bluuebunny/crossref_metadata_2025 huggingface dataset:
Arabinogalactan Structures of Repetitive Serine-Hydroxyproline Glycomodule Expressed by Arabidopsis Cell Suspension Cultures
Authors: Tan Li , Xu Jianfeng , Held Michael , Lamport Derek T. A. , Kieliszewski Marcia
Published: February 2023

Arabinogalactan-proteins (AGPs) are members of the hydroxyproline-rich glycoprotein (HRGP) superfamily. They are heavily glycosylated with arabinogalactans, which are usually composed of a β-1,3-linked galactan backbone with 6-O-linked galactosyl, oligo-1,6-galactosyl, or 1,6-galactan side chains that are further decorated with arabinosyl, glucuronosyl, rhamnosyl, and/or fucosyl residues. Here, our work with Hyp-O-polysaccharides isolated from (Ser-Hyp)32-EGFP (enhanced green fluorescent protein) fusion glycoproteins overexpressed in transgenic Arabidopsis suspension culture is consistent with the common structural features of AGPs isolated from tobacco. In addition, this work confirms the presence of β-1,6-linkage on the galactan backbone identified previously in AGP fusion glycoproteins expressed in tobacco suspension culture. Furthermore, the AGPs expressed in Arabidopsis suspension culture lack terminal-rhamnosyl residues and have a much lower level of glucuronosylation compared with those expressed in tobacco suspension culture. These differences not only suggest the presence of distinct glycosyl transferases for AGP glycosylation in the two systems, but also indicate the existence of minimum AG structures for type II AG functional features.
Differential Expression of miRNAs Involved in Response to Candidatus Liberibacter asiaticus Infection in Mexican Lime at Early and Late Stages of Huanglongbing Disease
Authors: Bojórquez-Orozco Ana Marlenne , Arce-Leal Ángela Paulina , Montes Ricardo A. Chávez , Santos-Cervantes María Elena , Cruz-Mendívil Abraham , Méndez-Lozano Jesús , Castillo Araceli G. , Rodríguez-Negrete Edgar A. , Leyva-López Norma Elena
Published: February 2023

Huanglongbing (HLB) is one of the most destructive diseases threatening citriculture worldwide. This disease has been associated with α-proteobacteria species, namely Candidatus Liberibacter. Due to the unculturable nature of the causal agent, it has been difficult to mitigate the disease, and nowadays a cure is not available. MicroRNAs (miRNAs) are key regulators of gene expression, playing an essential role in abiotic and biotic stress in plants including antibacterial responses. However, knowledge derived from non-model systems including Candidatus Liberibacter asiaticus (CLas)-citrus pathosystem remains largely unknown. In this study, small RNA profiles from Mexican lime (Citrus aurantifolia) plants infected with CLas at asymptomatic and symptomatic stages were generated by sRNA-Seq, and miRNAs were obtained with ShortStack software. A total of 46 miRNAs, including 29 known miRNAs and 17 novel miRNAs, were identified in Mexican lime. Among them, six miRNAs were deregulated in the asymptomatic stage, highlighting the up regulation of two new miRNAs. Meanwhile, eight miRNAs were differentially expressed in the symptomatic stage of the disease. The target genes of miRNAs were related to protein modification, transcription factors, and enzyme-coding genes. Our results provide new insights into miRNA-mediated regulation in C. aurantifolia in response to CLas infection. This information will be useful to understand molecular mechanisms behind the defense and pathogenesis of HLB.
Phytochemical Investigation of Myrcianthes cisplatensis: Structural Characterization of New p-Coumaroyl Alkylphloroglucinols and Antimicrobial Evaluation against Staphylococcus aureus
Authors: Guzzo Francesca , Buommino Elisabetta , Landrum Leslie , Russo Rosita , Lembo Francesca , Fiorentino Antonio , D’Abrosca Brigida
Published: February 2023

