bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial and Temporal Evolution of Groundwater Chemistry of Baotu Karst Water System at Northern China
Authors: Li Changsuo , Zhang Xingzhou , Gao Xubo , Li Chengcheng , Jiang Chunfang , Liu Wen , Lin Guangqi , Zhang Xin , Fang Jiancong , Ma Lijun , Zhang Xiubo
Published: March 2022

Karst water quality degradation has been a challenge for domestic and industrial water supplies worldwide. To reveal the possible factors response for karst water quality degradation, Baotu karst spring system is studied as an representative example. In this study, a hydrogeochemical investigation and mathematical, statistical, and geochemical modeling was conducted together to identify the major hydrochemical processes involved in the degradation process. It is found that the karst water is normally fresh, neutral-to-slightly alkaline, with calcium and magnesium as the predominant cations, and bicarbonate and sulfate as the predominant anions. The abnormally high chloride (95.05 mg/L) and nitrate concentrations (148.4 mg/L) give clues to the potential source of contamination in some karst water. The main hydrochemical facies of karst water are HCO3-Ca and HCO3 × SO4-Ca, accounting for 76% of water samples. The water hydrochemistry is controlled mainly by the dissolution of carbonate minerals (calcite, dolomite), followed by the dissolution of silicate and gypsum. The dissolution of calcite and dolomite mostly happens in the recharge area. In the discharge area, the karst water is basically in equilibrium with calcite. The negative SI value of gypsum represents that the water–gypsum interaction is dominated by dissolution along the whole flow path. Cation exchange is observed in the karst water in the indirect recharge area. Along the flow path, the contents of chloride, sulfate, nitrate, and TDS (Total dissolved solids, abbreviated TDS, indicates how many milligrams of dissolved solids are dissolved in one liter of water) vary significantly, which is mostly affected by pollution from human industrial and agricultural activities. The concentrations of major ions were maintained at a low level (<20 mg/L) in the 1960s in karst water. The fast elevation of the parameter values has occurred in the past two decades. The temporal elevation of some pollutants in karst water suggest that global changes (acid rain) and human activity (such as overusing fertilizer) are main factors resulting in the degradation of karst water quality in the study area. The results of this paper provide invaluable information for the management and protection of karst water resources in the urban and rural areas.
Rehabilitation of Disturbed Lands with Industrial Wastewater Sludge
Authors: Petrova Tatyana A. , Rudzisha Edelina , Alekseenko Alexey V. , Bech Jaume , Pashkevich Mariya A.
Published: March 2022

Wastelands of the mining industry are among the largest of disturbed areas that demand revitalization. To reduce environmental impact and to better manage these geo-resources, the formation of sustainable plant and soil complexes and the restoration of self-recovery soil function are critical points. The successful return of vegetative cover at post-mining sites requires eliminating the deficiency of organic matter. For this, we assessed the usability of non-traditional ameliorants to provide a better understanding of benefits from mutual dependencies of environmental resources. To prevent losses and to close resource cycles, we studied the applicability of wastewater sludge from the pulp and paper (SPP) industry as an amendment to counteract soil degradation and rehabilitate human-disturbed lands. Waste rock limestone, beresite, and phosphogypsum substrates of post-mining sites were used in vitro for the application of sludge and peat mixture and consequent grass seeding. The formed vegetative cover was analyzed to compare the germination and biomass growth on reconstructed soils. We assessed the efficiency of ameliorant combinations by two approaches: (1) the traditional technique of cutting-off plant material to measure the obtained plant biomass, and (2) digital image analysis for RGB-processed photographs of the vegetative cover (r2 = 0.75–0.95). The effect of SPP on plant cover biomass and grass height showed similar results: land rehabilitation with the formation of a 20 cm soil layer on mine waste dumps was environmentally suitable with an SPP:soil ratio of 1:3. However, excessive application (ratio 1:1 of SPP to the soil) negatively affected seed germination and plant vegetation.
Iron Ore Tailing Composition Estimation Using Fused Visible–Near Infrared and Thermal Infrared Spectra by Outer Product Analysis
Authors: Bao Nisha , Lei Haimei , Cao Yue , Liu Shanjun , Gu Xiaowei , Zhou Bin , Fu Yanhua
Published: March 2022

