bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Electrical Failure of Polyimide-Based Flexible Neural Implants: The Role of Thin Film Adhesion
Authors: Tintelott Marcel , Schander Andreas , Lang Walter
Published: September 2022

The lack of long-term stability of polymeric neural interfaces remains one of the most important and less tackled issues in this research field. To address this issue, we fabricated two test structures based on interdigitated electrodes (IDEs) encapsulated with polyimide (PI). One of the test samples was pretreated with barrel oxygen plasma prior to spin coating of the second PI layer. The second test structure was pretreated using a reactive ion etching (RIE) process. The test samples were immersed in an electrolyte solution at elevated temperatures to mimic the conditions inside the human brain. The samples were then electrically and mechanically stressed to accelerate their degradation. Real-time monitoring of the electrical insulation stability was used to compare the impact of the pretreatment on the long-term stability. Barrel-plasma-activated test samples showed a mean lifetime of 1.5 days, whereas RIE pretreatment increased the mean lifetime to 24 days. Therefore, RIE-pretreated test samples exhibited 16 times longer mean stability compared to purely chemically activated test samples. Furthermore, the electrical measurements were correlated with mechanical adhesion tests. Chemically activated test samples showed significant delamination, whereas RIE pretreatment enhanced the adhesion, and no delamination could be observed. The correlation of these investigations suggests that the adhesion between different layers is higher following RIE pretreatment compared to pretreatment with chemical barrel plasma. In conclusion, the adhesion between the two PI foils seems to play a key role in the long-term stability of such devices.
Structural and Mechanical Properties of Konjac Glucomannan Gels and Influence of Freezing-Thawing Treatments on Them
Authors: Takeno Hiroyuki , Hashimoto Ryuki , Lu Yunqiao , Hsieh Wen-Chuan
Published: September 2022

Freezing has been widely used for long-term food preservation. However, freezing-thawing (FT) treatment usually influences the texture and structure of food gels such as konjac. For their texture control after FT treatment, it is important to clarify the structural change of food gels during the FT process. In this study, we investigated the aggregated structures of konjac glucomannan (GM) gels during the FT process using simultaneous synchrotron small-angle X-ray/wide-angle X-ray scattering (SAXS/WAXS) techniques. The FT treatment resulted in more crystallization of GM, and consequently, a large increase in compressive stress. In-situ SAXS/WAXS measurements revealed the following findings: on freezing, water molecules came out of the aggregated phase of GM and after the thawing, they came back into the aggregated phase, but the aggregated structure did not return to the one before the freezing; the gel network enhanced the inhomogeneity due to the growth of ice crystals during freezing. Furthermore, we examined the influence of additives such as polyvinyl (alcohol) (PVA) and antifreeze glycoprotein (AFGP) on the mechanical and structural properties of freeze-thawed GM gels. Although the addition of PVA and AFGP suppressed the crystallization of GM, it could not prevent the growth of ice crystals and the increase in the inhomogeneity of the gel network. As a result, the compressive stresses for freeze-thawed GM gels containing PVA or AFGP were significantly higher compared with those of GM gels without FT treatments, although they were lower than those of freeze-thawed GM gels. The findings of this study may be useful for not only the texture control of freeze-thawed foods but also the improvement of the mechanical performance of the biomaterials.
Optimization of Polyacrylic Acid Coating on Graphene Oxide-Functionalized Reverse-Osmosis Membrane Using UV Radiation through Response Surface Methodology
Authors: Ashfaq Mohammad Yousaf , Al-Ghouti Mohammad A.
Published: September 2022

Reverse osmosis (RO) is affected by multiple types of fouling such as biofouling, scaling, and organic fouling. Therefore, a multi-functional membrane capable of reducing more than one type of fouling is a need of the hour. The polyacrylic acid and graphene oxide (PAA-GO) nanocomposite functionalization of the RO membrane has shown its effectiveness against both mineral scaling and biofouling. In this research, the polyacrylic acid concentration and irradiation times were optimized for the PAA-GO-coated RO membrane using the response surface methodology (RSM) approach. The effect of these parameters on pure water permeability and salt rejection was investigated. The models were developed through the design of the experiment (DoE), which were further validated through the analysis of variance (ANOVA). The optimum conditions were found to be: 11.41 mg·L−1 (acrylic acid concentration) and 28.08 min (UV activation times) with the predicted results of 2.12 LMH·bar−1 and 98.5% NaCl rejection. The optimized membrane was prepared as per the model conditions, which showed an increase in both pure water permeability and salt rejection as compared to the control. The improvement in membrane surface smoothness and hydrophilicity for the optimized membrane also helped to inhibit mineral scaling by 98%.
Design of Arbitrary Magnetic Patterns on Magnetic Polymer Composite Objects: A Finite Element Modelling Tool
Authors: González-Losada Pedro , Martins Marco , Paz Elvira , Vinayakumar K. B. , Pereira Domingos , Cortez Ana R. , Aguiam Diogo Elói
Published: September 2022