Species of Myrtaceae Juss., the ninth largest family of flowering plants, are a valuable source of bioactive specialized metabolites. A leading position belongs to phloroglucinol derivatives, thanks to their unusual structural features and biological and pharmacological properties. Myrcianthes cisplatensis (Cambess.) O. Berg, a common tree on the banks of rivers and streams of Uruguay, southern Brazil, and northern Argentina, with aromatic leaves, is known as a diuretic, febrifuge, tonic, and good remedy for lung and bronchial diseases. Despite knowledge about traditional use, few data on its phytochemical properties have been reported in the literature. The methanol extract of M. cisplatensis, grown in Arizona, USA, was first partitioned between dichloromethane and water and then with ethyl acetate. The enriched fractions were evaluated using a broth microdilution assay against Staphylococcus aureus ATCC 29213 and 43300 (methicillin-resistant S. aureus (MRSA)). The potential antimicrobial activity seemed to increase in the dichloromethane extract, with a MIC value of 16 µg/mL against both strains. Following a bio-guided approach, chromatographic techniques allowed for isolating three coumarin derivatives, namely endoperoxide G3, catechin, and quercitrin, and four new p-coumaroyl alkylphloroglucinol glucosides, named p-coumaroylmyrciacommulone A-D. Their structures were characterized through spectroscopic techniques: 2D-NMR experiments (HSQC, HMBC, and HSQC-TOCSY) and spectrometric analyses (HR-MS). The antimicrobial assessment of pure compounds against S. aureus ATCC 29213 and ATCC 43300 demonstrated the best activity for p-coumaroylmyrciacommulone C and D with the growth inhibition of 50% at 32 µg/mL against both strains of S. aureus.
Validation of a New HPLC-DAD Method to Quantify 3-Deoxyanthocyanidins Based on Carajurin in Medicinal Plants and for Chemical Ecology Studies
Authors: Souza Paulo Victor R. , Martins Vanessa G. F. C. , Chagas Maria do Socorro S. , Moragas-Tellis Carla J. , Behrens Maria D. , Moreira Davyson L.
Published: February 2023

Anthocyanidins are found in nature mainly as pelargonidin, cyanidin, peonidin, delphinidin, petunidin, and malvidin derivatives. These compounds are found free or as glycoside derivatives which are responsible for the color (red, blue, and violet) of some foods and are responsible for attracting seed dispersers. They are grouped into 3-hydroxyanthocyanidins, 3-deoxyanthocyanidins (3D-anth), and O-methylated anthocyanidins. A new method was developed and validated to quantify 3D-anth in plant-rich extracts. To test the new method, Arrabidaea chica Verlot was selected as it is widely used in folk medicine, and it is rich in 3D-anth. The new method was developed by HPLC-DAD and expressed 3D-anth as carajurin content. Carajurin was chosen as the reference standard due to its role as a biological marker for the antileishmanial activity for A. chica. The selected method used a silica-based phenyl column, a mobile phase composed of potassium dihydrogen phosphate buffer, acetonitrile, and methanol, in a gradient elution mode and detection at 480 nm. The method reliability was confirmed by verifying selectivity, linearity, precision, recovery, and robustness. This method contributes to quality control and development of a possible active pharmaceutical ingredient from A. chica as well as it can be used to evaluate 3D-anth in plant extracts with chemical ecology interest.
Review of Aging Evaluation Methods for Silicone Rubber Composite Insulators
Authors: Zeng Zhou , Guo Pan , Zhang Ruoshuang , Zhao Zhirui , Bao Jiankang , Wang Qian , Xu Zheng
Published: February 2023