Iron ore tailings are mainly composed of SiO2 and iron, whose content determines the potential reuse strategy of the tailings. Compared with the traditional wet chemistry approach, spectroscopy has proven its superior effectiveness in characterizing and predicting minerals, such as iron oxides, clay, and SiO2. This study aims to estimate the content of SiO2 and TFe in iron ore tailings based on visible–near infrared (VIS–NIR, 350–2500 nm) and thermal infrared (TIR, 8–14 μm) spectroscopy. The outer product analysis (OPA) method is used to combine VIS–NIR and TIR spectral domains, from which an outer product matrix of fusion data can be generated. The study area is the iron ore tailing dam from Waitoushan, which is one of the super-large iron deposits in the Anshan–Benxi iron cluster of northeastern China. The spectral analysis results demonstrated the following: (1) The reflectance feature at 1163–2499 nm in the VIS–NIR range correlates with TFe and the emissivity feature at 8–9.4 and 10.7–12 μm in the TIR range correlates with SiO2. (2) Compared with the original absorbance spectra, the correlation coefficients of fusion spectra improve from 0.66 to 0.87 for TFe and from 0.64 to 0.84 for SiO2. (3) The partial least squares regression, random forest (RF), and extreme learning machine exploiting particle swarm optimization modeling methods are established for SiO2 and TFe estimation. The prediction accuracy results indicate that the prediction model with OPA-fused spectra performs significantly better than with individual VIS–NIR and TIR spectra. The RF model with input-fused spectra provides the highest accuracy with the coefficients of determination of 0.95 and 0.91, root mean square errors of 0.97% and 0.96%, and ratios of performance to interquartile distance of 6.49 and 2.31 for SiO2 and TFe content estimation, respectively. These outcomes provide a theoretical basis and technical support for tailing composition estimation using spectroscopy.
An Effective Label-Free Electrochemical Aptasensor Based on Gold Nanoparticles for Gluten Detection
Authors: Svigelj Rossella , Zuliani Ivan , Grazioli Cristian , Dossi Nicolò , Toniolo Rosanna
Published: March 2022

Nanomaterials can be used to modify electrodes and improve the conductivity and the performance of electrochemical sensors. Among various nanomaterials, gold-based nanostructures have been used as an anchoring platform for the functionalization of biosensor surfaces. One of the main advantages of using gold for the modification of electrodes is its great affinity for thiol-containing molecules, such as proteins, forming a strong Au-S bond. In this work, we present an impedimetric biosensor based on gold nanoparticles and a truncated aptamer for the quantification of gluten in hydrolyzed matrices such as beer and soy sauce. A good relationship between the Rct values and PWG-Gliadin concentration was found in the range between 0.1–1 mg L−1 of gliadin (corresponding to 0.2–2 mg L−1 of gluten) with a limit of detection of 0.05 mg L−1 of gliadin (corresponding to 0.1 mg L−1 of gluten). The label-free assay was also successfully applied for the determination of real food samples.
Systematic Research on the Transport of Ball-Milled Biochar in Saturated Porous Media: Effect of Humic Acid, Ionic Strength, and Cation Types
Authors: Cao Gang , Qiao Jiachang , Ai Juehao , Ning Shuaiqi , Sun Huimin , Chen Menghua , Zhao Lin , Zhang Guilong , Lian Fei
Published: March 2022

Ball-milled biochar (BMBC) is a typical engineering material that has promising application prospects in remediating contaminated soil and water. It is fundamental to rate the transport behaviors of BMBC in the underground environment before extensive use. In this study, the effects of the ubiquitous cations (Na+, Mg2+, and Al3+) and model organic matter (humic acid) on the transport of BMBC were investigated using laboratory column experiments. The results demonstrated the facilitated effect of HA on the transport of BMBC due to the negatively charged surface and steric effect under neutral conditions. HA and ionic strength manifested an antagonistic effect on the transport of BMBC, where the presence of one could weaken the effect from the other. We also found the charge reversal of the BMBC surface in the presence of Mg2+, thus enhancing the deposition of BMBC onto the medium surface. On the other hand, the charge reversal from Al3+-coupled acid conditions led to the restabilization and transport of BMBC in porous media. Therefore, the rational usage of BMBC is indispensable and more attention should be paid to the composition and change in underground water that might facilitate the transport of BMBC and thus lead to negative environmental implications.
Nanoarchitectonics of Illite-Based Materials: Effect of Metal Oxides Intercalation on the Mechanical Properties
Authors: Jia Jiwei , Wu Daoyong , Ren Yu , Lin Jiyu
Published: March 2022

Clay minerals inevitably interact with colloidal oxides (mainly iron and aluminum oxides) in the evolution of natural geomaterials. However, the interaction between the clay minerals and the colloidal oxides affecting the stability and the strength of geotechnical materials remains poorly understood. In the present work, the interaction between the clay minerals and the colloidal oxides was investigated by reaction molecular dynamics simulations to explore the mechanical properties of illite-based materials. It was found that the metal atoms of the intercalated amorphous iron and aluminum oxides interact with oxygen atoms of the silica tetrahedron at the interface generating chemical bonds to enhance the strength of the illite-based materials considerably. The deformation and failure processes of the hybrid illite-based structures illustrated that the Al–O bonds were more favorable to the mechanical properties’ improvement of the hybrid system compared with Fe–O bonds. Moreover, the anisotropy of illite was greatly improved with metal oxide intercalation. This study provides new insight into the mechanical properties’ improvement of clay-based materials through metal oxides intercalation.
Effect of Graphene Oxide and Nanosilica Modifications on Electrospun Core-Shell PVA–PEG–SiO2@PVA–GO Fiber Mats
Authors: Kan Yuliya , Bondareva Julia V. , Statnik Eugene S. , Cvjetinovic Julijana , Lipovskikh Svetlana , Abdurashitov Arkady S. , Kirsanova Maria A. , Sukhorukhov Gleb B. , Evlashin Stanislav A. , Salimon Alexey I. , Korsunsky Alexander M.
Published: March 2022

