bluuebunny/crossref_metadata_2025 huggingface dataset:
Salt Stress Differentially Affects the Primary and Secondary Metabolism of Peppers (Capsicum annuum L.) According to the Genotype, Fruit Part, and Salinity Level
Authors: Zamljen Tilen , Medic Aljaz , Hudina Metka , Veberic Robert , Slatnar Ana
Published: March 2022

A total of four Capsicum annuum L. genotypes (‘Caro F1’, ‘Berenyi F1’, ‘Somborka’ and ‘Novosadka’) were exposed to two intensities of salt stress. We observed a significant decrease in the sugar content in all salt stressed treatments, except for the sucrose content of the pericarp of the ‘Caro F1’ cultivar. Salt stress had a largely negative effect on the total and individual organic acid content, although the effect differed among cultivars. Using high performance liquid chromatography coupled with a mass spectrometer, most phenolics were identified in the pericarp (18), followed by the placenta (7) and seeds (8). Treatment with 40 mM NaCl caused the highest increase in individual phenols, followed by treatment with 20 mM NaCl. The cultivar ‘Berenyi F1’ was less affected by salt stress treatment than the other three cultivars in terms of content of individual and total phenols. Salt stress increased the content of capsaicinoids in all the cultivars. The pericarp of the cultivar ‘Novosadka’ showed 17.5 and 50 times higher total capsaicinoid content than the control in the 20 mM and 40 mM NaCl, respectively. With the results of several metabolite groups, we confirmed that the reaction and metabolic content to salt stress within the genus Capsicum is genotype-, fruit part-, and salinity level-dependent.
Biodegradable and Biocompatible Thermoplastic Poly(Ester-Urethane)s Based on Poly(ε-Caprolactone) and Novel 1,3-Propanediol Bis(4-Isocyanatobenzoate) Diisocyanate: Synthesis and Characterization
Authors: Rubio Hernández-Sampelayo Alejandra , Navarro Rodrigo , González-García Dulce María , García-Fernández Luis , Ramírez-Jiménez Rosa Ana , Aguilar María Rosa , Marcos-Fernández Ángel
Published: March 2022

A series of non-toxic biodegradable and biocompatible polyurethanes bearing p-aminobenzoate moieties are presented. The introduction of this attractive motif was carried out by the synthesis of a novel isocyanate. These biodegradable polymers were chemically and physically characterized by several techniques and methods including bioassay and water uptake measurements. The molecular weight of the soft segment (poly-ε-caprolactone, PCL) and hard segment crystallinity dictated the mechanical behavior and water uptake. The behavior of short PCL-based polyurethanes was elastomeric, whilst increasing the molecular weight of the soft segment led to plastic polyurethanes. Water uptake was hindered for long PCL due to the crystallization of the soft segment within the polyurethane matrix. Furthermore, two different types of chain extender, hydrolyzable and non-hydrolyzable, were also evaluated: polyurethanes based on hydrolyzable chain extenders reached higher molecular weights, thus leading to a better performance than their unhydrolyzable counterparts. The good cell adhesion and cytotoxicity results demonstrated the cell viability of human osteoblasts on the surfaces of these non-toxic biodegradable polyurethanes.
Development and Mechanistic Studies of Ternary Nanocomposites for Hydrogen Production from Water Splitting to Yield Sustainable/Green Energy and Environmental Remediation
Authors: Jilani Asim , Hussain Syed Zajif , Melaibari Ammar A. , Abu-Hamdeh Nidal H.
Published: March 2022

Photocatalysts lead vitally to water purifications and decarbonise environment each by wastewater treatment and hydrogen (H2) production as a renewable energy source from water-photolysis. This work deals with the photocatalytic degradation of ciprofloxacin (CIP) and H2 production by novel silver-nanoparticle (AgNPs) based ternary-nanocomposites of thiolated reduce-graphene oxide graphitic carbon nitride (AgNPs-S-rGO2%@g-C3N4) material. Herein, the optimised balanced ratio of thiolated reduce-graphene oxide in prepared ternary-nanocomposites played matchlessly to enhance activity by increasing the charge carriers’ movements via slowing down charge-recombination ratios. Reduced graphene oxide (rGO), >2 wt.% or <2 wt.%, rendered H2 production by light-shielding effect. As a result, CIP degradation was enhanced to 95.90% by AgNPs-S-rGO2%@g-C3N4 under the optimised pH(6) and catalyst dosage(25 mg/L) irradiating beneath visible-light (450 nm, 150 watts) for 70 min. The chemical and morphological analysis of AgNPs-S-rGO2%@g-C3N4 surface also supported the possible role of thiolation for this enhancement, assisted by surface plasmon resonance of AgNPs having size < 10 nm. Therefore, AgNPs-S-rGO2%@g-C3N4 has 3772.5 μmolg−1 h−1 H2 production, which is 6.43-fold higher than g-C3N4 having cyclic stability of 96% even after four consecutive cycles. The proposed mechanism for AgNPs-S-rGO2%@g-C3N4 revealed that the photo-excited electrons in the conduction-band of g-C3N4 react with the adhered water moieties to generate H2.
Effective, Environmentally Friendly PVC Plasticizers Based on Succinic Acid
Authors: Ledniowska Kerstin , Nosal-Kovalenko Hanna , Janik Weronika , Krasuska Agata , Stańczyk Dorota , Sabura Ewa , Bartoszewicz Maria , Rybak Aleksandra
Published: March 2022