Silicone rubber insulation material is widely used for the external insulation of power systems. During the continuous service of a power grid, it will be seriously aged due to the influence of high voltage electric fields and harsh climate environments, which will reduce its insulation performance and service life and cause transmission line failure. How to evaluate the aging performance of silicone rubber insulation materials scientifically and accurately is a hot and difficult issue in the industry. Starting from the composite insulator, which is the most widely used insulating device of silicone rubber insulation materials, this paper expounds the aging mechanism of silicone rubber materials, analyzes the applicability and effectiveness of various existing aging tests and evaluation methods, especially discusses the magnetic resonance detection methods emerging in recent years, and finally summarizes the characterization and evaluation technology of the aging state of silicone rubber insulation materials.
Wastewater Treatment of Real Effluents by Microfiltration Using Poly(vinylidene fluoride–hexafluoropropylene) Membranes
Authors: Zioui Djamila , Martins Pedro Manuel , Aoudjit Lamine , Salazar Hugo , Lanceros-Méndez Senentxu
Published: February 2023

Over the last decades, the growing contamination of wastewater, mainly caused by industrial processes, improper sewage, natural calamities, and a variety of anthropogenic activities, has caused an increase in water-borne diseases. Notably, industrial applications require careful consideration as they pose significant threats to human health and ecosystem biodiversity due to the production of persistent and complex contaminants. The present work reports on the development, characterization, and application of a poly (vinylidene fluoride—hexafluoropropylene) (PVDF-HFP) porous membrane for the remediation of a wide range of contaminants from wastewater withdrawn from industrial applications. The PVDF-HFP membrane showed a micrometric porous structure with thermal, chemical, and mechanical stability and a hydrophobic nature, leading to high permeability. The prepared membranes exhibited simultaneous activity on the removal of organic matter (total suspended and dissolved solids, TSS, and TDS, respectively), the mitigation of salinity in 50%, and the effective removal of some inorganic anions and heavy metals, achieving efficiencies around 60% for nickel, cadmium, and lead. The membrane proved to be a suitable approach for wastewater treatment, as it showed potential for the simultaneous remediation of a wide range of contaminants. Thus, the as-prepared PVDF-HFP membrane and the designed membrane reactor represent an efficient, straightforward, and low-cost alternative as a pretreatment step for continuous treatment processes for simultaneous organic and inorganic contaminants’ remediation in real industrial effluent sources.
Temperature Responsive Copolymers Films of Polyether and Bio-Based Polyamide Loaded with Imidazolium Ionic Liquids for Smart Packaging Applications
Authors: Zampino Daniela C. , Clarizia Gabriele , Bernardo Paola
Published: February 2023

Temperature-responsive materials are highly interesting for temperature-triggered applications such as drug delivery and smart packaging. Imidazolium Ionic Liquids (ILs), with a long side chain on the cation and a melting temperature of around 50 °C, were synthetized and loaded at moderate amounts (up to 20 wt%) within copolymers of polyether and a bio-based polyamide via solution casting. The resulting films were analyzed to assess their structural and thermal properties, and the gas permeation changes due to their temperature-responsive behavior. The splitting of FT-IR signals is evident, and, in the thermal analysis, a shift in the glass transition temperature (Tg) for the soft block in the host matrix towards higher values upon the addition of both ILs is also observed. The composite films show a temperature-dependent permeation with a step change corresponding to the solid–liquid phase change in the ILs. Thus, the prepared polymer gel/ILs composite membranes provide the possibility of modulating the transport properties of the polymer matrix simply by playing with temperature. The permeation of all the investigated gases obeys an Arrhenius-type law. A specific permeation behavior, depending on the heating–cooling cycle sequence, can be observed for carbon dioxide. The obtained results indicate the potential interest of the developed nanocomposites as CO2 valves for smart packaging applications.
DFT and ONIOM Simulation of 1,3-Butadiene Polymerization Catalyzed by Neodymium-Based Ziegler–Natta System
Authors: Masliy Alexey N. , Akhmetov Ildar G. , Kuznetsov Andrey M. , Davletbaeva Ilsiya M.
Published: February 2023

