bluuebunny/crossref_metadata_2025 huggingface dataset:
Rotational Rheology of Wood Flour Composites Based on Recycled Polyethylene
Authors: Patti Antonella , Cicala Gianluca , Acierno Stefano
Published: July 2021

In this paper, we study the effect of the addition of wood flour as a filler in a recycled polyethylene (r-PE) in view of its potential applications in 3D printing. The composites, prepared by melt mixing, are characterized with torque measurements performed during the compounding, dynamic rotational rheology, and infrared spectroscopy. Data show that the introduction of wood results in increased viscosity and in sensible viscous heating during the compounding. The r-PE appear to be stable at temperatures up to 180 °C while at higher temperatures the material shows a rheological response characterized by time-increasing viscoelastic moduli that suggests a thermal degradation governed by crosslinking reactions. The compounds (with wood loading up to 50% in wt.) also shows thermal stability at temperatures up to 180 °C. The viscoelastic behavior and the infrared spectra of the r-PE matrix suggests the presence of branches in the macromolecular structure due to the process. Although the addition of wood particles determines increased viscoelastic moduli, a solid-like viscoelastic response is not shown even for the highest wood concentrations. This behavior, due to a poor compatibility and weak interfacial adhesion between the two phases, is however promising in view of common processing technologies as extrusion or injection molding.
Photodynamic Inactivation of Pseudomonas aeruginosa by PHEMA Films Loaded with Rose Bengal: Potentiation Effect of Potassium Iodide
Authors: López-Fernández Ana M. , Muñoz Resta Ignacio , de Llanos Rosa , Galindo Francisco
Published: July 2021

Four formulations have been used to produce different poly(2-hydroxyethyl methacrylate) (PHEMA) thin films, containing singlet oxygen photosensitizer Rose Bengal (RB). The polymers have been characterized employing Thermogravimetric Analysis (TGA), Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) and UV-vis Absorption Spectroscopy. When irradiated with white light (400–700 nm) films generated singlet oxygen (1O2), as demonstrated by the reactivity with 1O2 trap 9,10-dimethylanthracene (DMA). Material with the highest RB loading (polymer A4, 835 nmol RB/g polymer) was able to perform up to ten cycles of DMA oxygenation reactions at high conversion rates (ca. 90%). Polymer A4 was also able to produce the complete eradication of a Pseudomonas aeruginosa planktonic suspension of 8 log10 CFU/mL, when irradiated with white light (total dose 72 J/cm2). The antimicrobial photodynamic effect was remarkably enhanced by adding potassium iodide (100 mM). In such conditions the complete bacterial reduction occurred with a total light dose of 24 J/cm2. Triiodide anion (I3−) generation was confirmed by UV-vis absorption spectroscopy. This species was detected inside the PHEMA films after irradiation and at concentrations ca. 1 M. The generation of this species and its retention in the matrix imparts long-lasting bactericidal effects to the RB@PHEMA polymeric hydrogels. The polymers here described could find potential applications in the medical context, when optimized for their use in everyday objects, helping to prevent bacterial contagion by contact with surfaces.
Citrate-Coated Magnetic Polyethyleneimine Composites for Plasmid DNA Delivery into Glioblastoma
Authors: Leung Ken , Sham Kathy , Lai Josie , Wang Yi-Xiang , Wong Chi-Hin , Cheng Christopher
Published: July 2021

Several ternary composites that are based on branched polyethyleneimine (bPEI 25 kDa, polydispersity 2.5, 0.1 or 0.2 ng), citrate-coated ultrasmall superparamagnetic iron oxide nanoparticles (citrate-NPs, 8–10 nm, 0.1, 1.0, or 2.5 µg), and reporter circular plasmid DNA pEGFP-C1 or pRL-CMV (pDNA 0.5 µg) were studied for optimization of the best composite for transfection into glioblastoma U87MG or U138MG cells. The efficiency in terms of citrate-NP and plasmid DNA gene delivery with the ternary composites could be altered by tuning the bPEI/citrate-NP ratios in the polymer composites, which were characterized by Prussian blue staining, in vitro magnetic resonance imaging as well as green fluorescence protein and luciferase expression. Among the composites prepared, 0.2 ng bPEI/0.5 μg pDNA/1.0 µg citrate-NP ternary composite possessed the best cellular uptake efficiency. Composite comprising 0.1 ng bPEI/0.5 μg pDNA/0.1 μg citrate-NP gave the optimal efficiency for the cellular uptake of the two plasmid DNAs to the nucleus. The best working bPEI concentration range should not exceed 0.2 ng/well to achieve a relatively low cytotoxicity.
Application of Bacterial Cellulose in the Textile and Shoe Industry: Development of Biocomposites
Authors: Fernandes Marta , Souto António Pedro , Dourado Fernando , Gama Miguel
Published: July 2021

