bluuebunny/crossref_metadata_2025 huggingface dataset:
Azospirillum brasilense and Zinc Rates Effect on Fungal Root Colonization and Yield of Wheat-Maize in Tropical Savannah Conditions
Authors: Silva Philippe Solano Toledo , Cassiolato Ana Maria Rodrigues , Galindo Fernando Shintate , Jalal Arshad , Nogueira Thiago Assis Rodrigues , Oliveira Carlos Eduardo da Silva , Filho Marcelo Carvalho Minhoto Teixeira
Published: November 2022

A successful microbial inoculant can increase root colonization and establish a positive interaction with native microorganisms to promote growth and productivity of cereal crops. Zinc (Zn) is an intensively reported deficient nutrient for maize and wheat production in Brazilian Cerrado. It can be sustainably managed by inoculation with plant growth-promoting bacteria and their symbiotic association with other microorganisms such as arbuscular mycorrhizal fungi (AMF) and dark septate endophytes (DSE). The objective of this study was to evaluate the effect of Azospirillum brasilense inoculation and residual Zn rates on root colonization and grain yield of maize and wheat in succession under the tropical conditions of Brazil. These experiments were conducted in a randomized block design with four replications and arranged in a 5 × 2 factorial scheme. The treatments consisted of five Zn rates (0, 2, 4, 6 and 8 kg ha−1) applied from zinc sulfate in maize and residual on wheat and without and with seed inoculation of A. brasilense. The results indicated that root colonization by AMF and DSE in maize–wheat cropping system were significantly increased with interaction of Zn rates and inoculation treatments. Inoculation with A. brasilense at residual Zn rates of 4 kg ha−1 increased root colonization by AMF under maize cultivation. Similarly, inoculation with A. brasilense at residual Zn rates of 2 and 4 kg ha−1 reduced root colonization by DSE under wheat in succession. The leaf chlorophyll index and leaf Zn concentration were increased with inoculation of the A. brasilense and residual Zn rates. The inoculation did not influence AMF spore production and CO2-C in both crops. The grain yield and yield components of maize–wheat were increased with the inoculation of A. brasilense under residual Zn rates of 3 to 4 kg ha−1 in tropical savannah conditions. Inoculation with A. brasilense under residual Zn rates up to 4 kg ha−1 promoted root colonization by AMF and DSE in the maize cropping season. While the inoculation with A. brasilense under 2 and 4 kg ha−1 residual Zn rates reduced root colonization by AMF and DSE in the wheat cropping season. Therefore, inoculation with A. brasilense in combination with Zn fertilization could consider a sustainable approach to increase the yield and performance of the maize–wheat cropping system in the tropical savannah conditions of Brazil.
Color Stability of Zinc Oxide Poly(methyl methacrylate) Nanocomposite—A New Biomaterial for Denture Bases
Authors: Szerszeń Marcin , Cierech Mariusz , Wojnarowicz Jacek , Górski Bartłomiej , Mierzwińska-Nastalska Elżbieta
Published: November 2022

(1) Background: The purpose of this in vitro study was to evaluate the color change and stability of a zinc oxide nanoparticle–poly(methyl methacrylate) (ZnO NP–PMMA) nanocomposite for denture base material after immersion in different dietary and cleaning agent solutions. (2) Methods: One hundred samples were prepared and divided into four equinumerous groups depending on the weight content of ZnO NPs. The color coordinates (CIE L*a*b*) were measured using a digital colorimeter, ColorReader (Datacolor AG Europe, Rotkreuz, Switzerland), before and after immersion of the specimens in five different solutions (distilled water, coffee, red wine, black tea, denture cleaning tablet solution) for 6 months. The color changes (ΔE) were calculated using Euclidean distance and analyzed by the Shapiro–Wilk test and the ANOVA/Kruskal–Wallis multiple comparison and adequate post hoc tests. (3) Results: All tested materials showed significant color changes after their exposure to all solutions. Color changes were greatest in the case of red wine and progressed with the duration of the study. (4) Conclusions: The modification of PMMA with ZnO nanoparticles is acceptable in aesthetic terms in 2.5% and 5% weight content; however, color changes are more noticeable with higher nanoparticle content and must be discussed with the patient prior to possible use.
In Situ Synthesis of AZO-Np in Guar Gum/PVOH Composite Fiber Mats for Potential Bactericidal Release
Authors: Lubambo Adriana Freire , Mattoso Ney , Ono Lucy , da Luz Gisele Gomes , Gavinho Bruno , Martin Andressa Amado , Sierakowski Maria Rita , Saul Cyro Ketzer
Published: November 2022