Magnetic sensor systems integrate a sensing element and magnetic field generators to determine their relative position or to measure movement. Typically, the magnetic fields are produced by permanent magnets, which have high intensity but are hard to machine into custom shapes. However, novel solutions using magnetic polymer composites (MPCs) have emerged as field generators due to their low cost, weight and patterning freedom. Here, we present a finite element model developed in COMSOL Multiphysics that allows the design of complex magnetization patterns on these polymer composites, taking into account the geometries of the parts and the magnetic properties of the materials employed. The model, together with the characterization protocol of the materials, has proved to be capable of predicting the magnetization of polymer composites at different temperatures. In addition, the model incorporates the properties of the magnets used during the magnetization process, such as the size, shape and magnetization, as well as the properties of the surrounding elements. This new model facilitates the design of new polymeric parts with complex shapes and magnetization patterns that can be employed as field generators in magnetic sensing systems.
Preparation and Properties of Asymmetric Polyvinyl Pyrroli-Done/Polycaprolactone Composite Nanofiber Loaded with Tea Tree Extract
Authors: Xu Yu , Xie Lei , Hou Tingting , Wang Di , Zhang Tong , Li Chengpeng
Published: September 2022

To develop a novel asymmetric nanofiber membrane with antioxidant and antibacterial activities, biocompatible polyvinyl pyrrolidone (PVP) and polycaprolactone (PCL) were used as carriers to load water-soluble tea tree extract (TTE) and hydrophobic tea tree oil (TTO) via electrospinning and electrostatic spraying, respectively, which was named as TTE-PVP-3/TTO-PCL. The results show that uniform TTE-PVP nanofibers with an average diameter of 95 ± 27 nm could be obtained when the mass ratio of TTE to PVP was set as 1:3. Homogeneous TTO/PCL microspheres with an average size of 4.38 ± 0.79 µm could be obtained when the propulsion speed was 0.08 mm/min and the voltage was 10 KV. The activity study showed that TTE could only improve the antioxidant activity of TTE-PVP-3/TTO-PCL, while TTO could improve the antibacterial activity effectively. Under experimental conditions, the inhibition zones of TTE-PVP-3/TTO-PCL against Staphylococcus aureus and Escherichia coli were 7.50 ± 0.48 mm and 9.55 ± 0.45 mm, respectively, and its scavenging rates for 2,2-diphenylpicrylhydrazyl (DHPP) and hydroxyl radical were 59.79 ± 4.10% and 61.02 ± 4.95%, respectively. In conclusions, TTE-PVP-3/TTO PCL can be potentially used as a new kind of anti-oxidative and antibacterial wound dressings.
Super-Hydrophobic Magnetic Fly Ash Coated Polydimethylsiloxane (MFA@PDMS) Sponge as an Absorbent for Rapid and Efficient Oil/Water Separation
Authors: Zhao Mengqi , Ma Xiaoqing , Chao Yuxi , Chen Dejun , Liao Yinnian
Published: September 2022

In this study, magnetic fly ash was prepared with fly ash and nano-magnetic Fe3O4, obtained by co-precipitation. Then, a magnetic fly ash/polydimethylsiloxane (MFA@PDMS) sponge was prepared via simple dip-coating PDMS containing ethanol in magnetic fly ash aqueous suspension and solidifying, whereby Fe3O4 played a vital role in achieving the uniformity of the FA particle coating on the skeletons of the sponge. The presence of the PDMS matrix made the sponge super-hydrophobic with significant lubricating oil absorption capacity; notably, it took only 10 min for the material to adsorb six times its own weight of n-hexane (oil phase). Moreover, the MFA@PDMS sponge demonstrated outstanding recyclability and stability, since no decline in absorption efficiency was observed after more than eight cycles. Furthermore, the stress–strain curves of 20 compression cycles presented good overlap, i.e., the maximum stress was basically unchanged, and the sponge was restored to its original shape, indicating that it had good mechanical properties, elasticity, and fatigue resistance.
Structural, Thermal, and Electrical Properties of Poly(Ethylene Oxide)—Tetramethyl Succinonitrile Blend for Redox Mediators
Authors: Gupta Ravindra Kumar , Shaikh Hamid , Imran Ahamad , Bedja Idriss , Aldwayyan Abdullah Saleh
Published: September 2022

An all-solid–state dye-sensitized solar cell is one of the non-fossil fuel-based electrochemical devices for electricity generation in a high-temperature region. This device utilizes a redox mediator, which is a fast ion-conducting solid polymer electrolyte (SPE). The SPE makes the device economical, thinner, and safer in high-temperature regions. The SPE generally has a form of matrix−plasticizer−redox salts. Succinonitrile (SN) is generally employed as a plasticizer for reducing the crystallinity of poly(ethylene oxide), abbreviated as PEO, a common polymeric matrix. In the present paper, the structural and thermal properties of tetramethyl succinonitrile (TMSN) were compared with SN for its application as a solid plasticizer. TMSN and SN both are plastic crystals. TMSN has four methyl groups by replacing the hydrogen of the SN, resulting in higher molecular weight, solid–solid phase transition temperature, and melting temperature. We thoroughly studied the structural, thermal, and electrical properties of the [(1−x)PEO: xTMSN] blend for utilizing it as a matrix, where x = 0–0.25 in mole fraction. The FT-IR spectra and XRD patterns of the blends exhibited PEO-alike up to x = 0.15 mole and TMSN-alike for x > 0.15 mole. Differential scanning calorimetry revealed formation of a eutectic phase from x = 0.1 mole and phase separation from x = 0.15 mole. The blends with x = 0.1–0.15 mole had a low value of PEO crystallinity. Thermogravimetric analysis showed thermal stability of the blends up to 75 °C. The blends exhibited electrical conductivity, σ25°C more than 10−9 S cm−1, and Arrhenius behavior (activation energy, ~0.8 eV) in a temperature region, 25–50 °C.
Factors Affecting Mechanical Properties of Reinforced Bioplastics: A Review
Authors: Boey Jet Yin , Lee Chee Keong , Tay Guan Seng
Published: September 2022

