data obtained from these devices might provide accurate results in evaluating HRV in comparison with gold standard devices. We conclude that the application of the methods and techniques used and reported in this study are useful for the creation and validation of HRV indicators in time series obtained by means of wearable devices based on photoplethysmography sensors; therefore, they can be incorporated into remote monitoring processes as seen during the pandemic.
Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model
Authors: Atlam Hany F. , Azad Muhammad Ajmal , Fadhel Nawfal F.
Published: March 2022

Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.
A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal
Authors: Baygin Mehmet , Barua Prabal Datta , Dogan Sengul , Tuncer Turker , Key Sefa , Acharya U. Rajendra , Cheong Kang Hao
Published: March 2022

Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.