bluuebunny/crossref_metadata_2025 huggingface dataset:
Genome-Wide Identification of Hsp90 Gene Family in Perennial Ryegrass and Expression Analysis under Various Abiotic Stresses
Authors: Appiah Charlotte , Yang Zhong-Fu , He Jie , Wang Yang , Zhou Jie , Xu Wen-Zhi , Nie Gang , Zhu Yong-Qun
Published: November 2021

The heat shock protein 90 (Hsp90) is a protein produced in plants in response to stress. This study identified and analyzed Hsp90 gene family members in the perennial ryegrass genome. From the results, eight Hsp90 proteins were obtained and their MW, pI and number of amino acid bases varied. The amino acid bases ranged from 526 to 862. The CDS also ranged from 20 (LpHsp0-4) to 1 (LpHsp90-5). The least number of CDS regions was 1 (LpHsp90-5) with 528 kb amino acids, while the highest was 20 (LpHsp90-4) with 862 kb amino acids, which showed diversity among the protein sequences. The phylogenetic tree revealed that Hsp90 genes in Lolium perenne, Arabidopsis thaliana, Oryza sativa and Brachypodium distachyon could be divided into two groups with five paralogous gene pairs and three orthologous gene pairs. The expression analysis after perennial ryegrass was subjected to heat, salt, chromium (Cr), cadmium (Cd), polyethylene glycol (PEG) and abscisic acid (ABA) revealed that LpHsp90 genes were generally highly expressed under heat stress, but only two LpHsp90 proteins were expressed under Cr stresses. Additionally, the expression of the LpHsp90 proteins differed at each time point in all treatments. This study provides the basis for an understanding of the functions of LpHsp90 proteins in abiotic stress studies and in plant breeding.
Comparative Analysis of Genotyping by Sequencing and Whole-Genome Sequencing Methods in Diversity Studies of Olea europaea L.
Authors: Friel James , Bombarely Aureliano , Fornell Carmen Dorca , Luque Francisco , Fernández-Ocaña Ana Maria
Published: November 2021

Olive, Olea europaea L., is a tree of great economic and cultural importance in the Mediterranean basin. Thousands of cultivars have been described, of which around 1200 are conserved in the different olive germplasm banks. The genetic characterisation of these cultivars can be performed in different ways. Whole-genome sequencing (WGS) provides more information than the reduced representation methods such as genotype by sequencing (GBS), but at a much higher cost. This may change as the cost of sequencing continues to drop, but, currently, genotyping hundreds of cultivars using WGS is not a realistic goal for most research groups. Our aim is to systematically compare both methodologies applied to olive genotyping and summarise any possible recommendations for the geneticists and molecular breeders of the olive scientific community. In this work, we used a selection of 24 cultivars from an olive core collection from the World Olive Germplasm Collection of the Andalusian Institute of Agricultural and Fisheries Research and Training (WOGBC), which represent the most of the cultivars present in cultivated fields over the world. Our results show that both methodologies deliver similar results in the context of phylogenetic analysis and popular population genetic analysis methods such as clustering. Furthermore, WGS and GBS datasets from different experiments can be merged in a single dataset to perform these analytical methodologies with proper filtering. We also tested the influence of the different olive reference genomes in this type of analysis, finding that they have almost no effect when estimating genetic relationships. This work represents the first comparative study between both sequencing techniques in olive. Our results demonstrate that the use of GBS is a perfectly viable option for replacing WGS and reducing research costs when the goal of the experiment is to characterise the genetic relationship between different accessions. Besides this, we show that it is possible to combine variants from GBS and WGS datasets, allowing the reuse of publicly available data.
Plasma-Activated Water Triggers Rapid and Sustained Cytosolic Ca2+ Elevations in Arabidopsis thaliana
Authors: Cortese Enrico , Settimi Alessio G. , Pettenuzzo Silvia , Cappellin Luca , Galenda Alessandro , Famengo Alessia , Dabalà Manuele , Antoni Vanni , Navazio Lorella
Published: November 2021

Increasing evidence indicates that water activated by plasma discharge, termed as plasma-activated water (PAW), can promote plant growth and enhance plant defence responses. Nevertheless, the signalling pathways activated in plants in response to PAW are still largely unknown. In this work, we analysed the potential involvement of calcium as an intracellular messenger in the transduction of PAW by plants. To this aim, Arabidopsis thaliana (Arabidopsis) seedlings stably expressing the bioluminescent Ca2+ reporter aequorin in the cytosol were challenged with PAW generated by a plasma torch. Ca2+ measurement assays demonstrated the induction by PAW of rapid and sustained cytosolic Ca2+ elevations in Arabidopsis seedlings. The dynamics of the recorded Ca2+ signals were found to depend upon different parameters, such as the operational conditions of the torch, PAW storage, and dilution. The separate administration of nitrate, nitrite, and hydrogen peroxide at the same doses as those measured in the PAW did not trigger any detectable Ca2+ changes, suggesting that the unique mixture of different reactive chemical species contained in the PAW is responsible for the specific Ca2+ signatures. Unveiling the signalling mechanisms underlying plant perception of PAW may allow to finely tune its generation for applications in agriculture, with potential advantages in the perspective of a more sustainable agriculture.
Bearing Capacity and Mechanical Behavior of an Innovative Bamboo Fiber-Reinforced Polymer Tendon with U-Head
Authors: Li Xiang , Zhao Shuaiyu , Meng Xinmiao , Ji Xiaodong
Published: November 2021