The plasticizers used in this study were synthesized from renewable raw materials using succinic acid, oleic acid, and propylene glycol. Four environmentally friendly plasticizer samples were obtained; their chemical structures and compositions were confirmed by gas chromatography (GC) and infrared spectroscopy (FT–IR) analyses, and their physicochemical properties and thermal stability (TGA analysis) were investigated. The obtained ester mixtures were used as poly(vinyl chloride) (PVC) plasticizers and their plasticization efficiency was determined in comparison to traditional, commercially available phthalate plasticizers, such as DEHP (di(2-ethylhexyl phthalate) and DINP (diisononyl phthalate). Mechanical properties and migration resistance were determined for soft PVC with the use of three concentrations of plasticizers (40 PHR, 50 PHR, and 60 PHR). It was observed that the obtained plasticizers exhibited the same plasticization efficiency and were characterized with good mechanical and physical properties in comparison to commercial plasticizers. The tensile strength was approx. 19 MPa, while the elongation at break was approx. 250% for all tested plasticizers at a concentration of 50 PHR. Furthermore, plasticizer migration studies showed that the synthesized plasticizers had excellent resistance to plasticizer leaching. The best migration test result obtained was 70% lower than that for DEHP or DINP. The ester mixture that was found to be the most favorable plasticizer was characterized by good thermal and thermo-oxidative stability (5% weight loss temperature: 227.8 °C in air and 261.1 °C in nitrogen). The results of the research clearly indicate that the synthesized esters can provide a green alternative to toxic phthalate plasticizers.
Face Masks to Combat Coronavirus (COVID-19)—Processing, Roles, Requirements, Efficacy, Risk and Sustainability
Authors: Rahman Md Zillur , Hoque Md Enamul , Alam Md Rubel , Rouf Md Abdur , Khan Saiful Islam , Xu Huaizhong , Ramakrishna Seeram
Published: March 2022

Increasingly prevalent respiratory infectious diseases (e.g., COVID-19) have posed severe threats to public health. Viruses including coronavirus, influenza, and so on can cause respiratory infections. A pandemic may potentially emerge owing to the worldwide spread of the virus through persistent human-to-human transmission. However, transmission pathways may vary; respiratory droplets or airborne virus-carrying particles can have a key role in transmitting infections to humans. In conjunction with social distancing, hand cleanliness, and other preventative measures, the use of face masks is considered to be another scientific approach to combat ubiquitous coronavirus. Different types of face masks are produced using a range of materials (e.g., polypropylene, polyacrylonitrile, polycarbonate, polyurethane, polystyrene, polyester and polyethylene) and manufacturing techniques (woven, knitted, and non-woven) that provide different levels of protection to the users. However, the efficacy and proper disposal/management of the used face masks, particularly the ones made of non-biodegradable polymers, pose great environmental concerns. This review compiles the recent advancements of face masks, covering their requirements, materials and techniques used, efficacy, challenges, risks, and sustainability towards further enhancement of the quality and performance of face masks.
Hemicellulose and Nano/Microfibrils Improving the Pliability and Hydrophobic Properties of Cellulose Film by Interstitial Filling and Forming Micro/Nanostructure
Authors: Li Yan , Yao Mingzhu , Liang Chen , Zhao Hui , Liu Yang , Zong Yifeng
Published: March 2022

In this paper, nano/microfibrils were applied to enhance the mechanical and hydrophobic properties of the sugarcane bagasse fiber films. The successful preparation of nano/microfibrils was confirmed by scanning electron microscope (SEM), X-ray diffraction (XRD), fiber length analyzer (FLA), and ion chromatography (IC). The transparency, morphology, mechanical and hydrophobic properties of the cellulose films were evaluated. The results show that the nanoparticle was formed by the hemicellulose diffusing on the surface of the cellulose and agglomerating in the film-forming process at 40 °C. The elastic modulus of the cellulose film was as high as 4140.60 MPa, and the water contact angle was increased to 113°. The micro/nanostructures were formed due to hemicellulose adsorption on nano/microfilament surfaces. The hydrophobicity of the films was improved. The directional crystallization of nano/microfibrous molecules was found. Cellulose films with a high elastic modulus and high elasticity were obtained. It provides theoretical support for the preparation of high-performance cellulose film.
Preparation, Properties and Water Dissolution Behavior of Polyethylene Oxide Mats Prepared by Solution Blow Spinning
Authors: Lorente Miguel Ángel , González-Gaitano Gustavo , González-Benito Javier
Published: March 2022