Since the number of antibiotic-resistant bacterial infections is growing and cases are getting worse every year, the search for new alternative bactericidal wound dressing treatments is becoming crucial. Within this context, the use of polysaccharides from plants and seeds in innovative biopolymer technologies is of key importance. In this work, bio-nano-composite guar gum/polyvinyl alcohol (PVOH) membranes loaded with aluminum-doped zinc oxide nanoparticles were produced via electrospinning. Citric acid was added to the mixture to increase spinnability. However, depending on the pH, zinc oxide nanoparticles are partially dissociated, decreasing their bactericidal efficiency. Thus, a second successful alkaline thermo-chemical regrowth step was added to the process to treat the obtained fibers. This alkaline thermo-chemical treatment reconstituted both the nanoparticles and their bactericidal properties. The Staphylococcus aureus antibacterial assay results show that the membranes obtained after the alkaline thermo-chemical treatment presented a 57% increase in growth inhibition.
A Multiaxial Fatigue Damage Model Based on Constant Life Diagrams for Polymer Fiber-Reinforced Laminates
Authors: Elkin Aleksandr , Gaibel Viktor , Dzhurinskiy Dmitry , Sergeichev Ivan
Published: November 2022

In the last decade, fatigue damage models for fiber-reinforced polymer composites have been developed assuming the fracture energy equivalence hypothesis. These models are able to predict a fatigue life of composite laminates, but their identification requires a significant number of off-axial tests for various stress ratios. The present study proposes the stress ratio dependent model, which phenomenologically adopts a decrease in stiffness and residual strength of a unique ply according to experimental constant life diagrams. Hashin, Tsai–Hill, and the maximum stress failure criteria are utilized for damage initiation considering the residual strength of the ply. The obtained results indicate a sufficiency of using S-N curves for UD 0°, UD 45°, and UD 90° for identification of the model. The model was verified by S-N curves for UD 10°, UD 15°, and UD 30° and its applicability was demonstrated for prediction of a fatigue life of composite laminates with an arbitrary lay-up. The model is implemented into ABAQUS finite element software as a user subroutine.
Fused Filament Fabrication of Short Glass Fiber-Reinforced Polylactic Acid Composites: Infill Density Influence on Mechanical and Thermal Properties
Authors: Chicos Lucia-Antoneta , Pop Mihai Alin , Zaharia Sebastian-Marian , Lancea Camil , Buican George Razvan , Pascariu Ionut Stelian , Stamate Valentin-Marian
Published: November 2022

Fused Filament Fabrication (FFF) is one of the frequently used material extrusion (MEX) additive manufacturing processes due to its ability to manufacture functional components with complex geometry, but their properties depend on the process parameters. This paper focuses on studying the effects of process parameters, namely infill density (25%, 50%, 75%, and 100%), on the mechanical and thermal response of the samples made of poly(lactic acid) (PLA) reinforced with short glass fibers (GF) produced using FFF process. To perform a comprehensive analysis, tensile, flexural, compression, differential scanning calorimetry (DSC), and thermal gravimetric analysis (TGA) tests were used. The paper also aims to manufacture by FFF process of composite structures of the fuselage section type, as structural elements of an unmanned aerial vehicle (UAV), and their testing to compression loads. The results showed that the tensile, flexural and compression strength of the additive manufactured (AMed) samples increased with the increase of infill density and therefore, the samples with 100% infill density provides the highest mechanical characteristics. The AMed samples with 50% and 75% infill density exhibited a higher toughness than samples with 100% infill. DSC analyses revealed that the glass transition (Tg), and melting (Tm) temperature increases slightly as the infill density increases. Thermogravimetric analyses (TGA) show that PLA-GF filament loses its thermal stability at a temperature of about 311 °C and the increase in fill density leads to a slight increase in thermal stability and the complete degradation temperature of the AMed material. The compression tests of the fuselage sections manufactured by FFF made of PLA-GF composite showed that their stiffening with stringers oriented at an angle of ±45° ensures a higher compression strength than the stiffening with longitudinal stringers.
Tensile Behavior of Joints of Strip Ends Made of Polymeric Materials
Authors: Mihalache Andrei-Marius , Ermolai Vasile , Sover Alexandru , Nagîț Gheorghe , Boca Marius-Andrei , Slătineanu Laurențiu , Hrițuc Adelina , Dodun Oana , Rîpanu Marius-Ionuț
Published: November 2022

The strength of a joint between the ends of one or more strips can be improved by making the contours of the joint into the shape of either the Greek letter omega or of a dovetail. From the point of view of industrial practice, it is of interest to study the behavior of these joints under stretching demands. The emergence and development of additive manufacturing processes for parts made of polymeric materials has led to the idea of conducting experimental tests to highlight the behavior of omega and dovetail-type joints during the tensile test. For the tensile testing of some test samples in which omega and dovetail joints were used, a Taguchi array of type L18 was employed, with eight independent variables, one variable with a two-level variation, and seven variables with variations on three levels. As independent variables, the type of joint, the couple of polymer materials used to make the two components of the joint, some characteristic dimensions of the joint contours, and some input factors in the 3D printing process were established. The values of average force and average displacement at the peak were considered output parameters. The experimental results were mathematically processed, determining empirical mathematical models of the second-degree polynomial type. These models highlight the influence exerted by the considered input factors on the values of the output parameters.
Synthesis of Cellulose Nanocrystals/HKUST-1 Composites and Their Applications: Crystal Violet Removal and Doxorubicin Loading
Authors: Wijaya Christian J. , Soetaredjo Felycia E. , Ismadji Suryadi , Gunawan Setiyo
Published: November 2022