The short life cycle and recalcitrant nature of petroleum-based plastics have been associated with plastic waste accumulation due to their composition rather than worldwide overproduction. The drive to replace single-use products has sparked a considerable amount of research work to discover sustainable options for petroleum-based plastics. Bioplastics open up a new horizon in plastics manufacturing operations and industrial sectors because of their low environmental impact, superior biodegradability, and contribution to sustainable goals. Their mechanical properties regarding tensile, flexural, hardness, and impact strength vary substantially. Various attempts have been made to augment their mechanical characteristics and capacities by incorporating reinforcement materials, such as inorganic and lignocellulosic fibres. This review summarizes the research on the properties of bioplastics modified by fibre reinforcement, with a focus on mechanical performance. The mechanical properties of reinforced bioplastics are significantly driven by parameters such as filler type, filler percentage, and aspect ratio. Fibre treatment aims to promote fibre–matrix adhesion by changing their physical, chemical, thermal, and mechanical properties. A general overview of how different filler treatments affect the mechanical properties of the composite is also presented. Lastly, the application of natural fibre-reinforced bioplastics in the automobile, construction, and packaging industries is discussed.
3D Printing of Triamcinolone Acetonide in Triblock Copolymers of Styrene–Isobutylene–Styrene as a Slow-Release System
Authors: Hilgeroth Philipp S. , Thümmler Justus F. , Binder Wolfgang H.
Published: September 2022

Additive manufacturing has a wide range of applications and has opened up new methods of drug formulation, in turn achieving attention in medicine. We prepared styrene–isobutylene–styrene triblock copolymers (SIBS; Mn = 10 kDa–25 kDa, PDI 1,3–1,6) as a drug carrier for triamcinolone acetonide (TA), further processed by fused deposition modeling to create a solid drug release system displaying improved bioavailability and applicability. Living carbocationic polymerization was used to exert control over block length and polymeric architecture. Thermorheological properties of the SIBS polymer (22.3 kDa, 38 wt % S) were adjusted to the printability of SIBS/TA mixtures (1–5% of TA), generating an effective release system effective for more than 60 days. Continuous drug release and morphological investigations were conducted to probe the influence of the 3D printing process on the drug release, enabling 3D printing as a formulation method for a slow-release system of Triamcinolone.
Curaua–Aramid Hybrid Laminated Composites for Impact Applications: Flexural, Charpy Impact and Elastic Properties
Authors: Meliande Natalin , Oliveira Michelle , Silveira Pedro , Dias Rafael , Marçal Rubens , Monteiro Sergio , Nascimento Lucio
Published: September 2022

Curaua, as a leaf-based natural fiber, appears to be a promising component with aramid fabric reinforcement of hybrid composites. This work deals with the investigation of flexural, impact and elastic properties of non-woven curaua–aramid fabric hybrid epoxy composites. Five configurations of hybrid composites in a curaua non-woven mat with an increasing quantity of layers, up to four layers, were laminated through the conventional hand lay-up method. The proposed configurations were idealized with at least 60 wt% reinforcement in the non-alternating configuration. As a result, it was observed that the flexural strength decreased by 33% and the flexural modulus by 56%. In addition, the energy absorbed in the Charpy impact also decreased in the same proportion as the replaced amount of aramid. Through the impulse excitation technique, it was possible observe that the replacement of the aramid layers with the curaua layers resulted in decreased elastic properties. However, reduction maps revealed proportional advantages in hybridizing the curaua with the aramid fiber. Moreover, the hybrid composite produced an almost continuous and homogeneous material, reducing the possibility of delamination and transverse deformation, which revealed an impact-resistant performance.
Matrix Regeneration Ability In Situ Induced by a Silk Fibroin Small-Caliber Artificial Blood Vessel In Vivo
Authors: Li Helei , Dai Mengnan , Li Meng , Meng Lingpeng , Yu Yangxiao , Xu Jianmei , Dong Fenglin , Fan Qingmin , Yin Yin , Wang Aiqing , Wang Jiannan
Published: September 2022