Electrospinning is a well-established method for the fabrication of polymer biomaterials, including those with core-shell nanofibers. The variability of structures presents a great range of opportunities in tissue engineering and drug delivery by incorporating biologically active molecules such as drugs, proteins, and growth factors and subsequent control of their release into the target microenvironment to achieve therapeutic effect. The object of study is non-woven core-shell PVA–PEG–SiO2@PVA–GO fiber mats assembled by the technology of coaxial electrospinning. The task of the core-shell fiber development was set to regulate the degradation process under external factors. The dual structure was modified with silica nanoparticles and graphene oxide to ensure the fiber integrity and stability. The influence of the nano additives and crosslinking conditions for the composite was investigated as a function of fiber diameter, hydrolysis, and mechanical properties. Tensile mechanical tests and water degradation tests were used to reveal the fracture and dissolution behavior of the fiber mats and bundles. The obtained fibers were visualized by confocal fluorescence microscopy to confirm the continuous core-shell structure and encapsulation feasibility for biologically active components, selectively in the fiber core and shell. The results provide a firm basis to draw the conclusion that electrospun core-shell fiber mats have tremendous potential for biomedical applications as drug carriers, photocatalysts, and wound dressings.
Conversion of Carbon Dioxide into Methanol Using Cu–Zn Nanostructured Materials as Catalysts
Authors: García Anna Carrasco , Moral-Vico Javier , Abo Markeb Ahmad , Sánchez Antoni
Published: March 2022

Nowadays, there is a growing awareness of the great environmental impact caused by the enormous amounts of carbon dioxide emitted. Several alternatives exist to solve this problem, and one of them is the hydrogenation of carbon dioxide into methanol by using nanomaterials as catalysts. The aim of this alternative is to produce a value-added chemical, such as methanol, which is a cheaply available feedstock. The development of improved materials for this conversion reaction and a deeper study of the existing ones are important for obtaining higher efficiencies in terms of yield, conversion, and methanol selectivity, in addition to allowing milder reaction conditions in terms of pressure and temperature. In this work, the performance of copper, zinc, and zinc oxide nanoparticles in supported and unsupported bimetallic systems is evaluated in order to establish a comparison among the different materials according to their efficiency. For that, a packed bed reactor operating with a continuous gas flow is used. The obtained results indicate that the use of bimetallic systems combined with porous supports, such as zeolite and activated carbon, is beneficial, thus improving the performance of unsupported materials by four times.
Calomys callosus: An Experimental Animal Model Applied to Parasitic Diseases Investigations of Public Health Concern
Authors: Rosa Rafael Borges , Costa Mylla Spirandelli da , Teixeira Samuel Cota , Castro Emilene Ferreira de , Dantas Willyenne Marília , Ferro Eloisa Amália Vieira , Silva Murilo Vieira da
Published: March 2022

The appearance and spread of parasitic diseases around the world aroused the interest of the scientific community to discover new animal models for improving the quality and specificity of surveys. Calomys callosus is a rodent native to South America, an easy handling model, with satisfactory longevity and reproducibility. C. callosus is susceptible to toxoplasmosis and can be used as experimental model for the study the pathogenesis, treatment, vertical transmission, and ocular toxoplasmosis. C. callosus can also be used to study cutaneous and visceral leishmaniasis, as the animals present cutaneous lesions, as well as parasites in the organs. C. callosus has epidemiological importance in Chagas disease, and since it is a Trypanosoma cruzi natural host in which rodents show high parasitemia and lethality, they are also effective as a model of congenital transmission. In the study of schistosomiasis, Schistosoma mansoni was proven to be a C. callosus natural host; thus, this rodent is a great model for fibrosis, hepatic granulomatous reaction, and celloma associated with lymphomyeloid tissue (CALT) during S. mansoni infection. In this review, we summarize the leading studies of parasitic diseases that used C. callosus as a rodent experimental model, describing the main uses and characteristics that led them to be considered an effective model.
Unexpected TBEV Seropositivity in Serbian Patients Who Recovered from Viral Meningitis and Encephalitis
Authors: Banović Pavle , Díaz-Sánchez Adrian Alberto , Đurić Selena , Sević Siniša , Turkulov Vesna , Lendak Dajana , Mikić Sandra Stefan , Simin Verica , Mijatović Dragana , Bogdan Ivana , Potkonjak Aleksandar , Savić Sara , Obregón Dasiel , Cabezas-Cruz Alejandro
Published: March 2022

The tick-borne encephalitis virus (TBEV) causes a life-threatening disease named Tick-borne encephalitis (TBE). The clinical symptoms associated with TBE range from non-specific to severe inflammation of the central nervous system and are very similar to the clinical presentation of other viral meningitis/encephalitis. In consequence, TBE is often misclassified by clinical physicians, mainly in the non-identified high-risk areas where none or only a few TBE cases have been reported. Considering this situation, we hypothesized that among persons from northern Serbia who recovered from viral meningitis or encephalitis, there would be evidence of TBEV infection. To test this hypothesis, in this observational study, we evaluated the seroreactivity against TBEV antigens in patients from northern Serbia who were hospitalized due to viral meningitis and/or viral encephalitis of unknown etiology. Three cases of seroreactivity to TBEV antigens were discovered among convalescent patients who recovered from viral meningitis and/or encephalitis and accepted to participate in the study (n = 15). The clinical and laboratory findings of these patients overlap with that of seronegative convalescent patients. Although TBE has been a notifiable disease in Serbia since 2004, there is no active TBE surveillance program for the serologic or molecular screening of TBEV infection in humans in the country. This study highlights the necessity to increase the awareness of TBE among physicians and perform active and systematic screening of TBEV antibodies among patients with viral meningitis and/or encephalitis.
Identification of Inflammatory and Regulatory Cytokines IL-1α-, IL-4-, IL-6-, IL-12-, IL-13-, IL-17A-, TNF-α-, and IFN-γ-Producing Cells in the Milk of Dairy Cows with Subclinical and Clinical Mastitis
Authors: Vitenberga-Verza Zane , Pilmane Māra , Šerstņova Ksenija , Melderis Ivars , Gontar Łukasz , Kochański Maksymilian , Drutowska Andżelika , Maróti Gergely , Prieto-Simón Beatriz
Published: March 2022