This study developed a novel composite material containing cellulose nanocrystals (CNCs) and HKUST-1. Here, the addition of CNCs was used to enhance the characteristics of HKUST-1 in terms of surface area, adsorption ability, and functional groups. Here, the fabrication of CNCs@HKUST-1 composites was carried out by adding CNCs into the fabrication process of HKUST-1. The addition of CNCs provides additional functional groups on the surface of composite material which can be used to attach other organic compounds, such as in waste management and drug delivery systems. Here, CNCs@HKUST-1 composites were tested as a material for crystal violet (CV) removal and doxorubicin (DOX) loading. The removal capacity of CNCs@HKUST-1 composite towards CV molecules reached 1182.25 ± 27.74 mg/g, while the loading capacity for DOX drugs was around 1514.94 ± 11.67 mg/g. Both applications showed that CNCs@HKUST-1 composite had higher adsorption capacity and ability compared to its precursor materials, i.e., CNCs and HKUST-1.
Preparation, Characterization, and Anti-Adhesive Activity of Sulfate Polysaccharide from Caulerpa lentillifera against Helicobacter pylori
Authors: Le Bao , Do Duy Thanh , Nguyen Hien Minh , Do Bich Hang , Le Huong Thuy
Published: November 2022

In the gastric mucosa, chronic inflammation due to Helicobacter pylori infection promotes gastrocarcinogenesis. Polysaccharides of Caulerpa lentillifera are well-characterized by broad antimicrobial activity and anti-inflammatory potentials. The present study was undertaken to investigate whether the low molecular sulfate polysaccharides of C. lentillifera (CLCP) exhibit any anti-adhesive activity against H. pylori. After a hot water extraction and purification process, two purified polysaccharide fractions (CLCP-1 and CLCP2) were studied based on structural characterization and bioactivity determination. The results implied that except for the molar ratio, CLCP-1 and CLCP-2 contain high sulfate, mannose, galactose, xylose, glucose levels, and low protein levels. The molecular weight and Fourier transform infrared spectroscopy (FT-IR) assays confirmed that CLCP-1 and CLCP-2 are sulfate polysaccharides with an average molecular weight (Mw) of 963.15 and 648.42 kDa, respectively. In addition, CLCP-1 and CLCP-2 exhibited stronger antibacterial activity against H. pylori. CLCP-1 and CLCP-2 could significantly promote macrophage proliferation and decrease the production of nitric oxide (NO) through downregulated expression of inducible nitric oxide synthase (iNOS). Meanwhile, CLCP-1 and CLCP-2 in this study showed efficiently protected gastric adenocarcinoma (AGS) cells against H. pylori with the inhibition of the IL-8/NF-κB axis. These findings suggested the effect of Caulerpa lentillifera polysaccharides on H. pylori adhesion, a potential supply of nutrients for eradication therapy through the reduction of cell count and inflammation.
Critical Review on the Progress of Plastic Bioupcycling Technology as a Potential Solution for Sustainable Plastic Waste Management
Authors: Lomwongsopon Passanun , Varrone Cristiano
Published: November 2022

Plastic production worldwide has doubled in the last two decades and is expected to reach a four-fold increase by 2050. The durability of plastic makes them a perfect material for many applications, but it is also a key limitation to their end-of-life management. The current plastic lifecycle is far from circular, with only 13% being collected for recycling and 9% being successfully recycled, indicating the failure of current recycling technology. The remaining plastic waste streams are thus incinerated, landfilled, or worse, mismanaged, leading to them leaking into the environment. To promote plastic circularity, keeping material in the loop is a priority and represents a more sustainable solution. This can be achieved through the reuse of plastic items, or by using plastic waste as a resource for new materials, instead of discarding them as waste. As the discovery of plastic-degrading/utilizing microorganisms and enzymes has been extensively reported recently, the possibility of developing biological plastic upcycling processes is opening up. An increasing amount of studies have investigated the use of plastic as a carbon source for biotechnological processes to produce high-value compounds such as bioplastics, biochemicals, and biosurfactants. In the current review, the advancements in fossil-based plastic bio- and thermochemical upcycling technologies are presented and critically discussed. In particular, we highlight the developed (bio)depolymerization coupled with bioconversion/fermentation processes to obtain industrially valuable products. This review is expected to contribute to the future development and scale-up of effective plastic bioupcycling processes that can act as a drive to increase waste removal from the environment and valorize post-consumer plastic streams, thus accelerating the implementation of a circular (plastic) economy.
Enhancing the Mechanical Properties of 3D-Printed Waterborne Polyurethane-Urea and Cellulose Nanocrystal Scaffolds through Crosslinking
Authors: Vadillo Julen , Larraza Izaskun , Calvo-Correas Tamara , Martin Loli , Derail Christophe , Eceiza Arantxa
Published: November 2022