The success of a small-caliber artificial vascular graft in the host in order to obtain functional tissue regeneration and remodeling remains a great challenge in clinical application. In our previous work, a silk-based, small-caliber tubular scaffold (SFTS) showed excellent mechanical properties, long-term patency and rapid endothelialization capabilities. On this basis, the aim of the present study was to evaluate the vascular reconstruction process after implantation to replace the common carotid artery in rabbits. The new tissue on both sides of the SFTSs at 1 month was clearly observed. Inside the SFTSs, the extracellular matrix (ECM) was deposited on the pore wall at 1 month and continued to increase during the follow-up period. The self-assembled collagen fibers and elastic fibers were clearly visible in a circumferential arrangement at 6 months and were similar to autologous blood vessels. The positive expression rate of Lysyl oxidase-1 (LOXL-1) was positively correlated with the formation and maturity of collagen fibers and elastic fibers. In summary, the findings of the tissue regeneration processes indicated that the bionic SFTSs induced in situ angiogenesis in defects.
Identification and Characterization of Fibronectin-Binding Peptides in Gelatin
Authors: Liu Yuying , Gao Jianping , Liu Lin , Kang Jiyao , Luo Xi , Kong Yingjun , Zhang Guifeng
Published: September 2022

Collagen and fibronectin (FN) are important components in the extracellular matrix (ECM). Collagen-FN binding belongs to protein-protein interaction and plays a key role in regulating cell behaviors. In this study, FN-binding peptides were isolated from gelatin (degraded collagen) using affinity chromatography, and the amino acid sequences were determined using HPLC-MS. The results indicated that all FN-binding peptides contained GPAG or GPPG. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and dual-polarization interferometry (DPI) were used to analyze the effects of hydroxylation polypeptide on FN binding activity. DPI analysis indicated that peptides with molecular weight (MW) between 2 kDa and 30 kDa showed higher FN-binding activity, indicating MW range played an important role in the interaction between FN and peptides. Finally, two peptides with similar sequences except for hydroxylation of prolines were synthesized. The FN-binding properties of the synthesized peptides were determined by MALDI-TOF MS. For peptide, GAPGADGP*AGAPGTP*GPQGIAGQR, hydroxylation of P8 and P15 is necessary for FN-binding. For peptide, GPPGPMGPPGLAGPPGESGR, the FN-binding process is independent of proline hydroxylation. Thus, FN-binding properties are proline-hydroxylation dependent.
Effect of Diatomite on the Thermal Degradation Behavior of Polypropylene and Formation of Graphene Products
Authors: Chen Yankun , Wang Biao
Published: September 2022

In this work, the thermogravimetry–Fourier transform infrared spectroscopy (TG–FTIR) and gas chromatography–mass spectrometry (GC–MS) techniques are used to investigate the thermal degradation behavior of polypropylene (PP) with 20 wt.% diatomite (DM). The initial decomposition temperature of these blends was 17 °C lower than that of pristine PP, and more olefin degradation products were formed during the pyrolysis process under Ar atmosphere. These results could be attributed to the catalytic effects of DM on the degradation of PP and the changes of PP chain scission pathways around the particles (more β scission happened via the secondary radical transfer). These olefins could be caught by DM through the Si–O–C bond formed during the heat–treatment around 400~500 °C. The formation of the cross–linked structure could facilitate the growth of graphene during a high–temperature graphitization process.
Influence of Incorporating 5% Weight Titanium Oxide Nanoparticles on Flexural Strength, Micro-Hardness, Surface Roughness and Water Sorption of Dental Self-Cured Acrylic Resin
Authors: Abdelraouf Rasha M. , Bayoumi Rania E. , Hamdy Tamer M.
Published: September 2022

Background: Polymethyl methacrylate (PMMA) is used in fabricating acrylic denture bases. Repairing a fractured acrylic denture base can be done by self-cured PMMA, yet this is still a weak point after repair. The aim of this study was to evaluate the effect of incorporating 5% weight titanium oxide nanoparticles (TiO2) to self-cured PMMA on flexural strength, surface micro-hardness, roughness, and water sorption. Methods: A total of 160 acrylic–resin specimens were used in this study. They were divided in two main groups; (a) control group, prepared by mixing self-cured PMMA powder to its liquid monomer, (b) treated group, prepared by blending 5% weight TiO2 nanoparticles to self-cured PMMA powder then this blend was mixed with the liquid monomer. Flexure strength, surface micro-hardness, roughness, and water sorption were evaluated. Data were analyzed using independent sample t-tests (p ≤ 0.05). Results: There was a significant increase in the flexural strength of PMMA of the treated group after the addition of TiO2 (137.6 MPa) compared with the control (75.4 MPa) (p ≤ 0.001). No significant difference between the two groups in terms of micro-hardness (p = 0.385) and surface roughness (p = 0.269). Water sorption showed a significant reduction in the treated group (p ≤ 0.001). Conclusions: Addition of 5% weight TiO2 nanoparticles to the self-cured acrylic resin improved its flexural strength and reduced its water-sorption without impairing the surface micro-hardness and roughness.
Factors Influencing Australian Muslims’ Attitudes toward Christian-Muslim Dialogue: The Case of Sunni Muslims of Adelaide and Uniting Church Christians
Authors: MacDonald Gregory , Abdalla Mohamad , Kabir Nahid Afrose
Published: September 2022