Using modern methods of quantum chemistry, a theoretical substantiation of the high cis-stereospecificity of 1,3-butadiene polymerization catalyzed by the neodymium-based Ziegler–Natta system was carried out. For DFT and ONIOM simulation, the most cis-stereospecific active site of the catalytic system was used. By analyzing the total energy, as well as the enthalpy and Gibbs free energy of the simulated catalytically active centers, it was found that the coordination of 1,3-butadiene in the trans-form was more favorable than in the cis-form by 11 kJ/mol. However, as a result of π-allylic insertion mechanism modeling, it was found that the activation energy of cis-1,3-butadiene insertion into the π-allylic neodymium–carbon bond of the terminal group on the reactive growing chain was 10–15 kJ/mol lower than the activation energy of trans-1,3-butadiene insertion. The activation energies did not change when both trans-1,4-butadiene and cis-1,4-butadiene were used for modeling. That is, 1,4-cis-regulation was due not to the primary coordination of 1,3-butadiene in its cis-configuration, but to its lower energy of attachment to the active site. The obtained results allowed us to clarify the mechanism of the high cis-stereospecificity of 1,3-butadiene polymerization by the neodymium-based Ziegler–Natta system.
Formation of a Conducting Polymer by Different Electrochemical Techniques and Their Effect on Obtaining an Immunosensor for Immunoglobulin G
Authors: Martinez-Sade Erika , Martinez-Rojas Francisco , Ramos Danilo , Aguirre Maria Jesus , Armijo Francisco
Published: February 2023

In this work, a conducting polymer (CP) was obtained through three electrochemical procedures to study its effect on the development of an electrochemical immunosensor for the detection of immunoglobulin G (IgG-Ag) by square wave voltammetry (SWV). The glassy carbon electrode modified with poly indol-6-carboxylic acid (6-PICA) applied the cyclic voltammetry technique presented a more homogeneous size distribution of nanowires with greater adherence allowing the direct immobilization of the antibodies (IgG-Ab) to detect the biomarker IgG-Ag. Additionally, 6-PICA presents the most stable and reproducible electrochemical response used as an analytical signal for developing a label-free electrochemical immunosensor. The different steps in obtaining the electrochemical immunosensor were characterized by FESEM, FTIR, cyclic voltammetry, electrochemical impedance spectroscopy, and SWV. Optimal conditions to improve performance, stability, and reproducibility in the immunosensing platform were achieved. The prepared immunosensor has a linear detection range of 2.0–16.0 ng·mL−1 with a low detection limit of 0.8 ng·mL−1. The immunosensing platform performance depends on the orientation of the IgG-Ab, favoring the formation of the immuno-complex with an affinity constant (Ka) of 4.32 × 109 M−1, which has great potential to be used as point of care testing (POCT) device for the rapid detection of biomarkers.
Correlations in Hard- and Soft-Core Generic Polymer Models
Authors: Wang Qiang
Published: February 2023

Generic polymer models capturing the chain connectivity and the non-bonded excluded-volume interactions between polymer segments can be classified into hard- and soft-core models depending on their non-bonded pair potential. Here we compared the correlation effects on the structural and thermodynamic properties of the hard- and soft-core models given by the polymer reference interaction site model (PRISM) theory, and found different behaviors of the soft-core models at large invariant degree of polymerization (IDP) depending on how IDP is varied. We also proposed an efficient numerical approach, which enables us to accurately solve the PRISM theory for chain lengths as large as 106.
Development of an Fe3O4 Surface-Grafted Carboxymethyl Chitosan Molecularly Imprinted Polymer for Specific Recognition and Sustained Release of Salidroside
Authors: Ma Xingbin , Li Shuyu , Qiu Jiajie , Liu Zijie , Liu Siyu , Huang Zhifeng , Yong Yanhong , Li Youquan , Yu Zhichao , Liu Xiaoxi , Lin Hongling , Ju Xianghong , Abd El-Aty A. M.
Published: February 2023