The development of steel in engineering structures or components faces the problems of high cost and high carbon emission, which demands new materials used as reinforcement to be proposed and applied. Bamboo is a green and renewable natural material, and has higher tensile strength parallel to grain compared to wood. Based on the excellent mechanical properties in the parallel-to-grain direction of bamboo fiber, this paper proposed an innovative bamboo fiber-reinforced polymer tendon with U-head (BFRP tendon). To analyze the tension capacity and mechanical behavior of the BFRP tendon, the thin-shell model and thick-shell model have been proposed in succession. Both models were compared with the results of tensile test and good agreement was achieved in the stress distribution and the ultimate load. The tension capacity of the BFRP tendon has been proved to be reliable, which can give full play to the advantages of bamboo fiber.
Computational Simulation Tools to Support the Tissue Paper Furnish Management: Case Studies for the Optimization of Micro/Nano Cellulose Fibers and Polymer-Based Additives
Authors: Morais Flávia P. , Carta Ana M. M. S. , Amaral Maria E. , Curto Joana M. R.
Published: November 2021

Tissue paper production frequently combines two main types of raw materials: cellulose fibers from renewable sources and polymer-based additives. The development of premium products with improved properties and functionalities depends on the optimization of both. This work focused on the combination of innovative experimental and computational strategies to optimize furnish. The main goal was to improve the functional properties of the most suitable raw materials for tissue materials and develop new differentiating products with innovative features. The experimental plan included as inputs different fiber mixtures, micro/nano fibrillated cellulose, and biopolymer additives, and enzymatic and mechanical process operations. We present an innovative tissue paper simulator, the SimTissue, that we have developed, to establish the correlations between the tissue paper process inputs and the end-use paper properties. Case studies with industrial interest are presented in which the tissue simulator was used to design tissue paper materials with different fiber mixtures, fiber modification treatments, micro/nano fibrillated cellulose, and biopolymer formulations, and to estimate tissue softness, strength, and absorption properties. The SimTissue was able to predict and optimize a broader range of formulations containing micro/nanocellulose fibers, biopolymer additives, and treated-fiber mixtures, saving laboratory and industrial resources.
Photochemical Stability of a Cotton Fabric Surface Dyed with a Reactive Triphenodioxazine Dye
Authors: Rosu Liliana , Gavat Cristian-Catalin , Rosu Dan , Varganici Cristian-Dragos , Mustata Fanica
Published: November 2021

The paper describes the photochemical stability of a commercial triphenodioxazine dye (Reactive Blue_204) linked onto a cotton fabric. Preliminary studies have shown that as a result of irradiation, the dye and its photodegradation products can pass directly onto the skin under conditions that mimic human perspiration and cause side-effects. The cotton dyed fabric was photo irradiated at different time intervals. Standard methods were employed to evaluate the color strength at various levels of pH, temperature, dyeing contact time, and salt concentration. The influence of UV radiation at different doses (λ > 300 nm) on the structural and color modifications of the dyed cotton fabrics was studied. Structural modifications before and after irradiation were compared by applying FTIR, UV–Vis, and near infrared chemical imaging (NIR–CI) techniques. Color modifications were investigated with the CIELAB system. Color differences significantly increased with the irradiation dose. High irradiation doses caused changes in the dye structure.
Lasso Proteins—Unifying Cysteine Knots and Miniproteins
Authors: Greń Bartosz Ambroży , Dabrowski-Tumanski Pawel , Niemyska Wanda , Sulkowska Joanna Ida
Published: November 2021

Complex lasso proteins are a recently identified class of biological compounds that are present in considerable fraction of proteins with disulfide bridges. In this work, we look at complex lasso proteins as a generalization of well-known cysteine knots and miniproteins (lasso peptides). In particular, we show that complex lasso proteins with the same crucial topological features—cysteine knots and lasso peptides—are antimicrobial proteins, which suggests that they act as a molecular plug. Based on an analysis of the stability of the lasso piercing residue, we also introduce a method to determine which lasso motif is potentially functional. Using this method, we show that the lasso motif in antimicrobial proteins, as well in that in cytokines, is functionally relevant. We also study the evolution of lasso motifs, their conservation, and the usefulness of the lasso fingerprint, which extracts all topologically non-triviality concerning covalent loops. The work is completed by the presentation of extensive statistics on complex lasso proteins to analyze, in particular, the strange propensity for “negative” piercings. We also identify 21 previously unknown complex lasso proteins with an ester and a thioester bridge.
Composite Polyvinylpyrrolidone–Sodium Alginate—Hydroxyapatite Hydrogel Films for Bone Repair and Wound Dressings Applications
Authors: Fadeeva Inna V. , Trofimchuk Elena S. , Forysenkova Anna A. , Ahmed Abdulrahman I. , Gnezdilov Oleg I. , Davydova Galina A. , Kozlova Svetlana G. , Antoniac Aurora , Rau Julietta V.
Published: November 2021