The relationship between processing conditions, structure and morphology are key issues to understanding the final properties of materials. For instance, in the case of polymers to be used as scaffolds in tissue engineering, wound dressings and membranes, morphology tuning is essential to control mechanical and wettability behaviors. In this work, the relationship between the processing conditions of the solution blow spinning process (SBS) used to prepare nonwoven mats of polyethylene oxide (PEO), and the structure and morphology of the resulting materials are studied systematically, to account for the thermal and mechanical behaviors and dissolution in water. After finding the optimal SBS processing conditions (air pressure, feed rate, working distance and polymer concentration), the effect of the solvent composition has been considered. The structure and morphology of the blow spun fibers are studied as well as their thermal, mechanical behaviors and dissolution in water. We demonstrate that the morphology of the fibers (size and porosity) changes with the solvent composition, which is reflected in different thermal and mechanical responses and in the dissolution rates of the materials in water.
Green Flame-Retardant Composites Based on PP/TiO2/Lignin Obtained by Melt-Mixing Extrusion
Authors: Andrade-Guel Marlene , Cabello-Alvarado Christian , Avila-Orta Carlos Alberto , Pérez-Alvarez Marissa , Cadenas-Pliego Gregorio , Reyes-Rodríguez Pamela Yahaira , Rios-González Leopoldo
Published: March 2022

Nowadays, highly flammable and harmful plastic materials are used in many daily applications. To prevent burning of materials, other harmful molecules or materials that are not environmentally friendly are added to plastics. To overcome this environmental issue, new materials have been investigated. Lignin, an industrial by-product, is an abundant biopolymer that can be used in fire safety plastics; it is considered a renewable and readily available resource. In this work, PP–TiO2/lignin composites were obtained with TiO2/lignin mixtures through the melt extrusion process, with different weight percentages of nanoparticles (10, 20, 25, and 30 wt.%). The PP–TiO2/lignin composites were characterized by XRD, FTIR, TGA, and SEM. Furthermore, cone calorimetry tests and the mechanical properties were evaluated. Cone calorimetry tests revealed that the introduction of 25 wt.% TiO2–lignin to the PP matrix reduced the peak of heat release rate (PHRR) and total heat release (THR) by 34.37% and 35.45%, respectively. The flame retardancy index (FRI) values of the composites were greater than 1.0 and were classified as good; the highest value of 1.93 was obtained in the PP-30 sample. The tensile tests demonstrated that the flexural modulus of the composites increased gradually with increasing lignin and TiO2 content, and the flexural strength decreased slightly. The use of lignin in PP composites can be an excellent alternative to synthesize new materials with improved flame-retardant properties and which is friendly to the environment.
Low-Cycle Fatigue Behavior of 3D-Printed PLA Reinforced with Natural Filler
Authors: Müller Miroslav , Šleger Vladimír , Kolář Viktor , Hromasová Monika , Piš Dominik , Mishra Rajesh Kumar
Published: March 2022

Additive production is currently perceived as an advanced technology, where intensive research is carried out in two basic directions—modifications of existing printing materials and the evaluation of mechanical properties depending on individual production parameters and the technology used. The current research is focused on the evaluation of the fatigue behavior of 3D-printed test specimens made of pure PLA and PLA reinforced with filler based on pinewood, bamboo, and cork using FDM (fused deposition modeling) technology. This research was carried out in response to the growing demand for filaments from biodegradable materials. This article describes the results of tensile fatigue tests and image analysis of the fracture surface determined by the SEM method. Biodegradable PLA-based materials have their limitations that influence their applicability in practice. One of these limitations is fatigue life, which is the cyclic load interval exceeding 50% of the tensile strength determined in a static test. Comparison of the cyclic fatigue test results for pure PLA and PLA reinforced with natural reinforcement, e.g., pinewood, bamboo, and cork, showed that, under the same loading conditions, the fatigue life of the 3D-printed specimens was similar, i.e., the filler did not reduce the material’s ability to respond to low-cycle fatigue. Cyclic testing did not have a significant effect on the change in tensile strength and associated durability during this loading interval for PLA-based materials reinforced with biological filler. Under cyclic loading, the visco-elastic behavior of the tested materials was found to increase with increasing values of cyclic loading of 30%, 50% and 70%, and the permanent deformation of the tested materials, i.e., viscoelastic behavior (creep), also increased. SEM analysis showed the presence of porosity, interlayer disturbances, and at the same time good interfacial compatibility of PLA with the biological filler.
Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams
Authors: Amin Muhammad Nasir , Iqbal Mudassir , Khan Kaffayatullah , Qadir Muhammad Ghulam , Shalabi Faisal I. , Jamal Arshad
Published: March 2022

Due to rise in infrastructure development and demand for seawater and sea sand concrete, fiber-reinforced polymer (FRP) rebars are widely used in the construction industry. Flexural strength is an important component of reinforced concrete structural design. Therefore, this research focuses on estimating the flexural capacity of FRP-reinforced concrete beams using novel artificial intelligence (AI) decision tree (DT) and gradient boosting tree (GBT) approaches. For this purpose, six input parameters, namely the area of bottom flexural reinforcement, depth of the beam, width of the beam, concrete compressive strength, the elastic modulus of FRP rebar, and the tensile strength of rebar at failure, are considered to predict the moment bearing capacity of the beam under bending loads. The models were trained using 60% of the database and were validated first-hand on the remaining 40% database employing the correlation coefficient (R), error indices namely, mean absolute error, root mean square error (MAE, RMSE) and slope of the regression line between observed and predicted results. The developed models were further validated using sensitivity and parametric analysis. Both models revealed comparable performance; however, based on the comparison of the slope of the validation data (0.83 for GBT model against 0.75 for the DT model) and higher R for the validation phase in case of the GBT model in comparison to the DT, the GBT model can be considered more accurate and robust. The sensitivity analysis yielded depth of the beam as the most influential parameter in contributing flexural strength of the beam, followed by the area of flexural reinforcement. The developed GBT model surpasses the existing gene expression programming (GEP) model in terms of accuracy; however, the current American Concrete Institute (ACI) model equations are more reliable than AI models in predicting the flexural strength of FRP-reinforced concrete beams.
Biogenic Synthesis of Antibacterial, Hemocompatible, and Antiplatelets Lysozyme Functionalized Silver Nanoparticles through the One-Step Process for Therapeutic Applications
Authors: Dudhagara Pravin , Alagiya Jemisha , Bhagat Chintan , Dudhagara Dushyant , Ghelani Anjana , Desai Jigna , Patel Rajesh , Vansia Ashaka , Nhiem Dao Ngoc , Chen Yih-Yuan , Shyu Douglas J. H.
Published: March 2022