In naturally occurring bovine mastitis, effects of infection depend on the host inflammatory response, including the effects of secreted cytokines. Knowledge about the inflammatory and regulatory cytokines in milk cells of free-stall barn dairy cows and in naturally occurring mastitis is lacking as most studies focus on induced mastitis. Hereby, the aim of the study was to determine inflammatory and regulatory cytokines in the milk of dairy cows with subclinical and clinical mastitis. The following examinations of milk samples were performed: differential counting of somatic cells (SCC), bacteriological examination, and immunocytochemical analysis. Mean SCC increased in subclinical and clinical mastitis cases. The number of pathogenic mastitis-causing bacteria on plates increased in subclinical mastitis cases but decreased in clinical mastitis. The inflammatory and regulatory markers in the milk cells of healthy cows showed the highest mean cell numbers (%). In mastitis cases, immunoreactivity was more pronounced for IL-4, IL-6, IL-12, IL-13, IL-17A, TNF-α, and IFN-γ. Data about subclinical and clinical mastitis demonstrate inflammatory responses to intramammary infection driven by IL-1α, IL-4, and IL-17A. Moreover, the host defense response in mastitis is characterized by continuation or resolution of initial inflammation. IL-12 and INF-γ immunoreactivity was recognized to differ mastitis cases from the relative health status.
Graphene Nanoplatelets as a Replacement for Carbon Black in Rubber Compounds
Authors: Innes James R. , Young Robert J. , Papageorgiou Dimitrios G.
Published: March 2022

In this work, we evaluated the processing and reinforcement characteristics of both carbon black (CB) and graphene nanoplatelets (GNPs) within a nitrile butadiene rubber (NBR) matrix. The aspect ratio of the GNPs was measured using atomic force microscopy (AFM) and related to the dispersion and agglomeration within the NBR matrix, as observed by scanning electron microscopy (SEM). The relationship between GNP aspect ratio and mechanical properties was studied by micromechanical modelling. The tensile and tear properties of NBR after compounding with GNPs were enhanced to a greater extent compared to carbon black, while curing times were smaller and scorch times longer, indicating some of the advantages of using GNPs. Overall, the inherent properties of GNPs along with their geometry led to the production of better-performing rubber compounds that can replace their CB-filled counterparts in applications where flexibility, tear strength and compliance are important. The influence of processing on dispersion, orientation and agglomeration of flakes was also highlighted with respect to the Young’s modulus of the NBR compounds.
Modeling Impact Mechanics of 3D Helicoidally Architected Polymer Composites Enabled by Additive Manufacturing for Lightweight Silicon Photovoltaics Technology
Authors: Budiman Arief Suriadi , Sahay Rahul , Agarwal Komal , Fajarna Rayya , Gunawan Fergyanto E. , Baji Avinash , Raghavan Nagarajan
Published: March 2022

When silicon solar cells are used in the novel lightweight photovoltaic (PV) modules using a sandwich design with polycarbonate sheets on both the front and back sides of the cells, they are much more prone to impact loading, which may be prevalent in four-season countries during wintertime. Yet, the lightweight PV modules have recently become an increasingly important development, especially for certain segments of the renewable energy markets all over the world—such as exhibition halls, factories, supermarkets, farms, etc.—including in countries with harsh hailstorms during winter. Even in the standard PV module design using glass as the front sheet, the silicon cells inside remain fragile and may be prone to impact loading. This impact loading has been widely known to lead to cracks in the silicon solar cells that over an extended period of time may significantly degrade performance (output power). In our group’s previous work, a 3D helicoidally architected fiber-based polymer composite (enabled by an electrospinning-based additive manufacturing methodology) was found to exhibit excellent impact resistance—absorbing much of the energy from the impact load—such that the silicon solar cells encapsulated on both sides by this material breaks only at significantly higher impact load/energy, compared to when a standard, commercial PV encapsulant material was used. In the present study, we aim to use numerical simulation and modeling to enhance our understanding of the stress distribution and evolution during impact loading on such helicoidally arranged fiber-based composite materials, and thus the damage evolution and mechanisms. This could further aid the implementation of the lightweight PV technology for the unique market needs, especially in countries with extreme winter seasons.
Analysis of a Film Forming Process through Coupled Image Correlation and Infrared Thermography
Authors: Neubauer Moritz , Dannemann Martin , Herzer Niklas , Schwarz Benjamin , Modler Niels
Published: March 2022