Since the implementation of a multicultural policy in the 1970s, religious diversity in Australia has increased. Research has demonstrated that intergroup contact is essential for managing diverse multicultural societies. This is because, given the right conditions, intergroup contact will reduce prejudice and build trust between groups. Given the importance of intergroup contact, policy makers and researchers have identified interfaith dialogue’s importance to the success of multicultural societies. However, there is very limited research that explores interfaith dialogue from the perspectives of adherents, in this case Christians and Muslims in the Australian context. This paper focuses on interfaith dialogue between Christians of the Uniting Church and Sunni Muslims of Adelaide, South Australia. It explores the factors that influence participants’ attitudes towards engaging in interfaith dialogue. Using a grounded theory methodology, the study involved seventeen (17) mixed gender Muslim participants over the age of eighteen, including everyday adherents and religious leaders. Some of the key findings demonstrate that theological perspectives and notions of multicultural citizenship are positive drivers for dialogue; Islamophobia and dehumanization of Muslims were inhibitors.
Reference Data Accuracy Impacts Burned Area Product Validation: The Role of the Expert Analyst
Authors: Franquesa Magí , Rodriguez-Montellano Armando M. , Chuvieco Emilio , Aguado Inmaculada
Published: September 2022

Accurate reference data to validate burned area (BA) products are crucial to obtaining reliable accuracy metrics for such products. However, the accuracy of reference data can be affected by numerous factors; hence, we can expect some degree of deviation with respect to real ground conditions. Since reference data are usually produced by semi-automatic methods, where human-based image interpretation is an important part of the process, in this study, we analyze the impact of the interpreter on the accuracy of the reference data. Here, we compare the accuracy metrics of the FireCCI51 BA product obtained from reference datasets that were produced by different analysts over 60 sites located in tropical regions of South America. Additionally, fire severity, tree cover percentage, and canopy height were selected as explanatory sources of discrepancies between interpreters’ reference BA classifications. We found significant differences between the FireCCI51 accuracy metrics obtained with the different reference datasets. The highest accuracies (highest Dice coefficient) were obtained with the reference dataset produced by the most experienced interpreter. The results indicated that fire severity is the main source of discrepancy between interpreters. Disagreement between interpreters was more likely to occur in areas with low fire severity. We conclude that the training and experience of the interpreter play a crucial role in guaranteeing the quality of the reference data.
Identification and Area Information Extraction of Oat Pasture Based on GEE—A Case Study in the Shandan Racecourse (China)
Authors: Wang Ruijing , Feng Qisheng , Jin Zheren , Ma Kexin , Zhang Zhongxue , Liang Tiangang
Published: September 2022

Forage grass is very important for food security. The development of artificial grassland is the key to solving the shortage of forage grass. Understanding the spatial distribution of forage grass in alpine regions is of great importance for guiding animal husbandry and the rational selection of forage grass management measures. With its powerful computing power and complete image data storage, Google Earth Engine (GEE) has become a new method to address remote sensing data collection difficulties and low processing efficiency. High-resolution mapping of pasture distributions on the Tibetan Plateau (China) is still a difficult problem due to cloud disturbance and mixed planting of forage grass. Based on the GEE platform, Sentinel-2 data and three classifiers, this study successfully mapped the oat pasture area of the Shandan Racecourse (China) on the eastern Tibetan Plateau over 3 years from 2019 to 2021 at a resolution of 10 m based on cultivated land identification. In this study, the key phenology windows were determined by analysing the time series differences in vegetation indices between oat pasture and other forage grasses in the Shandan Racecourse, and monthly scale features were selected as features for oat pasture identification. The results show that the mean Overall Accuracy (OA) of Random Forest (RF) classifier, Support Vector Machine (SVM) classifier, and Classification and Regression Trees (CART) classifier are 0.80, 0.69, and 0.72 in cultivated land identification, respectively, with corresponding the Kappa coefficients of 0.74, 0.58, and 0.62. The RF classifier far outperforms the other two classifiers. In oat pasture identification, the RF, SVM and CART classifiers have high OAs of 0.98, 0.97, and 0.97 and high Kappa values of 0.95, 0.94, and 0.95, respectively. Overall, the RF classifier is more suitable for our research. The oat pasture areas in 2019, 2020 and 2021 were 347.77 km2 (15.87%), 306.19 km2 (13.97%) and 318.94 km2 (14.55%), respectively, with little change (1.9%) from year to year. The purpose of this study was to explore the identification model of forage grass area in alpine regions with a high spatial resolution, and to provide technical and methodological support for information extraction of the forage grass distribution status on the Tibetan Plateau.
Remote Sensing for Land Administration 2.0
Authors: Koeva Mila , Bennett Rohan , Persello Claudio
Published: September 2022