In this work, shape-customized scaffolds based on waterborne polyurethane-urea (WBPUU) were prepared via the combination of direct ink writing 3D-printing and freeze-drying techniques. To improve the printing performance of the ink and guarantee a good shape fidelity of the scaffold, cellulose nanocrystals (CNC) were added during the synthesis of the WBPUU and some of the printed constructs were immersed in CaCl2 prior to the freeze-drying process to promote ionic crosslinking between calcium ions and the polyurethane. The results showed that apart from allowing the ink to be successfully printed, obtaining scaffolds with good shape fidelity, the addition of the CNC resulted in a greater homogeneity of the porous structure as well as an increase of the swelling capacity of the scaffolds. Additionally, the CNC has a reinforcement effect in the printed systems, presenting a higher compression modulus as the CNC content increases. In the case of samples crosslinked by calcium ions, a rigid shell was observed by scanning electron microscopy, which resulted in stiffer scaffolds that presented a lower water absorption capacity as well as an enhancement of the thermal stability. These results showed the potential of this type of post-printing process to tune the mechanical properties of the scaffold, thus widening the potential of this type of material.
Bacterial Cellulose Composites with Polysaccharides Filled with Nanosized Cerium Oxide: Characterization and Cytocompatibility Assessment
Authors: Petrova Valentina A. , Gofman Iosif V. , Golovkin Alexey S. , Mishanin Alexander I. , Dubashynskaya Natallia V. , Khripunov Albert K. , Ivan’kova Elena M. , Vlasova Elena N. , Nikolaeva Alexandra L. , Baranchikov Alexander E. , Skorik Yury A. , Yakimansky Alexander V. , Ivanov Vladimir K.
Published: November 2022

A new biocompatible nanocomposite film material for cell engineering and other biomedical applications has been prepared. It is based on the composition of natural polysaccharides filled with cerium oxide nanoparticles (CeONPs). The preparative procedure consists of successive impregnations of pressed bacterial cellulose (BC) with a sodium alginate (ALG) solution containing nanoparticles of citrate-stabilized cerium oxide and a chitosan (CS) solution. The presence of CeONPs in the polysaccharide composite matrix and the interaction of the nanoparticles with the polymer, confirmed by IR spectroscopy, change the network architecture of the composite. This leads to noticeable changes in a number of properties of the material in comparison with those of the matrix’s polysaccharide composition, viz., an increase in mechanical stiffness, a decrease in the degree of planar orientation of BC macrochains, an increase in hydrophilicity, and the shift of the processes of thermo-oxidative destruction of the material to a low-temperature region. The latter effect is considered to be caused by the redox activity of cerium oxide (reversible transitions between the states Ce4+ and Ce3+) in thermally stimulated processes in the nanocomposite films. In the equilibrium swollen state, the material retains a mechanical strength at the level of ~2 MPa. The results of in vitro tests (cultivation of multipotent mesenchymal stem cells) have demonstrated the good biocompatibility of the BC-ALG(CeONP)-CS film as cell proliferation scaffolds.
A Critical Review on the Feasibility of Synthetic Polymers Inclusion in Enhancing the Geotechnical Behavior of Soils
Authors: Almajed Abdullah , Lemboye Kehinde , Moghal Arif Ali Baig
Published: November 2022

Polymers have attracted widespread interest as soil stabilizers and are proposed as an ecologically acceptable means for enhancing the geotechnical properties of soils. They have found profound applications in diverse fields such as the food industry, textile, medicine, agriculture, construction, and many more. Various polymers are proven to increase soil shear strength, improve volume stability, promote water retention, and prevent erosion, at extremely low concentrations within soils through the formation of a polymer membrane around the soil particles upon hydration. The purpose of this work is to provide an overview of existing research on synthetic polymers for soil improvement. A fundamental evaluation of many synthetic polymers used in soil stabilization is provided, Furthermore, the impact of different polymer types on the geotechnical parameters of treated soil was assessed and compared. Limiting factors like polymer durability and the effect of changing climatic conditions on the engineering behavior of the polymer-treated soils have been critically reviewed. The dominant mechanisms responsible for the alteration in the behavior of polymer-soil admixture are reviewed and discussed. This review article will allow practicing engineers to better understand the intrinsic and extrinsic parameters of targeted polymers before employing them in real-field scenarios for better long-term performance
The Effect of Chitosan on Plant Physiology, Wound Response, and Fruit Quality of Tomato
Authors: El Amerany Fatima , Rhazi Mohammed , Balcke Gerd , Wahbi Said , Meddich Abdelilah , Taourirte Moha , Hause Bettina
Published: November 2022