Today, the synthesis of biocompatible and bioresorbable composite materials such as “polymer matrix-mineral constituent,” which stimulate the natural growth of living tissues and the restoration of damaged parts of the body, is one of the challenging problems in regenerative medicine. In this study, composite films of bioresorbable polymers of polyvinylpyrrolidone (PVP) and sodium alginate (SA) with hydroxyapatite (HA) were obtained. HA was introduced by two different methods. In one of them, it was synthesized in situ in a solution of polymer mixture, and in another one, it was added ex situ. Phase composition, microstructure, swelling properties and biocompatibility of films were investigated. The crosslinked composite PVP-SA-HA films exhibit hydrogel swelling characteristics, increasing three times in mass after immersion in a saline solution. It was found that composite PVP-SA-HA hydrogel films containing HA synthesized in situ exhibited acute cytotoxicity, associated with the presence of HA synthesis reaction byproducts—ammonia and ammonium nitrate. On the other hand, the films with HA added ex situ promoted the viability of dental pulp stem cells compared to the films containing only a polymer PVP-SA blend. The developed composite hydrogel films are recommended for such applications, such as membranes in osteoplastic surgery and wound dressing.
Synthesis of Chitosan-Silver Nanoparticle Composite Spheres and Their Antimicrobial Activities
Authors: Mirda Erisna , Idroes Rinaldi , Khairan Khairan , Tallei Trina Ekawati , Ramli Muliadi , Earlia Nanda , Maulana Aga , Idroes Ghazi Mauer , Muslem Muslem , Jalil Zulkarnain
Published: November 2021

Synthesis of silver nanoparticles–chitosan composite particles sphere (AgNPs-chi-spheres) has been completed and its characterization was fulfilled by UV–vis spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and zetasizer nano. UV–vis spectroscopy characterization showed that AgNPs-chi-spheres gave optimum absorption at a wavelength of 410 nm. The XRD spectra showed that the structure of AgNPs-chi-spheres were crystalline and spherical. Characterization by SEM showed that AgNPs-chi-spheres, with the addition of 20% of NaOH, resulted in the lowest average particle sizes of 46.91 nm. EDX analysis also showed that AgNPs-chi-spheres, with the addition of a 20% NaOH concentration, produced particles with regular spheres, a smooth and relatively nonporous structure. The analysis using zetasizer nano showed that the zeta potential value and the polydispersity index value of the AgNPs-chi-sphere tended to increase with an increased NaOH concentration. The results of the microbial activity screening showed that the AgNP-chi-Spheres with highest concentration of NaOH, produced the highest inhibition zone diameters against S. aureus, E. coli, and C. albicans, with inhibition zone diameters of 19.5, 18.56, and 12.25 nm, respectively.
Application of Amberlite IRA 402 Resin Adsorption and Laccase Treatment for Acid Blue 113 Removal from Aqueous Media
Authors: Marin Nicoleta Mirela , Stanculescu Ioana
Published: November 2021

Despite Acid Blue 113 (AB 113)’s extensive use and negative environmental impact, very few studies have focused on its efficient and environmentally friendly removal. This research aims the removal of AB 113 from environmental aqueous media and its consequent enzymatic biodegradation. A strongly basic anion exchange resin in Cl− form, Amberlite IRA 402 (IRA 402(Cl−)) was used for AB 113 adsorption and a laccase was used to further biodegrade it. For the first time, two novel, efficient and environmentally friendly physical–chemical and biological assays for AB 113 wastewater removal and subsequent biodegradation were combined. The adsorption of AB 113 onto IRA 402(Cl−) was tested in batch and continuous flux modes. Influence of contact time, concentration and desorption in acidic media were evaluated. The kinetic data were best modulated by the Lagergren model with R2 = 0.9275. The Langmuir isotherm model best fitted the experimental data, and the maximum adsorption capacity was 130 mg/g. Dye, resin and AB113 loaded resin were characterized by thermogravimetry and FTIR to evaluate their physical chemical properties modification. Based on the performed studies, a consecutive methodology is proposed, incorporating the ion exchange process in the first stage and the biodegradation process in the second. Thus, in the second stage the residual concentration of AB 113 is reduced by an efficient bio-degradation process produced by the laccase at pH = 4.
Polyethylene Oxide as a Multifunctional Binder for High-Performance Ternary Layered Cathodes
Authors: Mo Jinshan , Zhang Dongmei , Sun Mingzhe , Liu Lehao , Hu Weihao , Jiang Bing , Chu Lihua , Li Meicheng
Published: November 2021