Several studies report the potential of bacterial cellulose (BC) in the fashion and leather industries. This work aimed at the development of BC-based composites containing emulsified acrylated epoxidized soybean oil (AESO) that are polymerized with the redox initiator system hydrogen peroxide (H2O2) and L-ascorbic acid and ferrous sulfate as a catalyst. BC was fermented under static culture. The polymerization of the emulsified organic droplets was tested before and after their incorporation into BC by exhaustion. The composites were then finished with an antimicrobial agent (benzalkonium chloride) and dyed. The obtained composites were characterized in terms of wettability, water vapor permeability (WVP), mechanical, thermal and antimicrobial properties. When AESO emulsion was polymerized prior to the exhaustion process, the obtained composites showed higher WVP, tensile strength and thermal stability. Meanwhile, post-exhaustion polymerized AESO conferred the composite higher hydrophobicity and elongation. The composites finished with the antimicrobial agent showed activity against S. aureus. Finally, intense colors were obtained more uniformly when they were incorporated simultaneously with the emulsified AESO with all the dyes tested.
Supercritical Assisted Production of Lutein-Loaded Liposomes and Modelling of Drug Release
Authors: Trucillo Paolo , Martino Mathieu , Reverchon Ernesto
Published: July 2021

In this work, a lipophilic ophthalmic drug, lutein, has been entrapped in liposomes, using a supercritical assisted process. Effects of pressure, temperature, and drug to lipid ratio variation were studied on mean diameters and lutein encapsulation efficiency. Liposomes with diameters between 153 ± 38 and 267 ± 56 nm were produced, and lutein encapsulation efficiencies between 86.5 ± 0.4% and 97.8 ± 1.2% were obtained. A Scanning Electron Microscope confirmed spherical shape and mean dimensions of vesicles. The variation of temperature for the production of liposomes showed a significant impact on lutein retention time in the double lipidic layer. Lutein drug release from liposomes produced at 35 °C ended in almost 4.5 days; whereas, liposomes produced at 40 °C showed a faster lutein release in 3 days; then, vesicles obtained at 45 °C released their lutein content in only 2 days. Drug release raw data were well-fitted using Weibull model (R2 up to 99%).
Numerical Investigation of Metal Foam Pore Density Effect on Sensible and Latent Heats Storage through an Enthalpy-Based REV-Scale Lattice Boltzmann Method
Authors: Mabrouk Riheb , Naji Hassane , Dhahri Hacen
Published: July 2021

In this work, an unsteady forced convection heat transfer in an open-ended channel incorporating a porous medium filled either with a phase change material (PCM; case 1) or with water (case 2) has been studied using a thermal lattice Boltzmann method (TLBM) at the representative elementary volume (REV) scale. The set of governing equations includes the dimensionless generalized Navier–Stokes equations and the two energy model transport equations based on local thermal non-equilibrium (LTNE). The enthalpy-based method is employed to cope with the phase change process. The pores per inch density (10≤PPI≤60) effects of the metal foam on the storage of sensible and latent heat were studied during charging/discharging processes at two Reynolds numbers (Re) of 200 and 400. The significant outcomes are discussed for the dynamic and thermal fields, the entropy generation rate (Ns), the LTNE intensity, and the energy and exergy efficiencies under the influence of Re. It can be stated that increasing the PPI improves the energy and exergy efficiencies of the latent heat model, reduces energy losses, and improves the stored energy quality. Likewise, at a moderate Re (=200), a low PPI (=10) would be suitable to reduce the system irreversibility during the charging period, while a high value (PPI = 60) might be advised for the discharging process. As becomes clear from the obtained findings, PPI and porosity are relevant factors. In conclusion, this paper further provides a first analysis of entropy generation during forced convection to improve the energy efficiency of various renewable energy systems.
Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach
Authors: Musa Bashir , Yimen Nasser , Abba Sani Isah , Adun Humphrey Hugh , Dagbasi Mustafa
Published: July 2021