In agriculture, chitosan has become popular as a metabolic enhancer; however, no deep information has been obtained yet regarding its mechanisms on vegetative tissues. This work was conducted to test the impact of chitosan applied at different plant growth stages on plant development, physiology, and response to wounding as well as fruit shape and composition. Five concentrations of chitosan were tested on tomato. The most effective chitosan doses that increased leaf number, leaf area, plant biomass, and stomatal conductance were 0.75 and 1 mg mL−1. Chitosan (1 mg mL−1) applied as foliar spray increased the levels of jasmonoyl–isoleucine and abscisic acid in wounded roots. The application of this dose at vegetative and flowering stages increased chlorophyll fluorescence (Fv/Fm) values, whereas application at the fruit maturation stage reduced the Fv/Fm values. This decline was positively correlated with fruit shape and negatively correlated with the pH and the content of soluble sugars, lycopene, total flavonoids, and nitrogen in fruits. Moreover, the levels of primary metabolites derived from glycolysis, such as inositol phosphate, lactic acid, and ascorbic acid, increased in response to treatment of plants with 1 mg mL−1- chitosan. Thus, chitosan application affects various plant processes by influencing stomata aperture, cell division and expansion, fruit maturation, mineral assimilation, and defense responses.
Scientific Advancements in Composite Materials for Aircraft Applications: A Review
Authors: Parveez Bisma , Kittur M. I. , Badruddin Irfan Anjum , Kamangar Sarfaraz , Hussien Mohamed , Umarfarooq M. A.
Published: November 2022

Recent advances in aircraft materials and their manufacturing technologies have enabled progressive growth in innovative materials such as composites. Al-based, Mg-based, Ti-based alloys, ceramic-based, and polymer-based composites have been developed for the aerospace industry with outstanding properties. However, these materials still have some limitations such as insufficient mechanical properties, stress corrosion cracking, fretting wear, and corrosion. Subsequently, extensive studies have been conducted to develop aerospace materials that possess superior mechanical performance and are corrosion-resistant. Such materials can improve the performance as well as the life cycle cost. This review introduces the recent advancements in the development of composites for aircraft applications. Then it focuses on the studies conducted on composite materials developed for aircraft structures, followed by various fabrication techniques and then their applications in the aircraft industry. Finally, it summarizes the efforts made by the researchers so far and the challenges faced by them, followed by the future trends in aircraft materials.
Introduction: The Qur’an in History, the History of the Qur’an
Authors: Demichelis Marco
Published: November 2022

The relationship between History and the Islamic revelation has been debated in Academia over the last few decades; its complex framework has usually been investigated in aspects such as the form, the language and the facets of structure that have not always been considered as relevant in the analysis of a historical milieu [...]
Real-Time UAV Patrol Technology in Orchard Based on the Swin-T YOLOX Lightweight Model
Authors: Lan Yubin , Lin Shaoming , Du Hewen , Guo Yaqi , Deng Xiaoling
Published: November 2022

Using unmanned aerial vehicle (UAV) real-time remote sensing to monitor diseased plants or abnormal areas of orchards from a low altitude perspective can greatly improve the efficiency and response speed of the patrol in smart orchards. The purpose of this paper is to realize the intelligence of the UAV terminal and make the UAV patrol orchard in real-time. The existing lightweight object detection algorithms are usually difficult to consider both detection accuracy and processing speed. In this study, a new lightweight model named Swin-T YOLOX, which consists of the advanced detection network YOLOX and the strong backbone Swin Transformer, was proposed. Model layer pruning technology was adopted to prune the multi-layer stacked structure of the Swin Transformer. A variety of data enhancement strategies were conducted to expand the dataset in the model training stage. The lightweight Swin-T YOLOX model was deployed to the embedded platform Jetson Xavier NX to evaluate its detection capability and real-time performance of the UAV patrol mission in the orchard. The research results show that, with the help of TensorRT optimization, the proposed lightweight Swin-T YOLOX network achieved 94.0% accuracy and achieved a detection speed of 40 fps on the embedded platform (Jetson Xavier NX) for patrol orchard missions. Compared to the original YOLOX network, the model accuracy has increased by 1.9%. Compared to the original Swin-T YOLOX, the size of the proposed lightweight Swin-T YOLOX has been reduced to two-thirds, while the model accuracy has slightly increased by 0.7%. At the same time, the detection speed of the model has reached 40 fps, which can be applied to the real-time UAV patrol in the orchard.
Supervised Segmentation of NO2 Plumes from Individual Ships Using TROPOMI Satellite Data
Authors: Kurchaba Solomiia , van Vliet Jasper , Verbeek Fons J. , Meulman Jacqueline J. , Veenman Cor J.
Published: November 2022