Contemporary land administration (LA) systems incorporate the concepts of cadastre and land registration. Conceptually, LA is part of a global land management paradigm incorporating LA functions such as land value, land tenure, land development, and land use. The implementation of land-related policies integrated with well-maintained spatial information reflects the aim set by the United Nations to deliver tenure security for all (Sustainable Development Goal target 1.4, amongst many others). Innovative methods for data acquisition, processing, and maintaining spatial information are needed in response to the global challenges of urbanization and complex urban infrastructure. Current technological developments in remote sensing and geo-spatial information science provide enormous opportunities in this respect. Over the past decade, the increasing usage of unmanned aerial vehicles (UAVs), satellite and airborne-based acquisitions, as well as active remote sensing sensors such as LiDAR, resulted in high spatial, spectral, radiometric, and temporal resolution data. Moreover, significant progress has also been achieved in automatic image orientation, surface reconstruction, scene analysis, change detection, classification, and automatic feature extraction with the help of artificial intelligence, spatial statistics, and machine learning. These technology developments, applied to LA, are now being actively demonstrated, piloted, and scaled. This Special Issue hosts papers focusing on the usage and integration of emerging remote sensing techniques and their potential contribution to the LA domain.
Retrieval of Live Fuel Moisture Content Based on Multi-Source Remote Sensing Data and Ensemble Deep Learning Model
Authors: Xie Jiangjian , Qi Tao , Hu Wanjun , Huang Huaguo , Chen Beibei , Zhang Junguo
Published: September 2022

Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and fire spread rate. In order to further improve the retrieval accuracy, two ensemble models combining deep learning models were proposed. One is a stacking ensemble model based on LSTM, TCN and LSTM-TCN models, and the other is an Adaboost ensemble model based on the LSTM-TCN model. Measured LFMC data, MODIS, Landsat-8, Sentinel-1 remote sensing data and auxiliary data such as canopy height and land cover of the forest-fire-prone areas in the Western United States, were selected for our study, and the retrieval results of different models with different groups of remote sensing data were compared. The results show that using multi-source data can integrate the advantages of different types of remote sensing data, resulting in higher accuracy of LFMC retrieval than that of single-source remote sensing data. The ensemble models can better extract the nonlinear relationship between LFMC and remote sensing data, and the stacking ensemble model with all the MODIS, Landsat-8 and Sentinel-1 remote sensing data achieved the best LFMC retrieval results, with R2  = 0.85, RMSE = 18.88 and ubRMSE = 17.99. The proposed stacking ensemble model is more suitable for LFMC retrieval than the existing method.
Evaluation of the Mid-Latitude Ionospheric trough Using GRACE Data
Authors: Lubyk Kateryna , Hoque Mohammed Mainul , Stolle Claudia
Published: September 2022

In this work, statistical analysis of the main ionospheric trough was performed using K-band ranging system (KBR) measurements from the GRACE satellite constellation from 2002 to 2015. The investigated period covers high- and low-solar-activity conditions. The mid-latitude ionospheric trough (MIT) characteristics were investigated for both the northern and southern hemispheres. MIT parameters such as the minimum trough position, trough width and depth, and trough occurrence probability were studied in connection with magnetic local time; geographic distribution; seasons; solar activity; and geomagnetic activity conditions, such as the solar wind plasma speed, interplanetary magnetic field components, and high-resolution geomagnetic indices SYM–H and Hp30.
The Change Pattern and Its Dominant Driving Factors of Wetlands in the Yellow River Delta Based on Sentinel-2 Images
Authors: Wei Cuixia , Guo Bing , Fan Yewen , Zang Wenqian , Ji Jianwan
Published: September 2022

There were significant differences in the dominant driving factors of the change process of different types of wetlands in the Yellow River delta. In addition, to our knowledge, the optimal classification feature sets with the Random Forest algorithm for wetlands in the Yellow River delta were least explored. In this paper, the wetland information in the study area was extracted based on a Random Forest algorithm with de-feature variable redundancy, and then the change process of wetland and its dominant factors from 2015 to 2021 was monitored and analyzed using the Geodetector and gravity center model. The results showed that (1) the optimal variable sets composed of red edge indexes based on the Random Forest algorithm had the highest classification accuracy, with the overall accuracy and Kappa coefficient of 95.75% and 0.93. (2) During 2015–2021, a large area of natural wetland in the Yellow River delta was transformed into an artificial wetland. The wetlands showed an overall development direction of “northwest–southeast” along the Yellow River. (3) The interaction between vegetation coverage and accumulated temperature had the largest explanatory power of the change in the natural wetland area. The interaction between solar radiation and DEM had the largest explanatory power for the change in the artificial wetland area. The research results could better provide decisions for wetland protection and restoration in the Yellow River delta.
A Method with Adaptive Graphs to Constrain Multi-View Subspace Clustering of Geospatial Big Data from Multiple Sources
Authors: Liu Qiliang , Huan Weihua , Deng Min
Published: September 2022