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.
Liquid-Liquid Equilibrium of Deep Eutectic Solvent-Aromatic-Aliphatic Ternary Systems: Experimental Study with COSMO Model Predictions
Authors: McGaughy Kyle , Reza M. Toufiq
Published: July 2021

Common solvents used for aromatic extraction from aliphatics typically degrade into toxic compounds, while green alternatives perform poorly compared to the state-of-the-art solvents. Deep eutectic solvents (DES) are a novel solvent type made of hydrogen bond donors (HBD) and hydrogen bond acceptors (HBA). DES have been applied in various applications, including advanced separations. In this study, DES were studied experimentally and using the Conductor-like Screening Model (COSMO) to separate benzene from cyclohexane as model compounds for an aromatic:aliphatic system. Both equilibrium and kinetic studies were performed to determine the liquid liquid equilibrium (LLE) and mass transfer rate for the DES-based separation. Selected HBAs including tetrabutylammonium bromide (N4444Br), tetrahexylammonium bromide (N6666Br), choline chloride (ChCl), and methyltriphenylphosphonium bromide (METPB) were paired with HBDs including ethylene glycol (EG) and glycerol (Gly). COSMO was used, with adjustments to reflect DES specific interactions, to predict the liquid-liquid equilibrium (LLE). COSMO results showed that ChCl and N6666Br-based DES extracted too little benzene or too much cyclohexane, respectively, to be considered for experimental evaluation. Overall, the COSMO model predictions for LLE of EG-based DES were very accurate, with root-mean-square deviations (RMSD) below 1% for both N4444Br:EG and METPB:EG. The glycerol systems were less accurately modeled, with RMSD’s of 4% for N4444Br:Gly and 6% for METPB:Gly. The lower accuracy of glycerol system predictions fmay be due to limitations in COSMO for handling glycerol’s influence on polarizability in the DES that is not seen in EG-based DES. Mass transfer kinetics were determined experimentally for DES and the results were fit to a first order kinetics model. METPB:Gly had the highest mass transfer coefficient at 0.180 min−1, followed by N4444Br:EG at 0.143 min−1. N4444Br:Gly and METPB:EG had the lowest mass transfer coefficients at 0.096 min−1 and 0.084 min−1, respectively. It was found that mass transfer rate was not directly related to maximum benzene solubility, as N4444Br:EG and METPB:Gly had the highest and lowest benzene removal, respectively, but had similar mass transfer coefficients.
Highly Specific L-Type Amino Acid Transporter 1 Inhibition by JPH203 as a Potential Pan-Cancer Treatment
Authors: Achmad Arifudin , Lestari Shinta , Holik Holis Abdul , Rahayu Driyanti , Bashari Muhammad Hasan , Faried Ahmad , Kartamihardja Achmad Hussein Sundawa
Published: July 2021

Accelerated cancer cell growth requires a massive intake of amino acids. Overexpression of L-type (large) amino acid transporter 1 (LAT1) on the cancer cell membrane facilitates such a demand, which is limited in normal organs. Therefore, LAT1 overexpression is ideal as a molecular cancer therapeutic target. JPH203, a LAT1-selective non-transportable blocker, had demonstrated LAT1 inhibition in <10 µM IC50 values and effectively suppressed cancer cell growth in studies involving several types of cancer cell lines and tumor xenograft models. A limited phase I clinical trial was performed on five different solid tumors and showed that JPH203 is well-tolerated and has a promising activity for the treatment of bile duct cancer. This review details the development and prospect of JPH203 as a LAT1-targeting cancer therapy.
Information about Human Evolution: An Analysis of News Published in Communication Media in Spanish between 2015 and 2017
Authors: Garibi Jon Ander , Antón Alvaro , Villarroel José Domingo
Published: July 2021

The present study examines a sample of 220 pieces of news related to human evolution, written in Spanish and published over a period of two years, both in digital and print media. The aim of this study is to assess the rigor and coherence of the information in the news in our sample with scientific knowledge on the theory of evolution. To this end, errors and the incorrect use of concepts related to biological evolution are identified, classified according to criteria resulting from the review of previous studies, and finally, the frequency of errors identified in news published in print media is compared with that identified in digital media. The results presented allow us to highlight the significantly high frequency of errors in the news analyzed and the most frequent error categories. Results are discussed within the frame of the important role that scientific journalism plays in the processes of knowledge dissemination, in this case, related to human evolution.
Stories Told by Publications about the Relationship between Industry 4.0 and Lean: Systematic Literature Review and Future Research Agenda
Authors: Salvadorinho Juliana , Teixeira Leonor
Published: July 2021