To evaluate silver nanoparticles’ (AgNPs) therapeutic and clinical potentials, antibacterial action, blood compatibility, and antiplatelet activities are the main concerns for toxicity profiling. Heat-denatured lysozyme-mediated formulation stabilized the AgNPs, thereby providing more bactericidal activity and blood compatibility. The study of the synthesis of AgNPs suggests the rapid and cost-effective formulation of AgNPs by one-step reaction using a 10:1 ratio of silver nitrate and lysozyme by incubating at 60 °C for two hours. Characterization of AgNPs was analyzed by UV–Visible spectroscopy, DLS, TEM, EDX, XRD, AFM, and FTIR, followed by antibacterial, hemocompatibility, and platelet aggregation testing. The average size of synthesized AgNPs was found to be 94.10 nm with 0.45 mV zeta potential and 0.293 polydispersity index by DLS. The TEM and EXD results indicated homogeneously 28.08 nm spherical-shaped pure formations of AgNPs. The XRD peaks showed the synthesis of small AgNPs with a crystallite size of 22.88 nm, while the AFM confirmed the homogeneity and smoothness of the monodispersed AgNPs. The FTIR spectra specified the coating of the lysozyme-derived amide group on the AgNPs surface, which provides stability and functionality of nanoparticles. The antibacterial activity of AgNPs was remarkable against six pathogenic bacteria and three multidrug resistance (MDR) strains (i.e., Escherichia coli, Klebsiella aerogenes, and Pseudomonas aeruginosa), which exhibited inhibition zones with diameters ranging between 13.5 ±  0.2 mm to 19.0 ±  0.3 mm. The non-hemolytic nature of the AgNPs was calculated by percentage hemolysis with four concentrations. The negative result of platelet aggregation using platelet-rich plasma suggests the antiplatelet effect of AgNPs. Only minor hemolysis of 6.17% in human erythrocytes and mild platelet aggregation of 1.98% were induced, respectively, by the use of 1000 µL of 1 mM AgNPs, which contains approximately 107.8 μg silver. The results indicated that the antiplatelet potency and non-hemolytic nature with the antibacterial action of the lysozyme functionalized AgNPs have a good chance to be used to solve in-stent restenosis and thrombosis issues of the coronary stent and may also have a possibility to use in vaccination to resolve the blood clotting problem. So, the optimized biogenic formulation of AgNPs offers promising opportunities to be used as a therapeutic agent.
Effects of Different Point Defects on the Electronic Properties of III–V Al0.5Ga0.5N Photocathode Nanowires
Authors: Li Yiting , Fang Qianglong , Shen Yang , Zhang Shuqin , Yang Xiaodong , Ye Lanzhi , Chen Liang
Published: March 2022

AlxGa1−xN nanowires are the key materials for next-generation ultraviolet (UV) detectors. However, such devices have a low quantum efficiency caused by the introduction of defects and impurities throughout the preparation process of nanowires. Herein, the effects of different interstitial defects and vacancy defects on the electronic structure of Al0.5Ga0.5N nanowires are investigated using density functional theory calculations. Our results successfully discovered that only the formation of an N interstitial defect is thermally stable. In addition, the introduction of different defects makes the different nanowires exhibit n-type or p-type characteristics. Additionally, different defects lead to a decrease in the conduction band minimum in band structures, which is the major cause for the decrease in work function and increase in electron affinity of Al0.5Ga0.5N nanowires. What is more, the calculation of the partial density of states also proved that the interstitial defects contribute to a re-hybridization of local electron orbitals and then cause more significant movement of the electron density. Our investigations provide theoretical guidance for the pursuit of higher-quantum-efficiency ultraviolet (UV) detectors.
Mechanistic Investigations of the Synthesis of Lactic Acid from Glycerol Catalyzed by an Iridium–NHC Complex
Authors: Chen Shiyao , Xu Shuguang , Ge Chenyu , Hu Changwei
Published: March 2022