The choice of carrier material is critical in the study of natural drug release preparations and glycosylated magnetic molecularly imprinted materials. The stiffness and softness of the carrier material affect the efficiency of drug release and the specificity of recognition. The dual adjustable aperture-ligand in molecularly imprinted polymers (MIPs) provides the possibility of individualized design for sustained release studies. In this study, a combination of paramagnetic Fe3O4 and carboxymethyl chitosan (CC) was used to enhance the imprinting effect and improve drug delivery. A combination of tetrahydrofuran and ethylene glycol was used as a binary porogen to prepare MIP-doped Fe3O4-grafted CC (SMCMIP). Salidroside serves as the template, methacrylic acid acts as the functional monomer, and ethylene glycol dimethacrylate (EGDMA) serves as the crosslinker. Scanning and transmission electron microscopy were used to observe the micromorphology of the microspheres. The structural and morphological parameters of the SMCMIP composites were measured, including the surface area and pore diameter distribution. In an in vitro study, we found that the SMCMIP composite had a sustained release property of 50% after 6 h of release time in comparison to the control SMCNIP. The total amounts of SMCMIP released at 25 °C and 37 °C were 77% and 86%, respectively. In vitro results showed that the release of SMCMIP followed Fickian kinetics, meaning that the rate of release is dependent on the concentration gradient, with diffusion coefficients ranging from 3.07 × 10−2 cm2/s to 5.66 × 10−3 cm2/s. The results of cytotoxicity experiments showed that the SMCMIP composite did not have any harmful effects on cell growth. The survival rates of intestinal epithelial cells (IPEC-J2) were found to be above 98%. By using the SMCMIP composite, drugs may be delivered in a sustained manner, potentially leading to improved therapeutic outcomes and reduced side effects.
Two-Window Approach to Monitor and Assess Cellular and Humoral Immune Responses in Poultry
Authors: Erf Gisela F. , Kong Hyeonmin R. , Falcon Daniel M. , Byrne Kristen A.
Published: February 2023

As previously reported, inflammatory activity initiated by intradermal injection of multiple growing feather (GF)-pulps of a chicken with lipopolysaccharide, and the subsequent periodic sampling of GFs and blood, enables the longitudinal evaluation of in vivo tissue- and systemic-inflammatory activities by ex vivo laboratory analyses. To demonstrate the suitability of this two-window approach to monitor and assess vaccine responses, two groups of chickens were immunized by intramuscular injection of mouse IgG (mIgG), mIgG in alum adjuvant (Alum&mIgG), or PBS-vehicle (Group I and II at 7- and 7- and 11-weeks, respectively). Plasma levels of mIgG-specific antibodies were monitored by ELISA for 28 days post-primary- and secondary-immunizations. To examine the cellular responses, 20 GF-pulps per bird were injected with mIgG on Day-10 or Day-5 post-primary- or -secondary-immunization, respectively. Two GFs were collected before- and at various times (0.25 to 7 days) post-injection for leukocyte population- and cytokine mRNA expression-analyses. The observed primary- and secondary-antibody response profiles were as expected for a T-dependent antigen. Leukocyte- and cytokine-profiles established in GF-pulps revealed temporal, qualitative, and quantitative differences in local naïve, primary, and secondary leukocyte-effector responses to antigen. This study demonstrates the unique opportunity in the avian model to monitor both cell- and antibody-mediated immune responses using minimally invasive techniques.
Adaptive Slicing-Aided Hyper Inference for Small Object Detection in High-Resolution Remote Sensing Images
Authors: Zhang Hao , Hao Chuanyan , Song Wanru , Jiang Bo , Li Baozhu
Published: February 2023

In the field of object detection, deep learning models have achieved great success in recent years. Despite these advances, detecting small objects remains difficult. Most objects in aerial images have features that are a challenge for traditional object detection techniques, including small size, high density, high variability, and varying orientation. Previous approaches have used slicing methods on high-resolution images or feature maps to improve performance. However, existing slicing methods inevitably lead to redundant computation. Therefore, in this article we present a novel adaptive slicing method named ASAHI (Adaptive Slicing Aided Hyper Inference), which can dramatically reduce redundant computation using an adaptive slicing size. Specifically, ASAHI focuses on the number of slices rather than the slicing size, that is, it adaptively adjusts the slicing size to control the number of slices according to the image resolution. Additionally, we replace the standard non-maximum suppression technique with Cluster-DIoU-NMS due to its improved accuracy and inference speed in the post-processing stage. In extensive experiments, ASAHI achieves competitive performance on the VisDrone and xView datasets. The results show that the mAP50 is increased by 0.9% and the computation time is reduced by 20–25% compared with state-of-the-art slicing methods on the TPH-YOLOV5 pretrained model. On the VisDrone2019-DET-val dataset, our mAP50 result is 56.4% higher, demonstrating the superiority of our approach.
Retrieval of TP Concentration from UAV Multispectral Images Using IOA-ML Models in Small Inland Waterbodies
Authors: Hu Wentong , Liu Jie , Wang He , Miao Donghao , Shao Dongguo , Gu Wenquan
Published: February 2023