Industry 4.0 and its application in the business has been the focus of attention by the academy, for its ability to establish principles of flexibility and connectivity along the shop floor. Meantime, because of a lean wave in the 1990’s, most of the western industry adopted principles, techniques and tools of lean production, whose results were quickly captured, guaranteed its adoption worldwide. Thus, with a view to turning traditional manufacturing companies into smart companies, it is essential to preserve the existing system and find ways for the two concepts (Lean and I4.0) to come together. This study was conducted based on a systematic literature review, using the Scopus database. The PRISMA process was the method used to select the articles. Finally, to analyze and discuss the results, the bibliometric analysis of the articles and content analysis were adopted. The results point to a greater impact of I4.0 technologies on lean, since it was perceived that I4.0 technologies give to lean tools a more dynamic way of working, accelerating information sharing processes and improving production manager’s and operator’s decision making. In the perspective of lean’s contribution to I4.0; however, there is little practical and theoretical application, thus the actual contribution is still somewhat blurred.
Evangelicalism before the Fall: The Christian Herald and Signs of our Times
Authors: Robins Roger Glenn
Published: July 2021

“Evangelicalism Before the Fall” reveals the surprising and largely forgotten world of the premillennialist wing of late Victorian Evangelicalism through a close reading of its leading paper, The Christian Herald and Signs of Our Times. Organized around five thematic soundings (“worldly affairs”; “great questions”; “self and other”; “meeting modernity”; and “Evangelical culture”), the paper shows that premillennialism comported easily with socially elite status, liberal instincts, and irenic habits of mind not commonly associated with those holding similar beliefs in the decades after. Although the primary goal of the article is to recover an overlooked moment in Evangelical history, it secondarily contributes to a historiographical debate in the field of Fundamentalism studies, where revisionists have challenged the “fall” narrative of an earlier cohort of scholars, such as George Marsden and Joel Carpenter, who documented a decline in social standing and influence for the movement relative to the late nineteenth century. The article lends support to the fall narrative, properly understood as a change in social and cultural status.
Robot-Assisted Glovebox Teleoperation for Nuclear Industry
Authors: Tokatli Ozan , Das Pragna , Nath Radhika , Pangione Luigi , Altobelli Alessandro , Burroughes Guy , Jonasson Emil T. , Turner Matthew F. , Skilton Robert
Published: July 2021

The nuclear industry has some of the most extreme environments in the world, with radiation levels and extremely harsh conditions restraining human access to many facilities. One method for enabling minimal human exposure to hazards under these conditions is through the use of gloveboxes that are sealed volumes with controlled access for performing handling. While gloveboxes allow operators to perform complex handling tasks, they put operators at considerable risk from breaking the confinement and, historically, serious examples including punctured gloves leading to lifetime doses have occurred. To date, robotic systems have had relatively little impact on the industry, even though it is clear that they offer major opportunities for improving productivity and significantly reducing risks to human health. This work presents the challenges of robotic and AI solutions for nuclear gloveboxes, and introduces a step forward for bringing cutting-edge technology to gloveboxes. The problem statement and challenges are highlighted and then an integrated demonstrator is proposed for robotic handling in nuclear gloveboxes for nuclear material handling. The proposed approach spans from tele-manipulation to shared autonomy, computer vision solutions for robotic manipulation to machine learning solutions for condition monitoring.
A GPR-Based Pavement Density Profiler: Operating Principles and Applications
Authors: Diamanti Nectaria , Annan A. Peter , Jackson Steven R. , Klazinga Dylan
Published: July 2021

Density is one of the most important parameters in the construction of asphalt mixtures and pavement engineering. When a mixture is properly designed and compacted, it will contain enough air voids to prevent plastic deformation but will have low enough air void content to prevent water ingress and moisture damage. By mapping asphalt pavement density, areas with air void content outside of the acceptable range can be identified to predict its future life and performance. We describe a new instrument, the pavement density profiler (PDP) that has evolved from many years of making measurements of asphalt pavement properties. This instrument measures the electromagnetic (EM) wave impedance to infer the asphalt pavement density (or air void content) locally and over profiles.
A Rotation-Invariant Optical and SAR Image Registration Algorithm Based on Deep and Gaussian Features
Authors: Li Zeyi , Zhang Haitao , Huang Yihang
Published: July 2021