Clustering of multi-source geospatial big data provides opportunities to comprehensively describe urban structures. Most existing studies focus only on the clustering of a single type of geospatial big data, which leads to biased results. Although multi-view subspace clustering methods are advantageous for fusing multi-source geospatial big data, exploiting a robust shared subspace in high-dimensional, non-uniform, and noisy geospatial big data remains a challenge. Therefore, we developed a method with adaptive graphs to constrain multi-view subspace clustering of multi-source geospatial big data (agc2msc). First, for each type of data, high-dimensional and noisy original features were projected into a low-dimensional latent representation using autoencoder networks. Then, adaptive graph constraints were used to fuse the latent representations of multi-source data into a shared subspace representation, which preserved the neighboring relationships of data points. Finally, the shared subspace representation was used to obtain the clustering results by employing a spectral clustering algorithm. Experiments on four benchmark datasets showed that agc2msc outperformed nine state-of-the-art methods. agc2msc was applied to infer urban land use types in Beijing using the taxi GPS trajectory, bus smart card transaction, and points of interest datasets. The clustering results may provide useful calibration and reference for urban planning.
Remote Sensing Image Information Quality Evaluation via Node Entropy for Efficient Classification
Authors: Yang Jiachen , Yang Yue , Wen Jiabao , Li Yang , Ercisli Sezai
Published: September 2022

Combining remote sensing images with deep learning algorithms plays an important role in wide applications. However, it is difficult to have large-scale labeled datasets for remote sensing images because of acquisition conditions and costs. How to use the limited acquisition budget to obtaina better remote sensing image dataset is a problem worth studying. In response to this problem, this paper proposes a remote sensing image quality evaluation method based on node entropy, which can be combined with active learning to provide low-cost guidance for remote sensing image collection and labeling. The method includes a node selection module and a remote sensing image quality evaluation module. The function of the node selection module is to select representative images, and the remote sensing image quality evaluation module evaluates the remote sensing image information quality by calculating the node entropy of the images. The image at the decision boundary of the existing images has a higher information quality. To validate the method proposed in this paper, experiments are performed on two public datasets. The experimental results confirm the superiority of this method compared with other methods.
Sparse SAR Imaging Method for Ground Moving Target via GMTSI-Net
Authors: Chen Luwei , Ni Jiacheng , Luo Ying , He Qifang , Lu Xiaofei
Published: September 2022

Ground moving targets (GMT), due to the existence of velocity in range and azimuth direction, will lead to the deviation from their true position and defocus in the azimuth direction during the synthetic aperture radar (SAR) imaging process. To address this problem and compress the amount of echo data, a sparse SAR imaging method for ground moving targets is proposed. Specifically, we first constructed a two-dimensional sparse observation model of the GMT based on matched filter operators. Then, the observation model was solved by a deep network, GMT sparse imaging network (GMTSI-Net), which was mainly obtained by unfolding an iterative soft threshold algorithm (ISTA)-based iterative solution. Furthermore, we designed an adaptive unfolding module in the imaging network to improve the adaptability of the network to the input of echo data with different sampling ratios. The proposed imaging network can achieve faster and more accurate SAR images of ground moving targets under a low sampling ratio and signal-to-noise ratio (SNR). Simulated and measured data experiments were conducted to demonstrate the performance of imaging quality of the proposed method.
Remote Sensing Scene Image Classification Based on mmsCNN–HMM with Stacking Ensemble Model
Authors: Cheng Xiang , Lei Hong
Published: September 2022

The development of convolution neural networks (CNNs) has become a significant means to solve the problem of remote sensing scene image classification. However, well-performing CNNs generally have high complexity and are prone to overfitting. To handle the above problem, we present a new classification approach using an mmsCNN–HMM combined model with stacking ensemble mechanism in this paper. First of all, a modified multi-scale convolution neural network (mmsCNN) is proposed to extract multi-scale structural features, which has a lightweight structure and can avoid high computational complexity. Then, we utilize a hidden Markov model (HMM) to mine the context information of the extracted features of the whole sample image. For different categories of scene images, the corresponding HMM is trained and all the trained HMMs form an HMM group. In addition, our approach is based on a stacking ensemble learning scheme, in which the preliminary predicted values generated by the HMM group are used in an extreme gradient boosting (XGBoost) model to generate the final prediction. This stacking ensemble learning mechanism integrates multiple models to make decisions together, which can effectively prevent overfitting while ensuring accuracy. Finally, the trained XGBoost model conducts the scene category prediction. In this paper, the six most widely used remote sensing scene datasets, UCM, RSSCN, SIRI-WHU, WHU-RS, AID, and NWPU, are selected to carry out all kinds of experiments. The numerical experiments verify that the proposed approach shows more important advantages than the advanced approaches.
Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite
Authors: Xu Lingling , Xiong Wei , Yi Weining , Qiu Zhenwei , Liu Xiao , Zhang Dongying , Fang Wei , Li Zhengqiang , Hou Weizhen , Lin Jun , Hu Zhongzheng , Wang Tao , Cui Wenyu
Published: September 2022