In the present work, the reaction pathways and the origin of catalytic activity for the production of lactic acid from glycerol catalyzed by an iridium–heterocyclic carbene (Iridium-NHC) complex at 383.15 K were investigated by DFT study at the M06-D3/6-311++G (d, p)//SDD level. Compared to the noncatalytic reaction pathway, the energy barrier sharply decreased from 75.2 kcal mol−1 to 16.8 kcal mol−1 with the introduction of the iridium–NHC complex. The catalytic reaction pathway catalyzed by the iridium–NHC complex with a coordinated hydroxide included two stages: the dehydrogenation of glycerol to 2,3-dihydroxypropanal, and the subsequent isomerization to lactic acid. Two reaction pathways, including dehydrogenation in terminal and that in C2-H, were studied. It was found that the formation of dihydroxyacetone from the H-removal in C2-H was more favorable, which might have been due to the lower energy of LUMO, whereas dihydroxyacetone could be easily transferred to 2,3-dihydroxypropanal. The analyses of electrostatic potential (ESP), hardness, and f- Fukui function also confirmed that the iridium–NHC complex acted as a hydrogen anion receptor and nucleophilic reaction center to highly promote the conversion of glycerol to lactic acid.
Development of Prediction Models for Pressure Loss and Classification Efficiency in Classifiers
Authors: Betz Michael , Nirschl Hermann , Gleiss Marco
Published: March 2022

This paper presents the development of prediction models for pressure loss and classification efficiency in classifiers. Classifiers belong to one of the most important classification devices in gas particle processing and a fast and accurate determination of pressure loss and cut size is of great interest. The first model developed in this work allows the calculation of pressure loss as a function of geometric and operational parameters. It is based on a number of measured values that are obtained from previous numerical simulations (CFD). The maximum deviation of the model is less than 20% and the model operates in real time. However, the model requires calibration for each type of classifier. The second model for classification efficiency is based on a simplified two-dimensional approach in which the flow profile and particle trajectories are determined exclusively for the area between two classifier blades. The model is applicable for all geometrical and operational parameters and calculates the desired parameters within a few minutes, with a maximum error rate of 25%. In combination, the two models allow for the process optimization of classifiers in complete systems.
Auto-Tuning of Attitude Control System for Heterogeneous Multirotor UAS
Authors: Hoshu Ayaz Ahmed , Wang Liuping , Sattar Abdul , Fisher Alex
Published: March 2022

This paper presents a heterogeneous configuration of the multirotor unmanned aerial system (UAS) that features the combined characteristics of the helicopter and quadrotor in a single multirotor design, featuring the endurance and energy efficiency similar to a helicopter, while keeping the mechanical simplicity, control, and manoeuvrability of the standard quadrotor. Power needed for a rotorcraft to hover has the inverse relation with the rotor disc. Therefore, multiple small rotors of the quadrotor are energetically outperformed by a large rotor of the helicopter, for a similar size. Designing the stable control system for such a dynamically complex multirotor configuration remains the main challenge as the studies previously carried out on these designs have successfully demonstrated energy efficiency but at the cost of degraded attitude control. Advancements in the energetics of the multirotor results in enhanced endurance and range that could be highly effective in remote operation applications. However, a stable control system is required for accurate positioning. In this paper, a cascaded PID control approach is proposed to provide the control solution for this heterogeneous multirotor. Automatic tuning is proposed to design the PID controller for each loop of the cascade structure. A relay feedback experiment is conducted in a controlled environment, followed by identification of the open-loop frequency response and estimation of dynamics. Subsequently, PID controllers are tuned through approximated models with the help of tuning rules. A custom-designed flight controller is used to experimentally implement the proposed control structure. Presented experimental results demonstrate the efficacy of the proposed control strategy for heterogeneous multirotor UAS.
Enhancing Front-Vehicle Detection in Large Vehicle Fleet Management
Authors: Mu Ching-Yun , Kung Pin , Chen Chien-Fu , Chuang Shu-Cheng
Published: March 2022

Transportation safety has been widely discussed for avoiding forward collisions. The broad concept of remote sensing can be applied to detect the front of vehicles without contact. The traditional Haar features use adjacent rectangular areas for many ordinary vehicle studies to detect the front vehicle images in practice. This paper focused on large vehicles using a front-installed digital video recorder (DVR) with a near-infrared (NIR) camera. The views of large and ordinary vehicles are different; thus, this study used a deep learning method to process progressive improvement in moving vehicle detection. This study proposed a You Only Look Once version 4 (YOLOv4) supplemented with the fence method, called YOLOv4(III), to enhance vehicle detection. This method had high detection accuracy and low false omission rates using the general DVR equipment, and it provided comparison results. There was no need to have a high specification front camera, and the proposed YOLOv4(III) was found to have competitive performance. YOLOv4(III) reduced false detection rates and had a more stable frame per second (FPS) performance than with Haar features. This improved detection method can give an alert for large vehicle drivers to avoid serious collisions, leading to a reduction in the waste of social resources.
Study on Significance Enhancement Algorithm of Abnormal Features of Urban Road Ground Penetrating Radar Images
Authors: Li Fanruo , Yang Feng , Yan Rui , Qiao Xu , Xing Hongjia , Li Yijin
Published: March 2022

Currently, ground penetrating radar is the major technology for the detection of urban road collapses and disaster sources. Vehicle-mounted GPR collects tens of GB of data on site every day, but the present interpretation of abnormal regions detected by radar relies on manual interpretation with low process efficiency. The abnormal region images of GPR are different from the surrounding normal images. In terms of the features of abnormal regions in GPR images with an obvious brightness change and obvious directional characteristics, an abnormal region detection algorithm based on visual attention mechanism is proposed. Firstly, the complex background noise in the GPR images is suppressed by wavelet denoising and gamma transform, and the brightness and directional characteristics of the abnormal regions are enhanced. Secondly, by building a multi-scale image brightness and orientation feature pyramid model, the features of abnormal regions of interest are continuously enhanced, and the rapid screening of abnormal regions has been achieved. The effectiveness of the algorithm has been verified by actual tests on different types of abnormal radar image data.
Impact of Ocean Currents on Wind Stress in the Tropical Indian Ocean
Authors: Chacko Neethu , Ali Meer M. , Bourassa Mark A.
Published: March 2022