Nickel cobalt manganese ternary cathode materials are some of the most promising cathode materials in lithium-ion batteries, due to their high specific capacity, low cost, etc. However, they do have a few disadvantages, such as an unstable cycle performance and a poor rate performance. In this work, polyethylene oxide (PEO) with high ionic conductance and flexibility was utilized as a multifunctional binder to improve the electrochemical performance of LiNi0.6Co0.2Mn0.2O2 cathode materials. Scanning electron microscopy showed that the addition of PEO can greatly improve the adhesion of the electrode components and simultaneously enhance the integrity of the electrode. Thus, the PEO-based electrode (20 wt% PEO in PEO/PVDF) shows a high electronic conductivity of 19.8 S/cm, which is around 15,000 times that of the pristine PVDF-based electrode. Moreover, the PEO-based electrode exhibits better cycling stability and rate performance, i.e., the capacity increases from 131.1 mAh/g to 147.3 mAh/g at 2 C with 20 wt% PEO addition. Electrochemical impedance measurements further indicate that the addition of the PEO binder can reduce the electrode resistance and protect the LiNi0.6Co0.2Mn0.2O2 cathode materials from the liquid electrolyte attack. This work offers a simple yet effective method to improve the cycling performance of the ternary cathode materials by adding an appropriate amount of PEO as a binder in the electrode fabrication process.
Behavior of CFRP-Confined Sand-Based Material Columns under Axial Compression
Authors: Li Guodong , Liu Honglin , Deng Wentao , Wang Hongzhi , Yan Haitian
Published: November 2021

This paper presents an innovative pumpable standing support designed for underground mines located in the arid and semi-arid deserts of the Gobi region with a shortage of water resources. The exterior shell of this pumpable standing support is made of carbon fiber-reinforced polymer (CFRP), while the infill material is a sand-based material (SBM). As the novel backfill material, SBM is the combination of high-water cementing material and desert sand. A series of experimental tests were conducted to obtain the mechanical response mechanism of this novel pumpable standing support under uniaxial compression. Test variables investigated in this research covered the water-to-powder ratio of the cementing material, the mixing amount of sand, and the thickness of the CFRP tube. Test results confirmed that the CFRP-confined SBM columns exhibited typical strain hardening behavior with the acceptable axial deformation. It was also demonstrated that using high-strength cementing material, a thicker CFRP tube, and a high mixing amount of sand effectively increased the bearing capacity of the CFRP-confined SBM column. Except for the exemplary structural behavior, the consumption of high-water cementing materials of the novel pumpable standing support is smaller than that of its counterparts made of pure cementing material, when specimens with the same mechanical performance are compared.
Sensitive Detection of Thiourea Hazardous Toxin with Sandwich-Type Nafion/CuO/ZnO Nanospikes/Glassy Carbon Composite Electrodes
Authors: Rahman Mohammed M. , Alam Md M. , Alfaifi Sulaiman Y. M. , Asiri Abdullah M. , Ali Meser M.
Published: November 2021

In this research study, we developed a voltammetric electrochemical sensor probe with a copolymer Nafion (Sulfonated Tetrafluoroethylene-based Fluoro-polymer) decorated with hydrothermally prepared sandwich-type CuO/ZnO nanospikes (NSs) onto a glassy carbon electrode (GCE) for reliable thiourea (TU) detection. The detailed characterizations in terms of structural morphology, binding energy, elemental compositions, grain size and crystallinity for synthesized NSs were performed by field emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) analysis, respectively. The differential pulse voltammetric (DPV) analysis for TU showed good linearity at current-versus-TU concentration on the calibration plot in the 0.15~1.20 mM range, which is defined as a dynamic detection range (LDR) of TU in a phosphate buffer solution. Considering the slope of LDR over the GCE-coated NSs surface area (0.0316 cm2), the TU sensor sensitivity (0.4122 µA µM−1 cm−2) was obtained. Besides this, the low limit (LOD) for TU detection was calculated and found to be 23.03 ± 1.15 µM. The fabricated Nafion/CuO/ZnO NSs/GCE sensor probe was created as a reliable sensor based on reproducibility, interference effect, stability and response time. Real bio-samples were investigated and the results confirm the anticipated reliability of the TU sensor probe. Thus, this is a noble way to develop enzyme-free electrochemical sensors that could be an alternative approach for the detection of chemicals in the field of enzyme-free biosensor development technology.
Towards High Efficiency CO2 Utilization by Glow Discharge Plasma
Authors: Renninger Stephan , Rößner Paul , Stein Jan , Lambarth Maike , Birke Kai Peter
Published: November 2021

Plasma technology reaches rapidly increasing efficiency in catalytic applications. One such application is the splitting reaction of CO2 to oxygen and carbon monoxide. This reaction could be a cornerstone of power-to-X processes that utilize electricity to produce value-added compounds such as chemicals and fuels. However, it poses problems in practice due to its highly endothermal nature and challenging selectivity. In this communication a glow discharge plasma reactor is presented that achieves high energy efficiency in the CO2 splitting reaction. To achieve this, a magnetic field is used to increase the discharge volume. Combined with laminar gas flow, this leads to even energy distribution in the working gas. Thus, the reactor achieves very high energy efficiency of up to 45% while also reaching high CO2 conversion efficiency. These results are briefly explained and then compared to other plasma technologies. Lastly, cutting edge energy efficiencies of competing technologies such as CO2 electrolysis are discussed in comparison.
A Comparison of Uric Acid Optical Detection Using as Sensitive Materials an Amino-Substituted Porphyrin and Its Nanomaterials with CuNPs, PtNPs and Pt@CuNPs
Authors: Epuran Camelia , Fratilescu Ion , Anghel Diana , Birdeanu Mihaela , Orha Corina , Fagadar-Cosma Eugenia
Published: November 2021