Traditional feature matching methods of optical and synthetic aperture radar (SAR) used gradient are sensitive to non-linear radiation distortions (NRD) and the rotation between two images. To address this problem, this study presents a novel approach to solving the rigid body rotation problem by a two-step process. The first step proposes a deep learning neural network named RotNET to predict the rotation relationship between two images. The second step uses a local feature descriptor based on the Gaussian pyramid named Gaussian pyramid features of oriented gradients (GPOG) to match two images. The RotNET uses a neural network to analyze the gradient histogram of the two images to derive the rotation relationship between optical and SAR images. Subsequently, GPOG is depicted a keypoint by using the histogram of Gaussian pyramid to make one-cell block structure which is simpler and more stable than HOG structure-based descriptors. Finally, this paper designs experiments to prove that the gradient histogram of the optical and SAR images can reflect the rotation relationship and the RotNET can correctly predict them. The similarity map test and the image registration results obtained on experiments show that GPOG descriptor is robust to SAR speckle noise and NRD.
Pre-Orientale Southwest Peak-Ring Basin: Gravity Structure, Geologic Characteristics, and Influence on Orientale Basin Ring Formation and Ejecta Emplacement
Authors: Ji Jinzhu , Head James W. , Liu Jianzhong
Published: July 2021

The Orientale impact basin is the youngest and most well-preserved of the lunar multi-ring basins. The generally well-preserved ring structures and basin facies are distinctly anomalous in the southwestern quadrant; the outer Cordillera ring extends significantly outward, the Outer and Inner Rook mountain rings are more poorly developed and show anomalous characteristics, and the Montes Rook Formation varies widely from its characteristics elsewhere in the basin interior. Based on the gravity, image, and topography data, we confirmed that the southwest region of the Orientale basin represents the location of a pre-existing ~320 km rim–crest diameter peak–ring basin centered at 108.8°W, 28.4°S, and characterized by an ~170 km peak–ring diameter. We model the structure and morphology of this large pre-Orientale peak–ring basin (about one-third the diameter of Orientale) and show that its presence and negative relief had a distinctive influence on the development of the basin rings (disrupting the otherwise generally circular continuity and causing radial excursions in their locations) and the emplacement of ejecta (causing filling of the low region represented by the peak–ring basin, creating anomalous surface textures, and resulting in late stage ejecta movement in response to the pre-existing peak–ring basin topography. The location and preservation of the peak–ring basin Bouguer anomaly strongly suggest that the rim crest of the Orientale basin excavation cavity lies at or within the Outer Rook Mountain ring.
Impact of Attitude Model, Phase Wind-Up and Phase Center Variation on Precise Orbit and Clock Offset Determination of GRACE-FO and CentiSpace-1
Authors: Yuan Junjun , Zhou Shanshi , Hu Xiaogong , Yang Long , Cao Jianfeng , Li Kai , Liao Min
Published: July 2021

Currently, low Earth orbit (LEO) satellites are attracting great attention in the navigation enhancement field because of their stronger navigation signal and faster elevation variation than medium Earth orbit (MEO) satellites. To meet the need for real-time and precise positioning, navigation and timing (PNT) services, the first and most difficult task is correcting errors in the process of precise LEO orbit and clock offset determination as much as possible. Launched in 29 September 2018, the CentiSpace-1 (CS01) satellite is the first experimental satellite of LEO-based navigation enhancement system constellations developed by Beijing Future Navigation Technology Co. Ltd. To analyze the impact of the attitude model, carrier phase wind-up (PWU) and phase center variation (PCV) on precise LEO orbit and clock offset in an LEO-based navigation system that needs extremely high precision, we not only select the CS01 satellite as a testing spacecraft, but also the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO). First, the dual-frequency global positioning system (GPS) data are collected and the data quality is assessed by analyzing the performance of tracking GPS satellites, multipath errors and signal to noise ratio (SNR) variation. The analysis results show that the data quality of GRACE-FO is slightly better than CS01. With residual analysis and overlapping comparison, a further orbit quality improvement is possible when we further correct the errors of the attitude model, PWU and PCV in this paper. The final three-dimensional (3D) root mean square (RMS) of the overlapping orbit for GRACE-FO and CS01 is 2.08 cm and 1.72 cm, respectively. Meanwhile, errors of the attitude model, PWU and PCV can be absorbed partly in the clock offset and these errors can generate one nonnegligible effect, which can reach 0.02~0.05 ns. The experiment results indicate that processing the errors of the attitude model, PWU and PCV carefully can improve the consistency of precise LEO orbit and clock offset and raise the performance of an LEO-based navigation enhancement system.
Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia
Authors: Kalantar Bahareh , Ueda Naonori , Saeidi Vahideh , Janizadeh Saeid , Shabani Fariborz , Ahmadi Kourosh , Shabani Farzin
Published: July 2021

Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in Australia (i.e., topographic, water-related, geological, and land use factors) were acquired for further processing and modeling. In this study, artificial neural networks (ANN), deep learning neural networks (DLNN), and optimized DLNN using particle swarm optimization (PSO) were exploited to predict and estimate the susceptible areas to the future floods. The significance of the conditioning factors analysis for the region highlighted that altitude, distance from river, sediment transport index (STI), and slope played the most important roles, whereas stream power index (SPI) did not contribute to the hazardous situation. The performance of the models was evaluated against the statistical tests such as sensitivity, specificity, the area under curve (AUC), and true skill statistic (TSS). DLNN and PSO-DLNN models obtained the highest values of sensitivity (0.99) for the training stage to compare with ANN. Moreover, the validations of specificity and TSS for PSO-DLNN recorded the highest values of 0.98 and 0.90, respectively, compared with those obtained by ANN and DLNN. The best accuracies by AUC were evaluated in PSO-DLNN (0.99 in training and 0.98 in testing datasets), followed by DLNN and ANN. Therefore, the optimized PSO-DLNN proved its robustness to compare with other methods.
Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle
Authors: Gautam Deepak , Ostendorf Bertram , Pagay Vinay
Published: July 2021

Crop water status and irrigation requirements are of great importance to the horticultural industry due to changing climatic conditions leading to high evaporative demands, drought and water scarcity in semi-arid and arid regions worldwide. Irrigation scheduling strategies based on evapotranspiration (ET), such as regulated deficit irrigation, requires the estimation of seasonal crop coefficients (kc). The ET-driven irrigation decisions for grapevines rely on the sampling of several kc values from each irrigation zone. Here, we present an unmanned aerial vehicle (UAV)-based technique to estimate kc at the single vine level in order to capture the spatial variability of water requirements in a commercial vineyard located in South Australia. A UAV carrying a multispectral sensor is used to extract the spectral, as well as the structural, information of Cabernet Sauvignon grapevines. The spectral and structural information, acquired at the various phenological stages of the vine through two seasons, is used to model kc using univariate (simple linear), multivariate (generalised linear and additive) and machine learning (convolution neural network and random forest) model frameworks. The structural information (e.g., canopy top view area) had the strongest correlation with kc throughout the season (p ≤ 0.001; Pearson R = 0.56), while the spectral indices (e.g., normalised indices) turned less-sensitive post véraison—the onset of ripening in grapes. Combining structural and spectral information improved the model’s performance. Among the investigated predictive models, the random forest predicted kc with the highest accuracy (R2: 0.675, root mean square error: 0.062, and mean absolute error: 0.047). This UAV-based approach improves the precision of irrigation by capturing the spatial variability of kc within a vineyard. Combined with an energy balance model, the water needs of a vineyard can be computed on a weekly or sub-weekly basis for precision irrigation. The UAV-based characterisation of kc can further enhance the water management and irrigation zoning by matching the infrastructure with the spatial variability of the irrigation demand.
Optimization in VHTS Satellite System Design with Irregular Beam Coverage for Non-Uniform Traffic Distribution
Authors: Ortiz-Gomez Flor G. , Salas-Natera Miguel A. , Martínez Ramón , Landeros-Ayala Salvador
Published: July 2021

Very High Throughput Satellites (VHTS) have a pivotal role in complementing terrestrial networks to increase traffic demand. VHTS systems currently assume a uniform distribution of traffic in the service area, but in a real system, traffic demands are not uniform and are dynamic. A possible solution is to use flexible payloads, but the cost of the design increases considerably. On the other hand, a fixed payload that uses irregular beam coverage depending on traffic demand allows maintaining the cost of a fixed payload while minimizing the error between offered and required capacity. This paper presents a proposal for optimizing irregular beams coverage and beam pattern, minimizing the costs per Gbps in orbit, the Normalized Coverage Error, and Offered Capacity Error per beam. We present the analysis and performance for the case study and compare it with a previous algorithm for a uniform coverage area.
Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques
Authors: Pedro Dário , Matos-Carvalho João P. , Fonseca José M. , Mora André
Published: July 2021