This study examines the effect of surface currents on the bulk algorithm calculation ofwind stress estimated using the scatterometer data during 2007–2020 in the Indian Ocean. In the study region as a whole, the wind stress decreased by 5.4% by including currents in the wind stress equation. The most significant reduction in the wind stress is found along the most energetic regions with strong currents such as Somali Current, Equatorial Jets, and Agulhas retroflection. The highest reduction of 11.5% is observed along the equator where the Equatorial Jets prevail. A sensitivity analysis has been carried out for the study region and for different seasons to assess the relative impact of winds and currents in the estimation of wind stress by changing the winds while keeping the currents constants and vice versa. The inclusion of currents decreased the wind stress (consistent with scatterometer winds) and this decrease is prominent when the currents are stronger. This study showed that the equatorial Indian Ocean is the most sensitive region where the current can impact wind stress estimation. The results showed that uncertainties in the wind stress estimations are quite large at regional levels and hence better representation of wind stress incorporating ocean currents should be considered in the ocean/climatic models for accurate air-sea interaction studies that are not based on remotely sensed winds.
Coordination of Complementary Sets for Low Doppler-Induced Sidelobes
Authors: Zhu Jiahua , Fan Chongyi , Song Yongping , Huang Xiaotao , Zhang Bingbing , Ma Yanxin
Published: March 2022

Golay complementary waveforms are, by definition, able to generate narrow pulses with low sidelobes via coherent signal processing. However, while an ideal impulse can be obtained for target returns at zero-Doppler, significant sidelobes are observed at nonzero Doppler shifts. In this paper, a Generalized Binominal Design (GBD) procedure is proposed for the waveforms consisting of complementary sets in an attempt to reduce the Doppler-induced sidelobes. Our theoretical analysis as well as simulation results show that the proposed approach performs as good as existing Binominal Design method used for sidelobe suppression with Golay complementary waveforms and can also achieve around 28% enhancement on the Doppler resolution, with an acceptable loss in peak to peak-sidelobe ratio (PPSR).
Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network
Authors: Ghadi Yazeed Yasin , Rafique Adnan Ahmed , al Shloul Tamara , Alsuhibany Suliman A. , Jalal Ahmad , Park Jeongmin
Published: March 2022

The latest visionary technologies have made an evident impact on remote sensing scene classification. Scene classification is one of the most challenging yet important tasks in understanding high-resolution aerial and remote sensing scenes. In this discipline, deep learning models, particularly convolutional neural networks (CNNs), have made outstanding accomplishments. Deep feature extraction from a CNN model is a frequently utilized technique in these approaches. Although CNN-based techniques have achieved considerable success, there is indeed ample space for improvement in terms of their classification accuracies. Certainly, fusion with other features has the potential to extensively improve the performance of distant imaging scene classification. This paper, thus, offers an effective hybrid model that is based on the concept of feature-level fusion. We use the fuzzy C-means segmentation technique to appropriately classify various objects in the remote sensing images. The segmented regions of the image are then labeled using a Markov random field (MRF). After the segmentation and labeling of the objects, classical and CNN features are extracted and combined to classify the objects. After categorizing the objects, object-to-object relations are studied. Finally, these objects are transmitted to a fully convolutional network (FCN) for scene classification along with their relationship triplets. The experimental evaluation of three publicly available standard datasets reveals the phenomenal performance of the proposed system.
Characteristic Scales of Tropical Convection Based on the Japanese Advanced Himawari-8 Imager Observations
Authors: Pu Jingchen , Zou Xiaolei
Published: March 2022

Convective activities play an important role in tropical weather systems. To investigate the characteristic scales of convection, a method combining a principal component (PC) analysis with Fourier decomposition is applied to brightness temperature observations from Advanced Himawari-8 Imager (AHI). Characteristic scales of different modes in tropical convective systems are obtained. The explained variance reduces rapidly from the first to the 60th PC mode by two magnitudes; the horizontal scale decreases from over 2000 km to about 100 km, and the timescale changes from more than 4 days to around 5 h. By a detailed comparison of the first, 20th, 40th, and 60th PC modes, it is found that large-scale (over 2000 km in wavelength) convective activities usually have significant nocturnal enhancement, whereas meso-scale (about 100 km in wavelength) convective activities feature short period and fast change with more intense development in regions of active large-scale convection. This study may be of some importance for the choice of AHI data assimilation cycling interval, the horizontal resolution as well as data thinning for geostationary satellite observations.
Robust Clutter Suppression and Radial Velocity Estimation for High-Resolution Wide-Swath SAR-GMTI
Authors: Zhang Zhenning , Yu Weidong , Zheng Mingjie , Zhao Liangbo , Zhou Zi-Xuan
Published: March 2022