The aim of the present investigation was to determine the dependence of the material and process parameters of the bending process of thermoplastic films. In this context, parameter combinations leading to high resulting forming ratios were identified. To measure the relevant parameters within the hot bending process, a coupled evaluation of infrared thermography (IRT) and deformation measurement using digital image correlation (DIC) was performed. The coupled measurement enables the identification of the actual mechanically stressed bending area of the film as a result of the bending process. This allows for the specification of the local forming temperatures required for the desired forming ratios. Furthermore, the mechanical and thermal strain along the defined measuring sections and their deviation in individual tests as well as the effect of thermal strain on process control on a larger scale were determined. Based on the results, a process window was defined for the film materials investigated, which will serve as a starting point for future efforts to develop a continuous manufacturing process.
Physico-Mechanical Properties of Commercially Available Tissue Conditioner Modified with Synthesized Chitosan Oligosaccharide
Authors: Saeed Asfia , Zahid Shahreen , Sajid Muhammad , Ud Din Shahab , Alam Mohammad Khursheed , Chaudhary Farooq Ahmad , Kaleem Muhammad , Alswairki Haytham Jamil , Abutayyem Huda
Published: March 2022

This study aims to compare the hardness, sorption and solubility of commercially available tissue conditioner [TC] modified with chitosan [CS] and synthesized chitosan oligosaccharide [COS] in antifungal concentration. COS was synthesized by acid hydrolysis and characterized by FTIR and XRD. Experimental materials were formulated by incorporating each per gram of TC powder with effective antifungal concentration of chitosan 1.02 mg (Group 1: TC-CS) and 0.51 mg COS (Group 2: TC-COS). A commercially available TC was used as control (Group 0: CTC). Shore A hardness test was performed according to ASTM D 2240-05 (2010) standards on samples stored in dry environment, distilled water (DW) and artificial saliva (AS) at 37 °C (n = 5 per group). Percent weight changes (n = 5 per group) after storage in DW and AS was used to record sorption and solubility. One-way Anova with post hoc Tukey’s test was applied. FTIR and XRD confirmed low molecular weight and amorphous nature of COS. Experimental groups had higher Shore A hardness values; however, these changes were not significant. Greatest variations in durometer values (p ≤ 0.05) were observed during the first 24 h. Experimental groups had higher (p ≤ 0.05) percentage sorption and solubility. Samples stored in DW had significantly higher (p = 0.019) sorption, whereas material had higher (p = 0.005) solubility in AS. Mean solubility values in both immersion mediums was highest for Group 2, followed by group 1 and group 0. In addition, significant (p ≤ 0.05) increase in solubility upon aging was noted for each material. Experimental tissue conditioner had higher hardness, sorption and solubility. However, these changes are not substantial to interfere with their tissue healing property. Therefore, these materials may be considered and explored further as potential antimicrobial drug delivery agent for denture stomatitis patients.
Fast Pyrolysis Bio-Oil-Based Epoxy as an Adhesive in Oriented Strand Board Production
Authors: Asafu-Adjaye Osei Asibe , Street Jason , Bansode Archana , Auad Maria L. , Peresin Maria Soledad , Adhikari Sushil , Liles Terry , Via Brian K.
Published: March 2022

The objectives of this study were to utilize bio-oil-based epoxy resin in oriented strand board (OSB) production and investigate the effect of bio-oil substitution in epoxy resin as an adhesive for OSB production. Bio-oil was produced by the fast pyrolysis (FP) process using southern yellow pine (Pinus spp.). Bio-oil-based epoxy resin was synthesized by the modification of epoxy resin with FP bio-oil at various substitution levels. Acetone extraction using a Soxhlet process indicated a superior cured reaction of bio-oil and epoxy resin at 20% bio-oil substitution. FTIR spectra corroborated the Soxhlet extraction with the removal of the epoxide peak signature within the cross-linked polymer. Images from the scanning electron microscopy suggested bulk phase homogeneity. OSB panels were tested according to ASTM D1037-12. The modulus of rupture (MOR), modulus of elasticity (MOE), internal bond strength, and water resistance (thickness swell and water absorption) properties of the OSB panels were feasible at bio-oil substitution up to 30% in the epoxy resin system.
Honoring the Saint through Poetry Recitation: Pilgrimage and the Memories of Shaikh Abdurrahman Siddiq Al-Banjari in Indragiri Hilir
Authors: Madjid Abd. , Latief Hilman , Fauzan Aris
Published: March 2022

This paper is about poetry and pilgrimage in Tembilahan, Indragiri Hilir, where Abdurrahman Siddiq, a prominent alim who lived in the late nineteenth and early twentieth century, is buried. In addition to his treatises on theology, mysticism, and ethics, Abdurrahman Siddiq is also renowned for his contribution to Islamic literature in Sumatra. He is a famous Islamic scholar and is respected in Indragiri Hilir for his spiritual messages delivered in the form of sya’ir (poetry). Malay Muslims have preserved the saint’s legacy through the act of pilgrimage and various cultural events and festivals. This paper argues that the recitation of Abdurrahman Siddiq’s poems during the pilgrimage to Tembilahan and Islamic festivals shows how Malay Muslims honor their respected saint.
A Combination of Lie Group Machine Learning and Deep Learning for Remote Sensing Scene Classification Using Multi-Layer Heterogeneous Feature Extraction and Fusion
Authors: Xu Chengjun , Zhu Guobin , Shu Jingqian
Published: March 2022