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.
Enhanced Oceanic Environmental Responses and Feedbacks to Super Typhoon Nida (2009) during the Sudden-Turning Stage
Authors: Li Jiagen , Yang Yuanjian , Wang Guihua , Cheng Hao , Sun Liang
Published: July 2021

The ocean surface and subsurface biophysical responses and their feedbacks to super typhoon Nida were comprehensively investigated based on a substantial dataset of multiple-satellite observations, Argo profiles, and reanalysis data. Nida experienced two Category 5 stages: a rapid intensification stage that was fast moving along a straight-line track, and a rapid weakening stage that was slowly moving along a sharp-left sudden-turning track. During the straight-line stage, Nida caused an average sea surface temperature (SST) cooling of 1.44 °C and a chlorophyll-a (chl-a) concentration increase of 0.03 mg m−3. During the sudden-turning stage, cyclonic sudden-turning induced a strong cold cyclonic eddy (SSHA < −60 cm) by strong upwelling, which caused the maximum SST cooling of 6.68 °C and a long-lasting chl-a bloom of 0.6 mg m−3 on the left-hand side of the track, resulting in substantial impacts on the ocean ecological environment. Furthermore, the enhanced ocean cold wake and the longer air–sea interaction in turn decreased the average inner-core SST of 4 °C and the corresponding enthalpy flux of 780 W m−2, which induced a notable negative feedback to the typhoon intensity by weakening it from Category 5 to Category 2. The left bias response and notable negative feedback are special due to sharp-left sudden-turning of typhoon. Comparing with the previously found slow translation speed (~4 m s−1) of significant ocean response, the negative feedback requires even more restriction of translation speed (<2 m s−1) and sharp sudden-turning could effectively relax restrictions by making equivalent translation speed lower and air-sea interaction time longer. Our findings point out that there are some unique features in ocean–typhoon interactions under sudden-turning and/or lingering tracks comparing with ordinary tracks.
Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery
Authors: Imran Hafiz Ali , Gianelle Damiano , Scotton Michele , Rocchini Duccio , Dalponte Michele , Macolino Stefano , Sakowska Karolina , Pornaro Cristina , Vescovo Loris
Published: July 2021

Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity.
Estimating the Key Parameter of a Tropical Cyclone Wind Field Model over the Northwest Pacific Ocean: A Comparison between Neural Networks and Statistical Models
Authors: Sun Ziyao , Zhang Biao , Tang Jie
Published: July 2021

Estimation of maximum wind speed associated with tropical cyclones (TCs) is crucial to evaluate potential wind destruction. The Holland B parameter is the key parameter of TC parametric wind field models. It plays an essential role in describing the radial distribution characteristics of a TC wind field and has been widely used in TC disaster risk evaluation. In this study, a backpropagation neural network (BPNN) is developed to estimate the Holland B parameter (Bs) in TC surface wind field model. The inputs of the BPNN include different combinations of TC minimum center pressure difference (Δp), latitude, radius of maximum wind speed, translation speed and intensity change rate from the best-track data of the Joint Typhoon Warning Center (JTWC). We find that the BPNN exhibits the best performance when only inputting TC central pressure difference. The Bs estimated from BPNN are compared with those calculated from previous statistical models. Results indicate that the proposed BPNN can describe well the nonlinear relation between Bs and Δp. It is also found that the combination of BPNN and Holland’s wind pressure model can significantly improve the maximum wind speed underestimation and overestimation of the two existing wind pressure models (AH77 and KZ07) for super typhoons.
Estimation of Hail Damage Using Crop Models and Remote Sensing
Authors: Gobbo Stefano , Ghiraldini Alessandro , Dramis Andrea , Dal Ferro Nicola , Morari Francesco
Published: July 2021