Moving targets are usually smeared and imaged at incorrect positions in synthetic aperture radar (SAR) images due to the target motions during the illumination time. Moreover, a moving target will cause multiple artifacts in the reconstructed image since pulse repetition frequency (PRF) operated in high-resolution wide-swath (HRWS) SAR is very low. In order to reliably indicate moving targets, a robust cancellation algorithm is derived in this paper for clutter suppression in multichannel HRWS SAR, which is free by velocity searching and covariance matrix estimation of clutter plus noise. The proposed multilayer channel-cancellation combined with the deramp processing is designed to sequentially suppress the seriously aliased clutter in HRWS SAR. Experimental results show that the proposed algorithm is efficient and robust in tough situations, and have a superior detection ability in weak targets and low-velocity targets. In addition, the radial velocity estimation algorithm combined with the channel cancellation is exploited to relocate moving targets. The effectiveness of the proposed algorithms is validated by actual spaceborne SAR data acquired by a coordination experiment with two controllable vehicles.
Assessing the Sensitivity of Vegetation Cover to Climate Change in the Yarlung Zangbo River Basin Using Machine Learning Algorithms
Authors: Cui Lizhuang , Pang Bo , Zhao Gang , Ban Chunguang , Ren Meifang , Peng Dingzhi , Zuo Depeng , Zhu Zhongfan
Published: March 2022

Vegetation is a key indicator of the health of most terrestrial ecosystems and different types of vegetation exhibit different sensitivity to climate change. The Yarlung Zangbo River Basin (YZRB) is one of the highest basins in the world and has a wide variety of vegetation types because of its complex topographic and climatic conditions. In this paper, the sensitivity to climate change for different vegetation types, as reflected by the Normalized Difference Vegetation Index (NDVI), was assessed in the YZRB. Three machine learning models, including multiple linear regression, support vector machine, and random forest, were adopted to simulate the response of each vegetation type to climatic variables. We selected random forest, which showed the highest performance in both the calibration and validation periods, to assess the sensitivity of the NDVI to temperature and precipitation changes on an annual and monthly scale using hypothetical climatic scenarios. The results indicated there were positive responses of the NDVI to temperature and precipitation changes, and the NDVI was more sensitive to temperature than to precipitation on an annual scale. The NDVI was predicted to increase by 1.60%–4.68% when the temperature increased by 1.5 °C, while it only changed by 0.06%–0.24% when the precipitation increased by 10% in the YZRB. Monthly, the vegetation was more sensitive to temperature changes in spring and summer. Spatially, the vegetation was more sensitive to temperature increases in the upper and middle reaches, where the existing temperatures were cooler. The time-lag effects of climate were also analyzed in detail. For both temperature and precipitation, Needleleaf Forest and Broadleaf Forest had longer time lags than those of other vegetation types. These findings are useful for understanding the eco-hydrological processes of the Tibetan Plateau.
Diversity Effects on Canopy Structure Change throughout a Growing Season in Experimental Grassland Communities
Authors: Guimarães-Steinicke Claudia , Weigelt Alexandra , Ebeling Anne , Eisenhauer Nico , Wirth Christian
Published: March 2022

Increasing plant diversity commonly enhances standing biomass and other ecosystem functions (i.e., carbon fluxes, water use efficiency, herbivory). The standing biomass is correlated with vegetation volume, which describes plant biomass allocation within a complex canopy structure. As the canopy structure of plant communities is not static throughout time, it is expected that its changes also control diversity effects on ecosystem functioning. Yet, most studies are based on one or two measures of ecosystem function per year. Here, we examine the temporal effects of diversity of grassland communities on canopy structural components in high temporal (bi-weekly throughout the growing season) and spatial resolutions as a proxy for ecosystem functioning. Using terrestrial laser scanning, we estimate metrics of vertical structure, such as biomass distribution (evenness) and highest biomass allocation (center of gravity) along height strata. For horizontal metrics, we calculated community stand gaps and canopy surface variation. Our findings show that species-rich communities start filling the vertical space (evenness) earlier in the growing season, suggesting a more extended period of resource use (i.e., light-harvesting). Moreover, more diverse communities raised their center of gravity only at the peak of biomass in spring, likely triggered by higher interspecific competition inducing higher biomass allocation at upper layers of the canopy. Furthermore, richer communities were clumpier only after mowing, revealing species-specific differences in regrowth. Lastly, species richness strongly affected canopy variation when the phenology status and height differences were maximal, suggesting differences in plant functional strategies (space to grow, resource use, and flowering phenology). Therefore, the effects of diversity on ecosystem functions depending on those structural components such as biomass production, decomposition, and herbivory, may also change throughout the season due to various mechanisms, such as niche differences, increased complementarity, and temporal and spatial variation in biological activity.
Optimizing a Standard Spectral Measurement Protocol to Enhance the Quality of Soil Spectra: Exploration of Key Variables in Lab-Based VNIR-SWIR Spectral Measurement
Authors: Xu Zhengyuan , Chen Shengbo , Lu Peng , Wang Zibo , Li Anzhen , Zeng Qinghong , Chen Liwen
Published: March 2022