Total phosphorus (TP) concentration is high in countless small inland waterbodies in Hubei province, middle China, which is threating the water environment. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, because the cost of time, labor, and money is high and it does not meet the needs of spatiotemporal dynamic monitoring. Remote sensing provides an effective tool for TP concentration monitoring spatiotemporally. However, monitoring the TP concentration of small inland waterbodies is challenging for satellite remote sensing due to the inadequate spatial resolution. Recently, unmanned aerial vehicles (UAV) have been applied to quantitatively retrieve the spatiotemporal distribution of TP concentration without the challenges of cloud cover and atmospheric effects. Although state-of-the-art algorithms to retrieve TP concentration have been improved, specific models are only used for specific water quality parameters or regions, and there are no robust and reliable TP retrieval models for small inland waterbodies at this time. To address this issue, six machine learning methods optimized by intelligent optimization algorithms (IOA-ML models) have been developed to quantitatively retrieve TP concentration combined with the reflectance of original bands and selected band combinations of UAV multispectral images. We evaluated the performances of models in terms of coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD). The results showed that the R2 of the six IOA-ML models for training, validation, and test sets were 0.8856–0.984, 0.8054–0.8929, and 0.7462–0.9045, respectively, indicating the methods had high precision and transferability. The extreme gradient boosting optimized by genetic algorithm (GA-XGB) performed best, with the highest precision for the validation and test sets. The spatial distribution of TP concentration of each flight derived from different models had similar distribution characteristics. This paper provides a reference for promoting the intelligent and automatic level of water environment monitoring in small inland waterbodies.
High-Precision Inversion of Shallow Bathymetry under Complex Hydrographic Conditions Using VGG19—A Case Study of the Taiwan Banks
Authors: Cui Jiaxin , Luo Xiaowen , Wu Ziyin , Zhou Jieqiong , Wan Hongyang , Chen Xiaolun , Qin Xiaoming
Published: February 2023

Shallow bathymetry is important for ocean exploration, and the development of high-precision bathymetry inversion methods, especially for shallow waters with poor quality, is a major research aim. Synthetic aperture radar (SAR) image data benefit from a wide coverage, high measurement density, rapidity, and low consumption but are limited by low accuracy. Alternatively, multibeam data have low coverage and are difficult to obtain but have a high measurement accuracy. In this paper, taking advantage of the complementary properties, we use SAR image data as the content map and multibeam images as the migrated style map, applying the VGG19 neural network (optimizing the loss function formula) for bathymetric inversion. The model was universal and highly accurate for bathymetric inversion of shallow marine areas, such as turbid water in Taiwan. There was a strong correlation between bathymetric inversion data and measured data (R2 = 0.8822; RMSE = 1.86 m). The relative error was refined by 9.22% over those of previous studies. Values for different bathymetric regions were extremely correlated in the region of 20–40 m. The newly developed approach is highly accurate over 20 m in the open ocean, providing an efficient, precise shallow bathymetry inversion method for complex hydrographic conditions.
Smooth GIoU Loss for Oriented Object Detection in Remote Sensing Images
Authors: Qian Xiaoliang , Zhang Niannian , Wang Wei
Published: February 2023