Discriminative feature learning is the key to remote sensing scene classification. Previous research has found that most of the existing convolutional neural networks (CNN) focus on the global semantic features and ignore shallower features (low-level and middle-level features). This study proposes a novel Lie Group deep learning model for remote sensing scene classification to solve the above-mentioned challenges. Firstly, we extract shallower and higher-level features from images based on Lie Group machine learning (LGML) and deep learning to improve the feature representation ability of the model. In addition, a parallel dilated convolution, a kernel decomposition, and a Lie Group kernel function are adopted to reduce the model’s parameters to prevent model degradation and over-fitting caused by the deepening of the model. Then, the spatial attention mechanism can enhance local semantic features and suppress irrelevant feature information. Finally, feature-level fusion is adopted to reduce redundant features and improve computational performance, and cross-entropy loss function based on label smoothing is used to improve the classification accuracy of the model. Comparative experiments on three public and challenging large-scale remote-sensing datasets show that our model improves the discriminative ability of features and achieves competitive accuracy against other state-of-the-art methods.
A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification
Authors: DeCastro Amy L. , Juliano Timothy W. , Kosović Branko , Ebrahimian Hamed , Balch Jennifer K.
Published: March 2022

Disturbance events can happen at a temporal scale much faster than wildland fire fuel data updates. When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire research. Remote sensing and machine learning methods can provide a solution for on-demand fuel estimation. Here, we show a proof of concept using C-band synthetic aperture radar and multispectral imagery, land cover classes, and tree mortality surveys to train a random forest classifier to estimate wildland fire fuel data in the East Troublesome Fire (Colorado) domain. The algorithm classified over 80% of the test dataset correctly, and the resulting wildland fire fuel data was used to simulate the East Troublesome Fire using the coupled atmosphere—wildland fire behavior model, WRF-Fire. The simulation using the modified fuel inputs, where 43% of original fuels are replaced with fuels representing dead trees, improved the burn area forecast by 38%. This study demonstrates the need for up-to-date fuel maps available in real time to provide accurate prediction of wildland fire spread, and outlines the methodology based on high-resolution satellite observations and machine learning that can accomplish this task.
Rapid Mapping of Landslides on SAR Data by Attention U-Net
Authors: Nava Lorenzo , Bhuyan Kushanav , Meena Sansar Raj , Monserrat Oriol , Catani Filippo
Published: March 2022

Multiple landslide events are common around the globe. They can cause severe damage to both human lives and infrastructures. Although a huge quantity of research has been shaped to address rapid mapping of landslides by optical Earth Observation (EO) data, various gaps and uncertainties are still present when dealing with cloud obscuration and 24/7 operativity. To address the issue, we explore the usage of SAR data over the eastern Iburi sub-prefecture of Hokkaido, Japan. In the area, about 8000 co-seismic landslides were triggered by an Mw 6.6 earthquake on 6 September 2018, at 03.08 local time (JST). In the following study, we modify a Deep Learning (DL) convolutional neural network (CNN) architecture suited for pixel-based classification purposes, the so-called Attention U-Net (Attn-U-Net) and we employ it to evaluate the potential of bi- and tri-temporal SAR amplitude data from the Sentinel-1 satellite and slope angle to map landslides even under thick cloud cover. Four different datasets, composed of two different band combinations per two satellite orbits (ascending and descending) are analyzed. Moreover, the impact of augmentations is evaluated independently for each dataset. The models’ predictions are compared against an accurate landslide inventory obtained by manual mapping on pre-and post-event PlanetScope imagery through F1-score and other common metrics. The best result was yielded by the augmented ascending tri-temporal SAR composite image (61% F1-score). Augmentations have a positive impact on the ascending Sentinel-1 orbit, while metrics decrease when augmentations are applied on descending path. Our findings demonstrate that combining SAR data with other data sources may help to map landslides quickly, even during storms and under deep cloud cover. However, further investigations and improvements are still needed, this being one of the first attempts in which the combination of SAR data and DL algorithms are employed for landslide mapping purposes.
Farmland Shelterbelt Age Mapping Using Landsat Time Series Images
Authors: Deng Rongxin , Xu Zhengran , Li Ying , Zhang Xing , Li Chunjing , Zhang Lu
Published: March 2022

The age of a shelterbelt is not only an important parameter for determining the function of a shelterbelt, it is also strongly related to the biomass and carbon flux of shelterbelt ecosystems. Therefore, timely and accurate identifications of shelterbelt ages are key for shelterbelt monitoring and management. This study developed a method for estimating shelterbelt age (i.e., years after planting) from a time series of remote sensing images. Firstly, the shelterbelts were divided into three states based on a single remote sensing image of each. Then, a three-stage growth process was established by analysis. Finally, the shelterbelt ages were determined based on time series remote sensing images over a two-year monitoring period in the study area. The actual shelterbelt ages based on field measurements were used to analyze the accuracy of the results. The total number of samples was 243. The results showed that the age identification accuracy was 68.7%. The main factors affecting the identification accuracy were missing images, cloud cover, and the length of the monitoring period. Despite some uncertainties, the proposed method may be used to obtain critical data for shelterbelt management and conducting quick surveys of current shelterbelt conditions over a large area.
Earth Observation Data Exploitation in Urban Surface Modelling: The Urban Energy Balance Response to a Suburban Park Development
Authors: Tsirantonakis Dimitris , Chrysoulakis Nektarios
Published: March 2022