The method of proximal VNIR-SWIR (with a spectral region of 400–2500 nm) spectroscopy in a laboratory setting has been widely employed in soil property estimations. Increasing attention has been focused recently on establishing an agreed-upon protocol for soil spectral measurement, fueled by the recognition that studies carried out under different laboratory settings have made future data sharing and model comparisons difficult. This study aimed to explore the key factors in a lab-based spectral measurement procedure to provide recommendations for enhancing the spectra quality and promoting the development of the spectral measurement protocol. To this aim, with the support of the standard spectral laboratory at Jilin University, China, we designed and performed control experiments on four key factors—the light interference in the measurement course, soil temperature, soil moisture, and soil particle size—to quantify the variation in the spectra quality by the subsequent estimation accuracies of different estimation models developed with different spectra obtained from control groups. The results showed that (1) the soil–probe contact measurement derived the optimum spectra quality and estimation accuracy; however, close-non-contact measurement also achieved acceptable results; (2) sieving the soil sample into particle sizes below 1 mm and drying before spectral measurement effectively enhanced spectra quality and estimation accuracy; (3) the variation in soil temperature did not have a distinct influence on spectra quality, and the estimation accuracies of models developed based on soil samples at 20–50 °C were all acceptable. Moreover, a 30-min warm-up of the spectrometer and contact probe was found to be effective. We carried out a complete and detailed control experiment process, the results of which offer a guide for optimizing the process of laboratory-based soil proximal spectral measurement to enhance spectra quality and corresponding estimation accuracy. Furthermore, we present theoretical support for the development of the spectral measurement protocol. We also present optional guidance with relatively lower accuracy but effective results, which are save time and are low cost for future spectral measurement projects.
Unifying Obstacle Detection, Recognition, and Fusion Based on the Polarization Color Stereo Camera and LiDAR for the ADAS
Authors: Long Ningbo , Yan Han , Wang Liqiang , Li Haifeng , Yang Qing
Published: March 2022

The perception module plays an important role in vehicles equipped with advanced driver-assistance systems (ADAS). This paper presents a multi-sensor data fusion system based on the polarization color stereo camera and the forward-looking light detection and ranging (LiDAR), which achieves the multiple target detection, recognition, and data fusion. The You Only Look Once v4 (YOLOv4) network is utilized to achieve object detection and recognition on the color images. The depth images are obtained from the rectified left and right images based on the principle of the epipolar constraints, then the obstacles are detected from the depth images using the MeanShift algorithm. The pixel-level polarization images are extracted from the raw polarization-grey images, then the water hazards are detected successfully. The PointPillars network is employed to detect the objects from the point cloud. The calibration and synchronization between the sensors are accomplished. The experiment results show that the data fusion enriches the detection results, provides high-dimensional perceptual information and extends the effective detection range. Meanwhile, the detection results are stable under diverse range and illumination conditions.
LoRaWAN Behaviour Analysis through Dataset Traffic Investigation
Authors: Spadaccino Pietro , Crinó Francesco Giuseppe , Cuomo Francesca
Published: March 2022

The large development of Internet of Things technologies is increasing the use of smart-devices to solve and support several real-life issues. In many cases, the aim is to move toward systems that, even if significant demands are not required in terms of quantity of exchanged data, they should be very reliable in terms of battery life and signal coverage. Networks that have these characteristics are the Low Power WAN (LPWAN). One of the most interesting LPWAN is LoRaWAN. LoRaWAN is a network with four principal components: end-devices, gateways, network servers, and application servers. It uses a LoRa physical layer to exchange messages between end-devices and gateways that forward these messages, through classic TCP/IP protocol, to the network server. In this work, we analyse LoRa and LoRaWAN by looking at its transmission characteristics and network behaviour, respectively, explaining the role of its components and showing the message exchange. This analysis is performed through the exploration of a dataset taken from the literature collecting real LoRaWAN packets. The goal of the work is twofold: (1) to investigate, under different perspectives, how a LoRaWAN works and (2) to provide software tools that can be used in several other LoraWAN datasets to measure the network behaviour. We carry out six different analyses to look at the most important features of LoRaWAN. For each analysis we present the adopted measurement strategy as well as the obtained results in the specific use case.
A Low-Latency Divider Design for Embedded Processors
Authors: Wei Xiaotong , Yang Ying , Chen Jie
Published: March 2022

Division is generally regarded as a low-frequency, high-latency operation in integer operations. Division is also the operation that stalls the processor pipeline most frequently. In order to improve the overall performance of embedded processors, a low-delay divider for embedded processors was designed. Based on the non-restoring algorithm, the divider uses a compound adder to execute addition and subtraction simultaneously and reduces the iteration path delay. By shifting the operands to align the most effective bits, the divider dynamically adjusts the number of iteration cycles to reduce the average number of cycles in the division process. The divider design was simulated by Modelsim and implemented on a FPGA board for verification. Synthesized in a Semiconductor Manufacturing International Corporation (SMIC) 65 nm Low Leakage process, the achieved frequency of the design was up to 500 MHz and the area cost was 5670.36 μm2. Compared with other dividers, the proposed divider design can reduce the delay of single iteration by up to 45.3%, save the average number of iteration cycles by 20–50%, and save the area by 23.3–86.1%. Compared with other dividers implemented on FPGA, it saves LUTs by 36.47–59.6% and FFs by 67–84.28%, runs 2–6.36 times faster. Therefore, the proposed design is suitable for embedded processors that require low power consumption, low resource consumption, and high performance.