Hybrid nanomaterials consisting in 5,10,15,20-tetrakis(4-amino-phenyl)-porphyrin (TAmPP) and copper nanoparticles (CuNPs), platinum nanoparticles (PtNPs), or both types (Pt@CuNPs) were obtained and tested for their capacity to optically detect uric acid from solutions. The introduction of diverse metal nanoparticles into the hybrid material proved their capacity to improve the detection range. The detection was monitored by using UV-Vis spectrophotometry, and differences between morphology of the materials were performed using atomic force microscopy (AFM). The hybrid material formed between porphyrin and PtNPs hasthe best and most stable response for uric acid detection in the range of 6.1958 × 10−6–1.5763 × 10−5 M, even in the presence of very high concentrations of the interference species present in human environment.
Introduction: New Discoveries and Theoretical Implications for the Last Foraging and First Farming in East Asia
Authors: Yu Pei-Lin , Ikeya Kazunobu , Zhang Meng
Published: November 2021

Worldwide, scientific understanding about domestication and the origins of food production is undergoing rapid change based on new data from discoveries in paleoclimates and environments, paleobiology, and archaeology [...]
Multi-Taxa Neo-Taphonomic Analysis of Bone Remains from Barn Owl Pellets and Cross-Validation of Observations: A Case Study from Dominica (Lesser Antilles)
Authors: Stoetzel Emmanuelle , Bochaton Corentin , Bailon Salvador , Cochard David , Gala Monica , Laroulandie Véronique
Published: November 2021

Paleo- and neo-taphonomic analyses of bone assemblages rarely consider all the occurring taxa in a single study and works concerning birds of prey as accumulators of microvertebrate bone remains mostly focus on small mammals such as rodents and soricomorphs. However, raptors often hunt and consume a large range of taxa, including vertebrates such as small mammals, fishes, amphibians, squamates and birds. Bone remains of all these taxonomic groups are numerous in many paleontological and archaeological records, especially in cave deposits. To better characterize the predators at the origin of fossil and sub-fossil microvertebrate accumulations and the taphonomic history of the deposit, it is thus mandatory to conduct global and multi-taxa taphonomic approaches. The aim of this study is to provide an example of such a global approach through the investigation of a modern bone assemblage from a sample of pellets produced by the Lesser Antillean Barn Owl (Tyto insularis) in the island of Dominica. We propose a new methodology that allows us to compare different taxa (rodents, bats, squamates and birds) and to experiment with a cross-validation process using two observers for each taxonomic group to test the reliability of the taphonomic observations.
Hebrew, Hebrews, Hubris?: Diagnosing Race and Religion in the Time of COVID-19
Authors: Newton Richard
Published: November 2021

This thought experiment in comparison ponders a Black man’s conviction that his Hebrew identity would make him immune to COVID-19. Surfacing the history of the claims and the scholar’s own suspicions, the paper examines the layered politics of identification. Contra an essentialist understanding of the terms, “Hebrew” and “Hebrews” are shown to be classificatory events, ones imbricated in the dynamics of racecraft. Furthermore, a contextualization of the “race religion” model of 19th century scholarship, 20th century US religio-racial movements, and the complicated legacy of Tuskegee in 21st century Black vaccine hesitancy help to outline the need for inquisitiveness rather than hubris in matters of comparison. In so doing, this working paper advances a model of the public scholar as a questioner of categories and a diagnostician of classification.
Tide-Inspired Path Planning Algorithm for Autonomous Vehicles
Authors: Kurdi Heba , Almuhalhel Shaden , Elgibreen Hebah , Qahmash Hajar , Albatati Bayan , Al-Salem Lubna , Almoaiqel Ghada
Published: November 2021

With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios.
Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand
Authors: Hutasavi Sirikul , Chen Dongmei
Published: November 2021

The intensive industrial development in special economic zones, such as Thailand’s Eastern Economic Corridor, increases energy consumption, leading to an imbalance of energy supply and a challenge for energy management. Electricity consumption at a local level is crucial for utility planners to manage and invest in the electrical grid. With this study, we propose an electricity consumption estimation model at the district level using machine learning with publicly available statistical data and built-up area (BU), area of lit (AL), and sum of light intensity (SL) data extracted from Landsat 8 and Suomi NPP satellite nighttime light images. The models created from three machine learning algorithms, which included Multiple Linear Regression (MR), Decision Tree (DT), and Support Vector Regression (SVR), were compared. The results show that (1) electricity consumption is highly correlated with SL, AL, and BU; and (2) the DT model demonstrated a better performance in predicting local electricity consumption when compared to MR and SVR with the lowest error rate and highest R2. The local government in developing countries with limited data and financial resources can adopt the proposed approach to benefit from utilizing commonly available remote sensing and statistical data with simple machine learning models such as DT (regression method) for sustainable electricity management.
Tree Species Classification in a Temperate Mixed Mountain Forest Landscape Using Random Forest and Multiple Datasets
Authors: Hologa Rafael , Scheffczyk Konstantin , Dreiser Christoph , Gärtner Stefanie
Published: November 2021