Cities are developing rapidly as an increasing percentage of the global population resides in urban areas. In the face of climate change, the sustainable development of cities is crucial for the well-being and safety of urban populations. The potential of planning interventions towards improving of urban resilience can be evaluated based on methodological approaches used in the domain of urban climate. In this study, we present how Earth Observation (EO) can be systematically used to evaluate urban planning interventions, based on Urban Surface Models (USM) simulations. More specifically, the impact of a suburban park development in Heraklion, Crete, was assessed based on simulations of the USM SUEWS (Surface Urban Energy and Water Balance Scheme), which was forced by EO data. Multi-source satellite data were analyzed to provide information on urban form, highlighting the importance of EO data in evaluating the environmental sustainability potential of urban planning interventions. The modifications caused by this planning intervention to surface energy fluxes were simulated. The scale (102 m) and the type (no-use vegetated area changed to recreational vegetated) of the intervention triggered minor responses in the Urban Energy Balance (UEB) at neighborhood scale, since the change of the relevant surface fluxes was not greater than 10 W m−2, on average, assuming no irrigation and no important changes in soil moisture. However, the planned substitution of grass and bare soil with paved surfaces and trees was found to increase the overall net change in heat storage, therefore contributing to the urban heat island development.
Prediction of Field-Scale Wheat Yield Using Machine Learning Method and Multi-Spectral UAV Data
Authors: Bian Chaofa , Shi Hongtao , Wu Suqin , Zhang Kefei , Wei Meng , Zhao Yindi , Sun Yaqin , Zhuang Huifu , Zhang Xuewei , Chen Shuo
Published: March 2022

Accurate prediction of food crop yield is of great significance for global food security and regional trade stability. Since remote sensing data collected from unmanned aerial vehicle (UAV) platforms have the features of flexibility and high resolution, these data can be used as samples to develop regional regression models for accurate prediction of crop yield at a field scale. The primary objective of this study was to construct regional prediction models for winter wheat yield based on multi-spectral UAV data and machine learning methods. Six machine learning methods including Gaussian process regression (GPR), support vector machine regression (SVR) and random forest regression (RFR) were used for the construction of the yield prediction models. Ten vegetation indices (VIs) extracted from canopy spectral images of winter wheat acquired from a multi-spectral UAV at five key growth stages in Xuzhou City, Jiangsu Province, China in 2021 were selected as the variables of the models. In addition, in situ measurements of wheat yield were obtained in a destructive sampling manner for prediction algorithm modeling and validation. Prediction results of single growth stages showed that the optimal model was GPR constructed from extremely strong correlated VIs (ESCVIs) at the filling stage (R2 = 0.87, RMSE = 49.22 g/m2, MAE = 42.74 g/m2). The results of multiple stages showed GPR achieved the highest accuracy (R2 = 0.88, RMSE = 49.18 g/m2, MAE = 42.57 g/m2) when the ESCVIs of the flowering and filling stages were used. Larger sampling plots were adopted to verify the accuracy of yield prediction; the results indicated that the GPR model has strong adaptability at different scales. These findings suggest that using machine learning methods and multi-spectral UAV data can accurately predict crop yield at the field scale and deliver a valuable application reference for farm-scale field crop management.
Quantitative Evaluation of Gully Erosion Using Multitemporal UAV Data in the Southern Black Soil Region of Northeast China: A Case Study
Authors: Wang Ranghu , Sun Huan , Yang Jiuchun , Zhang Shuwen , Fu Hanpei , Wang Nan , Liu Qianyu
Published: March 2022

The black soil region of northeast China is experiencing severe gully erosion. The lack of periodic, high-resolution, short–medium-term, annual, and seasonal observations considerably limit the comprehensive understanding of the processes and mechanisms of gully erosion caused by multiple forces at the watershed scale. Therefore, in this study, we periodically monitored the geomorphic, morphological, and volume changes of a stabilized gully both annually and seasonally in a small agricultural watershed (6 ha) in the southern black soil region in northeast China based on the centimeter-level resolution of unmanned aerial vehicle (UAV)-derived orthoimages and digital terrain models (DTMs) from 2015 to 2020. Compared with submeter-resolution satellite images, the multitemporal UAV data exhibited strong adaptability and various advantages for the assessment of short–medium-term (≤5 years) gully erosion rates in this region. The results demonstrated that the gully has an actively retreating headcut that was always the main source of sediment yield. The linear, areal, and volumetric gully headcut retreat (GHR) rates were 0.74 m year−1, 7.29 m2 year−1, and 9.66 m3 year−1, respectively. GHR in the rainy season accounted for 94.62% of the annual linear erosion and 87.64% of the areal erosion. In particular, sidewall collapse and gully head expansion dominated in the early rainy season, which accounted for 66.67% of the annual linear erosion and 49% of the areal erosion. Our results provide high-resolution orthoimages and a DTM time series produced by a UAV to evaluate short–medium-term (5 years) GHR rate and quantify the contribution of freeze–thaw processes, snowmelt, and rainfall to gully erosion in the region. The findings contribute to understanding the gully erosion processes induced by multiple forces in the southern black soil region of northeast China.
On the 3D Reconstruction of Coastal Structures by Unmanned Aerial Systems with Onboard Global Navigation Satellite System and Real-Time Kinematics and Terrestrial Laser Scanning
Authors: Gonçalves Diogo , Gonçalves Gil , Pérez-Alvávez Juan Antonio , Andriolo Umberto
Published: March 2022