Oriented object detection (OOD) can more accurately locate objects with an arbitrary direction in remote sensing images (RSIs) compared to horizontal object detection. The most commonly used bounding box regression (BBR) loss in OOD is smooth L1 loss, which requires the precondition that spatial parameters are independent of one another. This independence is an ideal that is not achievable in practice. To avoid this problem, various kinds of IoU-based BBR losses have been widely used in OOD; however, their relationships with IoUs are approximately linear. Consequently, the gradient value, i.e., the learning intensity, cannot be dynamically adjusted with the IoU in these cases, which restricts the accuracy of object location. To handle this problem, a novel BBR loss, named smooth generalized intersection over union (GIoU) loss, is proposed. The contributions it makes include two aspects. First of all, smooth GIoU loss can employ more appropriate learning intensities in the different ranges of GIoU values to address the above problem and the design scheme of smooth GIoU loss can be generalized to other IoU-based BBR losses. Secondly, the existing computational scheme of GIoU loss can be modified to fit OOD. The ablation study of smooth GIoU loss validates the effectiveness of its design scheme. Comprehensive comparisons performed on two RSI datasets demonstrate that the proposed smooth GIoU loss is superior to other BBR losses adopted by existing OOD methods and can be generalized for various kinds of OOD methods. Furthermore, the core idea of smooth GIoU loss can be generalized to other IoU-based BBR losses.
The Differences and Influence Factors in Extracting Urban Green Space from Various Resolutions of Data: The Perspective of Blocks
Authors: Wei Xiao , Hu Mengjun , Wang Xiao-Jun
Published: February 2023

The appropriate resolution has been confirmed to be crucial to the extraction of urban green space and the related research on ecosystem services. However, the factors affecting the differences between various resolutions of data in certain application scenarios are lacking in attention. To fill the gap, this paper made an attempt to analyze the differences of various resolutions of data in green space extraction and to explore where the differences are reflected in the actual land unit, as well as the factors affecting the differences. Further, suggestions for reducing errors and application scenarios of different resolutions of data in related research are proposed. Taking a typical area of Nanjing as an example, data taken by DJI drone (0.1 m), GaoFen-1 (2 m) and Sentinel-2A (10 m) were selected for analysis. The results show that: (1) There were minimal differences in the green space ratio of the study area calculated by different resolutions of data on the whole, but when subdivided into each land use type and block, the differences were obvious; (2) The function, area and shape of the block, as well as the patch density and aggregation degree of the internal green space, had a certain impact on the differences. However, the specific impact varied when the block area was different; and (3) For the selection of the data source, the research purpose and application scenarios need to be comprehensively considered, including the function and attributes of the block, the distribution characteristics of green space, the allowable error limits and the budget. The present study highlighted the reasons of differences and hopefully it can provide a reference for the data selection of urban green space in the practical planning and design.
COVID-19 Pandemic: Impacts on Air Quality during Partial Lockdown in the Metropolitan Area of São Paulo
Authors: Alvim Débora Souza , Herdies Dirceu Luis , Corrêa Sergio Machado , Basso Luana Santamaria , Khalid Bushra , Silva Gabriella Fernandes Prazeres , Oyerinde Gabriel , de Carvalho Nicolli Albuquerque , Coelho Simone Marilene Sievert da Costa , Figueroa Silvio Nilo
Published: February 2023