For monitoring protected forest landscapes over time it is essential to follow changes in tree species composition and forest dynamics. Data driven remote sensing methods provide valuable options if terrestrial approaches for forest inventories and monitoring activities cannot be applied efficiently due to restrictions or the size of the study area. We demonstrate how species can be detected at a single tree level utilizing a Random Forest (RF) model using the Black Forest National Park as an example of a Central European forest landscape with complex relief. The classes were European silver fir (Abies alba, AA), Norway spruce (Picea abies, PA), Scots pine (Pinus sylvestris, PS), European larch (Larix decidua including Larix kampferii, LD), Douglas fir (Pseudotsuga menziesii, PM), deciduous broadleaved species (DB) and standing dead trees (snags, WD). Based on a multi-temporal (leaf-on and leaf-off phenophase) and multi-spectral mosaic (R-G-B-NIR) with 10 cm spatial resolution, digital elevation models (DTM, DSM, CHM) with 40 cm spatial resolution and a LiDAR dataset with 25 pulses per m2, 126 variables were derived and used to train the RF algorithm with 1130 individual trees. The main objective was to determine a subset of meaningful variables for the RF model classification on four heterogeneous test sites. Using feature selection techniques, mainly passive optical variables from the leaf-off phenophase were considered due to their ability to differentiate between conifers and the two broader classes. An examination of the two phenological phases (using the difference of the respective NDVIs) is important to clearly distinguish deciduous trees from other classes including snags (WD). We also found that the variables of the first derivation of NIR and the tree metrics play a crucial role in discriminating PA und PS. With this unique set of variables some classes can be differentiated more reliably, especially LD and DB but also AA, PA and WD, whereas difficulties exist in identifying PM and PS. Overall, the non-parametric object-based approach has proved to be highly suitable for accurately detecting (OA: 89.5%) of the analyzed classes. Finally, the successful classification of complex 265 km2 study area substantiates our findings.
Stitching and Geometric Modeling Approach Based on Multi-Slice Satellite Images
Authors: Wang Longhui , Zhang Yan , Wang Tao , Zhang Yongsheng , Zhang Zhenchao , Yu Ying , Li Lei
Published: November 2021

Time delay and integration (TDI) charge-coupled device (CCD) is an image sensor for capturing images of moving objects at low light levels. This study examines the model construction of stitched TDI CCD original multi-slice images. The traditional approaches, for example, include the image-space-oriented algorithm and the object-space-oriented algorithm. The former indicates concise principles and high efficiency, whereas the panoramic stitching images lack the clear geometric relationships generated from the image-space-oriented algorithm. Similarly, even though the object-space-oriented algorithm generates an image with a clear geometric relationship, it is time-consuming due to the complicated and intensive computational demands. In this study, we developed a multi-slice satellite images stitching and geometric model construction method. The method consists of three major steps. First, the high-precision reference data assist in block adjustment and obtain the original slice image bias-corrected RFM to perform multi-slice image block adjustment. The second process generates the panoramic stitching image by establishing the image coordinate conversion relationship from the panoramic stitching image to the original multi-slice images. The final step is dividing the panoramic stitching image uniformly into image grids and employing the established image coordinate conversion relationship and the original multi-slice image bias-corrected RFM to generate a virtual control grid to construct the panoramic stitching image RFM. To evaluate the performance, we conducted experiments using the Tianhui-1(TH-1) high-resolution image and the Ziyuan-3(ZY-3) triple liner-array image data. The experimental results show that, compared with the object-space-oriented algorithm, the stitching accuracy loss of the generated panoramic stitching image was only 0.2 pixels and that the mean value was 0.799798 pixels, achieving the sub-pixel stitching requirements. Compared with the object-space-oriented algorithm, the RFM positioning difference of the panoramic stitching image was within 0.3 m, which achieves equal positioning accuracy.
Monitoring the Dynamics of Formby Sand Dunes Using Airborne LiDAR DTMs
Authors: Mahmoud Ahmed Mutasim Abdalla , Hussain Ekbal , Novellino Alessandro , Psimoulis Panos , Marsh Stuart
Published: November 2021

Coastal dunes play an important role in coastal erosion risk management, where they act as a dynamic natural sea defence line. Formby coast is part of the Sefton coast in the Northwest of England and is one of the largest and most rapidly evolving sand dune systems in the UK. Such dune systems require continuous comprehensive monitoring activity to understand their dynamics. In this research, we investigate the use of airborne LiDAR digital terrain model DTMs for monitoring the dynamics of the sand dunes at Formby between 1999 and 2020. We found that the rate of elevation change for the beach and the dune areas ranges from −0.78 to 0.02 m/year and −0.92 to 0.73 m/year, respectively. The beach and the frontal dunes have had significant sand erosion, while the inner dunes gained sand during the measurement period. Vegetated areas remained unchanged due to the impact of vegetation in stabilizing the movement of the dunes. Formby beach had a volume loss of about 907,000 m3 in the last 21 years, while the dunes had a volume increase of about 1,049,000 m3 over the same period. The total volume of the entire dune system, consisting of both the beach and dune areas, remained unchanged, which indicates that the growth of the inland dunes is fed by sand from the beach. All the volumetric changes occurred due to sand redistribution within the system, with erosion along the beach, and deposition and erosion in the dune areas.
An Object- and Topology-Based Analysis (OTBA) Method for Mapping Rice-Crayfish Fields in South China
Authors: Wei Haodong , Hu Qiong , Cai Zhiwen , Yang Jingya , Song Qian , Yin Gaofei , Xu Baodong
Published: November 2021