The shipping industry is one of the strongest anthropogenic emitters of NOx—a substance harmful both to human health and the environment. The rapid growth of the industry causes societal pressure on controlling the emission levels produced by ships. All the methods currently used for ship emission monitoring are costly and require proximity to a ship, which makes global and continuous emission monitoring impossible. A promising approach is the application of remote sensing. Studies showed that some of the NO2 plumes from individual ships can visually be distinguished using the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5 Precursor (TROPOMI/S5P). To deploy a remote-sensing-based global emission monitoring system, an automated procedure for the estimation of NO2 emissions from individual ships is needed. The extremely low signal-to-noise ratio of the available data, as well as the absence of the ground truth makes the task very challenging. Here, we present a methodology for the automated segmentation of NO2 plumes produced by seagoing ships using supervised machine learning on TROPOMI/S5P data. We show that the proposed approach leads to more than a 20% increase in the average precision score in comparison to the methods used in previous studies and results in a high correlation of 0.834 with the theoretically derived ship emission proxy. This work is a crucial step towards the development of an automated procedure for global ship emission monitoring using remote sensing data.
Evaluation of the Spatial and Temporal Variations of Condensation and Desublimation over the Qinghai–Tibet Plateau Based on Penman Model Using Hourly ERA5-Land and ERA5 Reanalysis Datasets
Authors: Li Hongyuan , Chen Rensheng , Han Chuntan , Yang Yong
Published: November 2022

Condensation and desublimation are important processes of nocturnal land–atmosphere interactions, energy transfer, and the water cycle, and have important ecological and hydrological roles in mitigating physiological water deficits caused by low temperatures and reducing the risk of frost damage to plants, animals, and microorganisms near the surface in the Alpine Region. The aim of the present study is to evaluate the spatial and temporal variations of condensation and desublimation from 1950 to 2020 based on Penman model using hourly ERA5-Land and ERA5 reanalysis datasets on the Qinghai–Tibet Plateau (QTP), where condensation and desublimation occur frequently but lack quantitative evaluation. The results showed that: (1) Condensation showed a decreasing trend from southeast to northwest, with annual mean condensation ranging from 0 mm to 72.8 mm, while desublimation showed regional enrichment rather than zonal variation, with the annual mean desublimation ranging from 0 mm to 23.6 mm; (2) At 95% confidence level, condensation showed a significant increasing trend in the central and western QTP, while desublimation showed a significant decreasing trend in most regions of the QTP, and the decreasing trend of desublimation was more obvious than the increasing trend of condensation; (3) Both condensation and desublimation showed significant seasonal characteristics; the maximum monthly condensation was 2.37 mm and the monthly mean condensation was 0.70 mm, while the maximum monthly desublimation was 1.45 mm and the monthly mean desublimation was 0.95 mm; (4) The annual mean condensation was 8.45 mm, with an increasing trend of 0.24 mm/10a, the annual mean desublimation was 11.45 mm, with a decreasing trend of −0.26 mm/10a, and the total annual mean condensation and desublimation was 19.89 mm, with a weak decreasing trend on the QTP; (5) The increase in condensation is most associated with the increase in precipitation, while the decrease in desublimation is most associated with the increase in air temperature on the QTP.
Mapping of Forest Biomass in Shangri-La City Based on LiDAR Technology and Other Remote Sensing Data
Authors: Deng Yuncheng , Pan Jiya , Wang Jinliang , Liu Qianwei , Zhang Jianpeng
Published: November 2022

Forest ecosystems can be regarded as huge carbon sinks. In order to effectively assess carbon balance in such ecosystems, rapid and accurate estimation of the aboveground biomass of a forest is critically needed. However, the current methods for biomass estimation and mapping are of limited spatial resolution and mostly depend on large numbers of measurements. In order to obtain better biomass estimation outcomes with higher spatial resolution, a rapid method is introduced for region-scale biomass estimation in alpine and canyon areas using space-borne light detection and ranging (LiDAR) data and optical remote-sensing images. Specifically, we explored alpine and canyon areas in Shangri-La City in China using space-borne LiDAR data from ICESAT-2 and optical remote-sensing images from Landsat8 OLI, Sentinel-2, and Microwave remote sensing Sentinel-1. An extrapolation model of the forest canopy heights in these areas was constructed with a 30-m resolution of continuous canopy height outputs. For continuously estimating the diameter at breast height (DBH) in Shangri-La City, a tree height-DBH growth model was constructed based on the LiDAR and remote-sensing measurements. Finally, based on the average DBH of the explored forests, a model was constructed for estimating and mapping the aboveground biomass and carbon storage in Shangri-La with a spatial resolution of 30 m. The results show that the forest canopy height in Shangri-La City is mainly in the range of 2.82–30.96 m, and that the estimation accuracy is verified by the LiDAR-based canopy height model (CHM) with a coefficient of determination of R2 = 0.7143. The inversion results were still largely affected by geospatial location factors (longitude, latitude), terrain factors (slope, elevation), and vegetation indices (NBR, NDGI, NDVI). Based on the relationship between the tree height and the DBH, the DBH of trees in Shangri-La City was estimated to be mainly in the range of 20 cm to 30 cm, and this estimate was verified by actual measurements with R2 greater than 0.7 all. Finally, the established model estimated the aboveground forest biomass and carbon storage of the study area of Shangri-La City in 2020 to be 1.28 × 108 t and 6.41 × 107 t, respectively. These estimates correspond to total accuracies of 92.28%, respectively.
Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images
Authors: Iordache Marian-Daniel , De Keukelaere Liesbeth , Moelans Robrecht , Landuyt Lisa , Moshtaghi Mehrdad , Corradi Paolo , Knaeps Els
Published: November 2022