A wide variety of hard structures protect coastal activities and communities from the action of tides and waves worldwide. It is fundamental to monitor the integrity of coastal structures, as interventions and repairs may be needed in case of damages. This work compares the effectiveness of an Unmanned Aerial System (UAS) and a Terrestrial Laser Scanner (TLS) to reproduce the 3D geometry of a rocky groin. The Structure-from-Motion (SfM) photogrammetry technique applied on drone images generated a 3D point cloud and a Digital Surface Model (DSM) without data gaps. Even though the TLS returned a 3D point cloud four times denser than the drone one, the TLS returned a DSM which was not representing about 16% of the groin (data gaps). This was due to the occlusions encountered by the low-lying scans determined by the displaced rocks composing the groin. Given also that the survey by UAS was about eight time faster than the TLS, the SFM-MV applied on UAS images was the most suitable technique to reconstruct the rocky groin. The UAS remote sensing technique can be considered a valid alternative to monitor all types of coastal structures, to improve the inspection of likely damages, and to support coastal structure management.
CRTransSar: A Visual Transformer Based on Contextual Joint Representation Learning for SAR Ship Detection
Authors: Xia Runfan , Chen Jie , Huang Zhixiang , Wan Huiyao , Wu Bocai , Sun Long , Yao Baidong , Xiang Haibing , Xing Mengdao
Published: March 2022

Synthetic-aperture radar (SAR) image target detection is widely used in military, civilian and other fields. However, existing detection methods have low accuracy due to the limitations presented by the strong scattering of SAR image targets, unclear edge contour information, multiple scales, strong sparseness, background interference, and other characteristics. In response, for SAR target detection tasks, this paper combines the global contextual information perception of transformers and the local feature representation capabilities of convolutional neural networks (CNNs) to innovatively propose a visual transformer framework based on contextual joint-representation learning, referred to as CRTransSar. First, this paper introduces the latest Swin Transformer as the basic architecture. Next, it introduces the CNN’s local information capture and presents the design of a backbone, called CRbackbone, based on contextual joint representation learning, to extract richer contextual feature information while strengthening SAR target feature attributes. Furthermore, the design of a new cross-resolution attention-enhancement neck, called CAENeck, is presented to enhance the characterizability of multiscale SAR targets. The mAP of our method on the SSDD dataset attains 97.0% accuracy, reaching state-of-the-art levels. In addition, based on the HISEA-1 commercial SAR satellite, which has been launched into orbit and in whose development our research group participated, we released a larger-scale SAR multiclass target detection dataset, called SMCDD, which verifies the effectiveness of our method.
Range Gate Pull-Off Mainlobe Jamming Suppression Approach with FDA-MIMO Radar: Theoretical Formalism and Numerical Study
Authors: Wan Pengfei , Weng Yuanlong , Xu Jingwei , Liao Guisheng
Published: March 2022

With the development of an electronic interference technique, the self-defense jammer can generate mainlobe jamming using the range gate pull-off (RGPO) strategy, which brings serious performance degradation of target tracking for the ground-based warning radar. In this paper, a RGPO mainlobe jamming suppression approach is proposed, with a frequency diverse array using multiple-input multiple-output (FDA-MIMO) radar. The RGPO mainlobe jamming differs from the true target in slant range, thus it is possible to identify the true target from the RGPO mainlobe jammings by exploiting the transmit beampattern diversity of FDA-MIMO radar. A RGPO mainlobe jamming suppression approach is devised by using joint transmit–receive beamforming for a group of range sectors. The jamming suppression performance is studied, in consideration of practical time-delay of RGPO jamming. Simulation examples are provided to verify the effectiveness of the proposed approach.
Semantic Segmentation of Polarimetric SAR Image Based on Dual-Channel Multi-Size Fully Connected Convolutional Conditional Random Field
Authors: Kong Yingying , Li Qiupeng
Published: March 2022

The traditional fully connected convolutional conditional random field has a proven robust performance in post-processing semantic segmentation of SAR images. However, the current challenge is how to improve the richness of image features, thereby improving the accuracy of image segmentation. This paper proposes a polarization SAR image semantic segmentation method based on a dual-channel multi-size fully connected convolutional conditional random field. Firstly, the full-polarization SAR image and the corresponding optical image are input into the model at the same time, which can increase the richness of feature information. Secondly, multi-size input integrates image information of different sizes and models images of various sizes. Finally, the importance of features is introduced to determine the weights of polarized SAR images and optical images, and CRF is improved into a potential function so that the model can adaptively adjust the degree of influence of different image features on the segmentation effect. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method.