The rice-crayfish field (i.e., RCF), a newly emerging rice cultivation pattern, has greatly expanded in China in the last decade due to its significant ecological and economic benefits. The spatial distribution of RCFs is an important dataset for crop planting pattern adjustment, water resource management and yield estimation. Here, an object- and topology-based analysis (OTBA) method, which considers spectral-spatial features and the topological relationship between paddy fields and their enclosed ditches, was proposed to identify RCFs. First, we employed an object-based method to extract crayfish breeding ditches using very high-resolution images. Subsequently, the paddy fields that provide fodder for crayfish were identified according to the topological relationship between the paddy field and circumjacent crayfish ditch. The extracted ditch objects together with those paddy fields were merged to derive the final RCFs. The performance of the OTBA method was carefully evaluated using the RCF and non-RCF samples. Moreover, the effects of different spatial resolutions, spectral bands and temporal information on RCF identification were comprehensively investigated. Our results suggest the OTBA method performed well in extracting RCFs, with an overall accuracy of 91.77%. Although the mapping accuracies decreased as the image spatial resolution decreased, satisfactory RCF mapping results (>80%) can be achieved at spatial resolutions greater than 2 m. Additionally, we demonstrated that the mapping accuracy can be improved by more than 10% when near-infrared (NIR) band information was involved, indicating the necessity of the NIR band when selecting images to derive reliable RCF maps. Furthermore, the images acquired in the rice growth phase are recommended to maximize the differences of spectral characteristics between paddy fields and ditches. These promising findings suggest that the OTBA approach performs well for mapping RCFs in areas with fragmented agricultural landscapes, which provides fundamental information for further agricultural land use and water resources management.
Sea-Level Fingerprints Due to Present-Day Water Mass Redistribution in Observed Sea-Level Data
Authors: Moreira Lorena , Cazenave Anny , Barnoud Anne , Chen Jianli
Published: November 2021

Satellite altimetry over the oceans shows that the rate of sea-level rise is far from uniform, with reported regional rates up to two to three times the global mean rate of rise of ~3.3 mm/year during the altimeter era. The mechanisms causing the regional variations in sea-level trends are dominated by ocean temperature and salinity changes, and other processes such as ocean mass redistribution as well as solid Earth’s deformations and gravitational changes in response to past and ongoing mass redistributions caused by land ice melt and terrestrial water storage changes (respectively known as Glacial Isostatic Adjustment (GIA) and sea-level fingerprints). Here, we attempt to detect the spatial trend patterns of the fingerprints associated with present-day land ice melt and terrestrial water mass changes, using satellite altimetry-based sea-level grids corrected for the steric component. Although the signal-to-noise ratio is still very low, a statistically significant correlation between altimetry-based sea-level and modelled fingerprints is detected in some ocean regions. We also examine spatial trend patterns in observed GRACE ocean mass corrected for atmospheric and oceanic loading and find that some oceanic regions are dominated by the fingerprints of present-day water mass redistribution.
Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning
Authors: Ofori-Ampofo Stella , Pelletier Charlotte , Lang Stefan
Published: November 2021

Crop maps are key inputs for crop inventory production and yield estimation and can inform the implementation of effective farm management practices. Producing these maps at detailed scales requires exhaustive field surveys that can be laborious, time-consuming, and expensive to replicate. With a growing archive of remote sensing data, there are enormous opportunities to exploit dense satellite image time series (SITS), temporal sequences of images over the same area. Generally, crop type mapping relies on single-sensor inputs and is solved with the help of traditional learning algorithms such as random forests or support vector machines. Nowadays, deep learning techniques have brought significant improvements by leveraging information in both spatial and temporal dimensions, which are relevant in crop studies. The concurrent availability of Sentinel-1 (synthetic aperture radar) and Sentinel-2 (optical) data offers a great opportunity to utilize them jointly; however, optimizing their synergy has been understudied with deep learning techniques. In this work, we analyze and compare three fusion strategies (input, layer, and decision levels) to identify the best strategy that optimizes optical-radar classification performance. They are applied to a recent architecture, notably, the pixel-set encoder–temporal attention encoder (PSE-TAE) developed specifically for object-based classification of SITS and based on self-attention mechanisms. Experiments are carried out in Brittany, in the northwest of France, with Sentinel-1 and Sentinel-2 time series. Input and layer-level fusion competitively achieved the best overall F-score surpassing decision-level fusion by 2%. On a per-class basis, decision-level fusion increased the accuracy of dominant classes, whereas layer-level fusion improves up to 13% for minority classes. Against single-sensor baseline, multi-sensor fusion strategies identified crop types more accurately: for example, input-level outperformed Sentinel-2 and Sentinel-1 by 3% and 9% in F-score, respectively. We have also conducted experiments that showed the importance of fusion for early time series classification and under high cloud cover condition.
Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems
Authors: Lu Bing , He Yuhong
Published: November 2021