The occurrence of litter in natural areas is nowadays one of the major environmental challenges. The uncontrolled dumping of solid waste in nature not only threatens wildlife on land and in water, but also constitutes a serious threat to human health. The detection and monitoring of areas affected by litter pollution is thus of utmost importance, as it allows for the cleaning of these areas and guides public authorities in defining mitigation measures. Among the methods used to spot littered areas, aerial surveillance stands out as a valuable alternative as it allows for the detection of relatively small such regions while covering a relatively large area in a short timeframe. In this study, remotely piloted aircraft systems equipped with multispectral cameras are deployed over littered areas with the ultimate goal of obtaining classification maps based on spectral characteristics. Our approach employs classification algorithms based on random forest approaches in order to distinguish between four classes of natural land cover types and five litter classes. The obtained results show that the detection of various litter types is feasible in the proposed scenario and the employed machine learning algorithms achieve accuracies superior to 85% for all classes in test data. The study further explores sources of errors, the effect of spatial resolution on the retrieved maps and the applicability of the designed algorithm to floating litter detection.
Classification of Ground-Based Cloud Images by Contrastive Self-Supervised Learning
Authors: Lv Qi , Li Qian , Chen Kai , Lu Yao , Wang Liwen
Published: November 2022

Clouds have an enormous influence on the hydrological cycle, Earth’s radiation budget, and climate changes. Accurate automatic recognition of cloud shape based on ground-based cloud images is beneficial to analyze the atmospheric motion state and water vapor content, and then to predict weather trends and identify severe weather processes. Cloud type classification remains challenging due to the variable and diverse appearance of clouds. Deep learning-based methods have improved the feature extraction ability and the accuracy of cloud type classification, but face the problem of lack of labeled samples. In this paper, we proposed a novel classification approach of ground-based cloud images based on contrastive self-supervised learning (CSSL) to reduce the dependence on the number of labeled samples. First, data augmentation is applied to the input data to obtain augmented samples. Then contrastive self-supervised learning is used to pre-train the deep model with a contrastive loss and a momentum update-based optimization. After pre-training, a supervised fine-tuning procedure is adopted on labeled data to classify ground-based cloud images. Experimental results have confirmed the effectiveness of the proposed method. This study can provide inspiration and technical reference for the analysis and processing of other types of meteorological remote sensing data under the scenario of insufficient labeled samples.
Multi-Instrumental Approach to Slope Failure Monitoring in a Landslide Susceptible Newly Built-Up Area: Topo-Geodetic Survey, UAV 3D Modelling and Ground-Penetrating Radar
Authors: Sestras Paul , Bilașco Ștefan , Roșca Sanda , Veres Ioel , Ilies Nicoleta , Hysa Artan , Spalević Velibor , Cîmpeanu Sorin M.
Published: November 2022

Slope failures and landslides cause economic damage and deaths worldwide. These losses can be minimized by integrating different methodologies, instruments, and data monitoring to predict future landslides. In the constantly growing metropolitan area of Cluj-Napoca, Romania, changes in land cover, land use, and build-up areas are an issue. The unprecedented urban sprawl pushed the city limits from the Somes River to hilly terrain prone to landslides and erosion. This study focuses on a landslide-prone area where a previous slope failure caused significant economic losses. It combines topo-geodetic measurements, UAV monitoring of surface displacement, GIS spatial analysis, ground-penetrating radar investigations, and geotechnical assessment. Two years of data show that the slope is undergoing surface erosion, with soil displacements of a few centimeters. Geodetic monitoring of the retaining wall’s control points indicates a small rotation. Coupled with georadar profile imaging showing changes in soil and rock layers with an uplift trend, it was deduced that the area suffers from a global instability. The findings provide valuable information about the dynamics of landslides and erosion for forecasting future movements and developing preventative strategies based on a new methodology that combines affordable and prevalent instrumentation and techniques.
Improvement of Lithological Mapping Using Discrete Wavelet Transformation from Sentinel-1 SAR Data
Authors: Guo Senmiao , Yang Changbao , He Rizheng , Li Yanqi
Published: November 2022

Lithological mapping using dual-polarization synthetic aperture radar (SAR) data is limited by the low classification accuracy. In this study, we extract ten parameters (backscatter coefficients and polarization decomposition parameters) from the Sentinel-1 dual-pol SAR data. Using 94 mother wavelet functions (MF), a one-level two-dimensional discrete wavelet transform (DWT) is applied to all the parameters, and the suitable MF is screened by comparing the overall accuracy and F1 score. Finally, the lithological mapping of the study area is performed. According to the cross-validation results, DWT can improve the overall accuracy for all MF. Db13 improved the overall accuracy by 6.1% (from 49.5% to 55.6%). The F1 score of granitoids improved by 0.223. Among the five rock units, Grantoids and Quaternary alluvium and sediment with finer gravel can be better differentiated than the other three rock units. The overall accuracy of effusive rocks (marine basic volcanic rocks) is not improved by DWT, but this study confirms the great potential of DWT in lithology classification.
Targeted Universal Adversarial Examples for Remote Sensing
Authors: Bai Tao , Wang Hao , Wen Bihan
Published: November 2022