Atmospheric conditions vary significantly in terms of the temporal and spatial scales. Therefore, it is critical to obtain atmospheric parameters synchronized with an image for atmospheric correction based on radiative transfer calculation methods. On 3 July 2020, the high resolution and multimode imaging satellite, Gao Fen Duo Mo (GFDM), which was the first civilian high-resolution remote sensing satellite equipped with the Synchronization Monitoring Atmospheric Corrector (SMAC), was launched. The SMAC is a multispectral and polarization detection device that is used to retrieve atmospheric parameters that are time-synchronized with the image sensor of GFDM in the same field-of-view. On the basis of the atmospheric parameters obtained from the SMAC, a synchronization atmospheric correction (Syn-AC) method is proposed to remove the influence of the atmosphere and the adjacency effects to retrieve the surface reflectance. The Syn-AC method was applied in the experiments of synchronous atmospheric correction for GFDM images, where the surface reflectance retrieved via the Syn-AC method was compared with the field-measured values. In addition, the classical correction method, the FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method. The results indicated that the image possessed better clarity and contrast with the blurring effect removed, and the multispectral reflectance was in agreement with the field-measured spectral reflectance. The deviations between the reflectance retrievals of Syn-AC and the field-measured values of the selected targets were within 0.0625, representing a higher precision than that of the FLAASH method (the max deviation was 0.2063). For the three sites, the mean relative error of Syn-AC was 19.3%, and the mean relative error of FLAASH was 76.6%. Atmospheric correction based on synchronous atmospheric parameters can improve the quantitative accuracy of remote sensing images, and it is meaningful for remote sensing applications.
The Influence of Temperature Inversion on the Vertical Distribution of Aerosols
Authors: Li Hui , Liu Boming , Ma Xin , Ma Yingying , Jin Shikuan , Fan Ruonan , Wang Weiyan , Fang Jing , Zhao Yuefeng , Gong Wei
Published: September 2022

Temperature inversion plays an important role in the accumulation and diffusion of aerosols. In this study, the relationship between temperature inversion and the vertical distribution of aerosols is investigated based on Raman lidar observations taken from January 2010 to September 2015 at the Atmospheric Radiation Measurement site in the Southern Great Plains, USA. First, the diurnal and seasonal variations of the surface-based inversion (SBI) and elevated temperature inversion (EI) are investigated. The results indicate that the occurrence frequency of SBI and EI have different seasonal trends. SBI has the highest frequency in summer, while EI has the highest frequency in winter. The diurnal variation of SBI is obvious, with a higher frequency in nighttime and a lower frequency in daytime. The inversion intensity (ΔT) and inversion depth (ΔZ) of SBI and EI have consistent diurnal and seasonal trends. The effects of SBI and EI on the vertical distribution of aerosols are then analyzed. The mean aerosol optical depth (AOD) below the SBI height shows a clear seasonal variation, which is consistent with the seasonal trends of ΔT and ΔZ. This phenomenon also occurs on the AOD below EI top height. The sensitivity analysis shows that the mean AOD below SBI height or EI top height increases with an increase of the ΔT and ΔZ of SBI (EI). It indicates that ΔT and ΔZ are the key factors affecting the vertical distribution of aerosols. In addition, the variation of AOD below and above EI top height is opposite to that of AOD below and above EI bottom height under different ΔT and ΔZ conditions. The correlation coefficients between ΔT (ΔZ) of EI with AOD in EI were 0.62 (0.65). These results indicate that the space between EI bottom height and EI top height can store aerosols. The larger the ΔZ of EI, the more aerosols are stored. These findings contribute to our understanding of the effect of temperature inversion on the vertical distribution of aerosols.
Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China
Authors: Chu Wenhao , Zhang Chunxiao , Zhao Yuwei , Li Rongrong , Wu Pengda
Published: September 2022

Aerosol optical depth (AOD) observations have been widely used to generate wide-coverage PM2.5 retrievals due to the adverse effects of long-term exposure to PM2.5 and the sparsity and unevenness of monitoring sites. However, due to non-random missing and nighttime gaps in AOD products, obtaining spatiotemporally continuous hourly data with high accuracy has been a great challenge. Therefore, this study developed an automatic geo-intelligent stacking (autogeoi-stacking) model, which contained seven sub-models of machine learning and was stacked through a Catboost model. The autogeoi-stacking model used the automated feature engineering (autofeat) method to identify spatiotemporal characteristics of multi-source datasets and generate extra features through automatic non-linear changes of multiple original features. The 10-fold cross-validation (CV) evaluation was employed to evaluate the 24-hour and continuous ground-level PM2.5 estimations in the Beijing-Tianjin-Hebei (BTH) region during 2018. The results showed that the autogeoi-stacking model performed well in the study area with the coefficient of determination (R2) of 0.88, the root mean squared error (RMSE) of 17.38 µg/m3, and the mean absolute error (MAE) of 10.71 µg/m3. The estimated PM2.5 concentrations had an excellent performance during the day (8:00–18:00, local time) and night (19:00–07:00) (the cross-validation coefficient of determination (CV-R2): 0.90, 0.88), and captured hourly PM2.5 variations well, even in the severe ambient air pollution event. On the seasonal scale, the R2 values from high to low were winter, autumn, spring, and summer, respectively. Compared with the original stacking model, the improvement of R2 with the autofeat and hyperparameter optimization approaches was up to 5.33%. In addition, the annual mean values indicated that the southern areas, such as Shijiazhuang, Xingtai, and Handan, suffered higher PM2.5 concentrations. The northern regions (e.g., Zhangjiakou and Chengde) experienced low PM2.5. In summary, the proposed method in this paper performed well and could provide ideas for constructing geoi-features and spatiotemporally continuous inversion products of PM2.5.