Chlorophyll is an essential vegetation pigment influencing plant photosynthesis rate and growth conditions. Remote sensing images have been widely used for mapping vegetation chlorophyll content in different ecosystems (e.g., farmlands, forests, grasslands, and wetlands) for evaluating vegetation growth status and productivity of these ecosystems. Compared to farmlands and forests that are more homogeneous in terms of species composition, grasslands and wetlands are more heterogeneous with highly mixed species (e.g., various grass, forb, and shrub species). Different species contribute differently to the ecosystem services, thus, monitoring species-specific chlorophyll content is critical for better understanding their growth status, evaluating ecosystem functions, and supporting ecosystem management (e.g., control invasive species). However, previous studies in mapping chlorophyll content in heterogeneous ecosystems have rarely estimated species-specific chlorophyll content, which was partially due to the limited spatial resolution of remote sensing images commonly used in the past few decades for recognizing different species. In addition, many previous studies have used one universal model built with data of all species for mapping chlorophyll of the entire study area, which did not fully consider the impacts of species composition on the accuracy of chlorophyll estimation (i.e., establishing species-specific chlorophyll estimation models may generate higher accuracy). In this study, helicopter-acquired high-spatial resolution hyperspectral images were acquired for species classification and species-specific chlorophyll content estimation. Four estimation models, including a universal linear regression (LR) model (i.e., built with data of all species), species-specific LR models (i.e., built with data of each species, respectively), a universal random forest regression (RFR) model, and species-specific RFR models, were compared to determine their performance in mapping chlorophyll and to evaluate the impacts of species composition. The results show that species-specific models performed better than the universal models, especially for species with fewer samples in the dataset. The best performed species-specific models were then used to generate species-specific chlorophyll content maps using the species classification results. Impacts of species composition on the retrieval of chlorophyll content were further assessed to support future chlorophyll mapping in heterogeneous ecosystems and ecosystem management.
A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data
Authors: Lezama Valdes Lilian-Maite , Katurji Marwan , Meyer Hanna
Published: November 2021

To monitor environmental and biological processes, Land Surface Temperature (LST) is a central variable, which is highly variable in space and time. This particularly applies to the Antarctic Dry Valleys, which host an ecosystem highly adapted to the extreme conditions in this cold desert. To predict possible climate induced changes on the Dry Valley ecosystem, high spatial and temporal resolution environmental variables are needed. Thus we enhanced the spatial resolution of the MODIS satellite LST product that is sensed sub-daily at a 1 km spatial resolution to a 30 m spatial resolution. We employed machine learning models that are trained using Landsat 8 thermal infrared data from 2013 to 2019 as a reference to predict LST at 30 m resolution. For the downscaling procedure, terrain derived variables and information on the soil type as well as the solar insolation were used as potential predictors in addition to MODIS LST. The trained model can be applied to all available MODIS scenes from 1999 onward to develop a 30 m resolution LST product of the Antarctic Dry Valleys. A spatio-temporal validation revealed an R2 of 0.78 and a RMSE of 3.32 ∘C. The downscaled LST will provide a valuable surface climate data set for various research applications, such as species distribution modeling, climate model evaluation, and the basis for the development of further relevant environmental information such as the surface moisture distribution.
CNN-Based Spectral Super-Resolution of Panchromatic Night-Time Light Imagery: City-Size-Associated Neighborhood Effects
Authors: Rybnikova Nataliya , Mirkes Evgeny M. , Gorban Alexander N.
Published: November 2021

Data on artificial night-time light (NTL), emitted from the areas, and captured by satellites, are available at a global scale in panchromatic format. In the meantime, data on spectral properties of NTL give more information for further analysis. Such data, however, are available locally or on a commercial basis only. In our recent work, we examined several machine learning techniques, such as linear regression, kernel regression, random forest, and elastic map models, to convert the panchromatic NTL images into colored ones. We compared red, green, and blue light levels for eight geographical areas all over the world with panchromatic light intensities and characteristics of built-up extent from spatially corresponding pixels and their nearest neighbors. In the meantime, information from more distant neighboring pixels might improve the predictive power of models. In the present study, we explore this neighborhood effect using convolutional neural networks (CNN). The main outcome of our analysis is that the neighborhood effect goes in line with the geographical extent of metropolitan areas under analysis: For smaller areas, optimal input image size is smaller than for bigger ones. At that, for relatively large cities, the optimal input image size tends to differ for different colors, being on average higher for red and lower for blue lights. Compared to other machine learning techniques, CNN models emerged comparable in terms of Pearson’s correlation but showed performed better in terms of WMSE, especially for testing datasets.