Researchers are focusing on the vulnerabilities of deep learning models for remote sensing; various attack methods have been proposed, including universal adversarial examples. Existing universal adversarial examples, however, are only designed to fool deep learning models rather than target specific goals, i.e., targeted attacks. To this end, we propose two variants of universal adversarial examples called targeted universal adversarial examples and source-targeted universal adversarial examples. Extensive experiments on three popular datasets showed strong attackability of the two targeted adversarial variants. We hope such strong attacks can inspire and motivate research on the defenses against adversarial examples in remote sensing.
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting
Authors: Grigoryev Timofey , Verezemskaya Polina , Krinitskiy Mikhail , Anikin Nikita , Gavrikov Alexander , Trofimov Ilya , Balabin Nikita , Shpilman Aleksei , Eremchenko Andrei , Gulev Sergey , Burnaev Evgeny , Vanovskiy Vladimir
Published: November 2022

Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data needed for marine operations. In this article, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin, and we can improve the model’s quality by using additional weather data and training on multiple regions to ensure its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions.
Remote Sensing Image Scene Classification with Self-Supervised Learning Based on Partially Unlabeled Datasets
Authors: Chen Xiliang , Zhu Guobin , Liu Mingqing
Published: November 2022

In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcrafted labeled datasets, which leads to a high cost of obtaining annotated samples. Self-supervised learning can alleviate this problem by using unlabeled data to learn the image’s feature representation and then migrate to the downstream task. In this study, we use an encoder–decoder structure to construct a self-supervised learning architecture. In the encoding stage, the image mask is used to discard some of the image patches randomly, and the image’s feature representation can be learned from the remaining image patches. In the decoding stage, the lightweight decoder is used to recover the pixels of the original image patches according to the features learned in the encoding stage. We constructed a large-scale unlabeled training set using several public scene classification datasets and Gaofen-2 satellite data to train the self-supervised learning model. In the downstream task, we use the encoder structure with the masked image patches that have been removed as the backbone network of the scene classification task. Then, we fine-tune the pre-trained weights of self-supervised learning in the encoding stage on two open datasets with complex scene categories. The datasets include NWPU-RESISC45 and AID. Compared with other mainstream supervised learning methods and self-supervised learning methods, our proposed method has better performance than the most state-of-the-art methods in the task of remote sensing image scene classification.
A W-Band Active Phased Array Miniaturized Scan-SAR with High Resolution on Multi-Rotor UAVs
Authors: Ding Manlai , Wang Xuemei , Tang Li , Qu Jiameng , Wang Yinshen , Zhou Liangjiang , Wang Bingnan
Published: November 2022

The smart unmanned aerial vehicle (UAV) with a mini-SAR payload provides an advanced earth observation capability for target detection and imaging. Simiar to large-scale SAR, mini-SAR also has an increasing requirement for high resolution and wide swath. However, due to the low cruising altitude of UAVs, small coverage angles in the direction range, and the insufficient operating range of mini-SAR, expanding the swath has become an urgent problem for mini-SAR. To solve this problem, this paper proposes a W-Band active phased array miniaturized SAR (APA mini-SAR), whose scanning capacity has been proven on the multi-rotor UAV platform. Many efforts, including the novel active phased array antenna scheme, the sparse triangular arrangement of antenna elements, the wideband chirp source, and the three-dimensional integration technology, have been made to develop this APA mini-SAR for the first time in the W-Band. The volume of this APA mini-SAR is 69 × 82 × 87 mm3, with a weight of 600 g. Combined with a new motion compensation strategy and the ω-k imaging algorithm, the focused image is finally obtained. Experiments have been conducted, and the results indicate that this APA mini-SAR has a resolution of 4.5 cm, the imaging swath is three times that of the traditional single-channel mini-SAR, and the operating range is increased to 800 m.
Evaluation and Comparison of Spatio-Temporal Relationship between Multiple Satellite Aerosol Optical Depth (AOD) and Near-Surface PM2.5 Concentration over China
Authors: Xu Qiangqiang , Chen Xiaoling , Rupakheti Dipesh , Dong Jiadan , Tang Linling , Kang Shichang
Published: November 2022

Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM2.5. However, the spatiotemporal relationship between AODs and PM2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM2.5 concentration across China and its 14 representative regions during 2016–2018 using the correlation coefficient (R), the PM2.5/AOD ratio (η), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM2.5 (R > 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R > 0.5) between the PM2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM2.5 per unit of these AODs (η > 130). The MAIAC AOD (Terra/Aqua) can better explain the spatial distribution (q > 0.4) of PM2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R > 0.5, q > 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM2.5 concentration based on AOD in different regions of China.