Insurance agents often provide crop hail damage estimates based on their personal experience and field samples, which are not always representative of the investigated field’s spatial variability. For these reasons, farmers and the insurance market ask for a reliable, objective, and less labor-intensive method to determine crop hail losses. Integrating remote sensing and crop modeling provides a unique opportunity for the crop insurance market for a reliable, objective, and less labor-intensive method to estimate hail damage. To this end, a study was conducted on eight distinct maize fields for a total of 90 hectares. Five fields were damaged by the hailstorm that occurred on 13 July 2019 and three were not damaged. Soil and plant samples were collected to characterize the experimental areas. The Surface Energy Balance Algorithm for Land (SEBAL) was deployed to determine the total aboveground biomass and obtainable yield at harvest, using Landsat 7 and 8 satellite images. Modeled hail damages (HDDSSAT1, coupling SEBAL estimates of obtainable yield and DSSAT-based potential yield; HDDSSAT2, coupling yield map at harvest and the Decision Support System for Agrotechnology Transfer (DSSAT)-based potential yield) were calculated and compared to the estimates of the insurance company (HDinsurance). SEBAL-based biomass and yield estimates agreed with in-season measurements (−4% and +0.5%, respectively). While some under and overestimations were observed, HDinsurance and HDDSSAT1 averaged similar values (−4.9% and +3.4%) compared to the reference approach (HDDSSAT2).
Large-Scale Particle Image Velocimetry to Measure Streamflow from Videos Recorded from Unmanned Aerial Vehicle and Fixed Imaging System
Authors: Liu Wen-Cheng , Lu Chien-Hsing , Huang Wei-Che
Published: July 2021

The accuracy of river velocity measurements plays an important role in the effective management of water resources. Various methods have been developed to measure river velocity. Currently, image-based techniques provide a promising approach to avoid physical contact with targeted water bodies by researchers. In this study, measured surface velocities collected under low flow and high flow conditions in the Houlong River, Taiwan, using large-scale particle image velocimetry (LSPIV) captured by an unmanned aerial vehicle (UAV) and a terrestrial fixed station were analyzed and compared. Under low flow conditions, the mean absolute errors of the measured surface velocities using LSPIV from a UAV with shooting heights of 9, 12, and 15 m fell within 0.055 ± 0.015 m/s, which was lower than that obtained using LSPIV on video recorded from a terrestrial fixed station (i.e., 0.34 m/s). The mean absolute errors obtained using LSPIV derived from UAV aerial photography at a flight height of 12 m without seeding particles and with different seeding particle densities were slightly different, and fell within the range of 0.095 ± 0.025 m/s. Under high flow conditions, the mean absolute errors associated with using LSPIV derived from terrestrial fixed photography and LSPIV derived from a UAV with flight heights of 32, 62, and 112 m were 0.46 m/s and 0.49 m/s, 0.27 m, and 0.97 m/s, respectively. A UAV flight height of 62 m yielded the best measured surface velocity result. Moreover, we also demonstrated that the optimal appropriate interrogation area and image acquisition time interval using LSPIV with a UAV were 16 × 16 pixels and 1/8 s, respectively. These two parameters should be carefully adopted to accurately measure the surface velocity of rivers.
Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection
Authors: De Cock Cedric , Joseph Wout , Martens Luc , Trogh Jens , Plets David
Published: July 2021

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.
FBG-Based Temperature Sensors for Liquid Identification and Liquid Level Estimation via Random Forest
Authors: Pereira Katiuski , Coimbra Wagner , Lazaro Renan , Frizera-Neto Anselmo , Marques Carlos , Leal-Junior Arnaldo Gomes
Published: July 2021

This paper proposed a liquid level measurement and classification system based on a fiber Bragg grating (FBG) temperature sensor array. For the oil classification, the fluids were dichotomized into oil and nonoil, i.e., water and emulsion. Due to the low variability of the classes, the random forest (RF) algorithm was chosen for the classification. Three different fluids, namely water, mineral oil, and silicone oil (Kryo 51), were identified by three FBGs located at 21.5 cm, 10.5 cm, and 3 cm from the bottom. The fluids were heated by a Peltier device placed at the bottom of the beaker and maintained at a temperature of 318.15 K during the entire experiment. The fluid identification by the RF algorithm achieved an accuracy of 100%. An average root mean squared error (RMSE) of 0.2603 cm, with a maximum RMSE lower than 0.4 cm, was obtained in the fluid level measurement also using the RF algorithm. Thus, the proposed method is a feasible tool for fluid identification and level estimation under temperature variation conditions and provides important benefits in practical applications due to its easy assembly and straightforward operation.