Air pollution has become one of the factors that most affect the quality of life, human health, and the environment. Gaseous pollutants from motor vehicles have a significantly harmful effect on air quality in the Metropolitan Area of São Paulo (MASP)—Brazil. Motor vehicles emit large amounts of particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and volatile organic compounds (VOCs), the last three acting as the main tropospheric ozone (O3) precursors. In this study, we evaluated the effects of these pollutants on air quality in the MASP during the partial lockdown that was imposed to ensure the social distancing necessitated by the COVID-19 pandemic. We compared the monthly data for nitrogen dioxide (NO2) from the Ozone Monitoring Instrument (OMI) and CO, SO2, and BC from MERRA-2 for the period between April and May 2020 (during the pandemic) with the average for the same period for the (pre-pandemic) years 2017 to 2019 in the southeast region of Brazil. The meteorological and pollutant concentration data from the CETESB air quality monitoring stations for the MASP were compared with the diurnal cycle of three previous years, with regard to the monthly averages of April and May (2017, 2018, and 2019) and the same period in 2020, when the partial lockdown was first imposed in southeast Brazil. Our findings showed that there was a decrease in NO2 concentrations ranging from 10% to more than 60% in the MASP and the Metropolitan Area of Rio de Janeiro (MARJ), whereas in the Metropolitan Area of Belo Horizonte and Vitoria (MABH and MAV, respectively), there was a reduction of around 10%. In the case of the concentrations of CO and BC from MERRA-2, there was a considerable decrease (approx. 10%) during the period of partial lockdown caused by COVID-19 throughout almost the entire state of São Paulo, particularly in the region bordering the state of Rio de Janeiro. The concentration of SO2 from MERRA-2 was 5 to 10% lower in the MASP and MARJ and the west of the MABH, and there was a decrease of 30 to 50% on the border between the states of São Paulo and Rio de Janeiro, while in the MAV region, there was an increase in pollutant levels, as this region was not significantly affected by the COVID-19 pandemic. Sharp reductions in the average hourly concentrations of CO (38.8%), NO (44.9%), NO2 (38.7%), and PM2.5 (6%) were noted at the CETESB air quality monitoring stations in the MASP during the partial lockdown in 2020 compared with the hourly average rate in the pre-pandemic period. In contrast, there was an increase of approximately 16.0% in O3 concentrations in urban areas that are seriously affected by vehicular emissions, which is probably related to a decrease in NOx.
Multi-Sensor Data Fusion for 3D Reconstruction of Complex Structures: A Case Study on a Real High Formwork Project
Authors: Zhao Linlin , Zhang Huirong , Mbachu Jasper
Published: February 2023

As the most comprehensive document types for the recording and display of real-world information regarding construction projects, 3D realistic models are capable of recording and displaying simultaneously textures and geometric shapes in the same 3D scene. However, at present, the documentation for much of construction infrastructure faces significant challenges. Based on TLS, GNSS/IMU, mature photogrammetry, a UAV platform, computer vision technologies, and AI algorithms, this study proposes a workflow for 3D modeling of complex structures with multiple-source data. A deep learning LoFTR network was used first for image matching, which can improve matching accuracy. Then, a NeuralRecon network was employed to generate a 3D point cloud with global consistency. GNSS information was used to reduce search space in image matching and produce an accurate transformation matrix between the image scene and the global reference system. In addition, to enhance the effectiveness and efficiency of the co-registration of the two-source point clouds, an RPM-net was used. The proposed workflow processed the 3D laser point cloud and UAV low-altitude multi-view image data to generate a complete, accurate, high-resolution, and detailed 3D model. Experimental validation on a real high formwork project was carried out, and the result indicates that the generated 3D model has satisfactory accuracy with a registration error value of 5 cm. Model comparison between the TLS, image-based, data fusion 1 (using the common method), and data fusion 2 (using the proposed method) models were conducted in terms of completeness, geometrical accuracy, texture appearance, and appeal to professionals. The results denote that the generated 3D model has similar accuracy to the TLS model yet also provides a complete model with a photorealistic appearance that most professionals chose as their favorite.
Satellite-Based Ocean Color and Thermal Signatures Defining Habitat Hotspots and the Movement Pattern for Commercial Skipjack Tuna in Indonesia Fisheries Management Area 713, Western Tropical Pacific
Authors: Zainuddin Mukti , Safruddin Safruddin , Farhum Aisjah , Budimawan Budimawan , Hidayat Rachmat , Selamat Muhammad Banda , Wiyono Eko Sri , Ridwan Muhammad , Syamsuddin Mega , Ihsan Yudi Nurul
Published: February 2023

Understanding the mechanisms that determine the critical habitat of commercial species is one of the significant challenges in marine science, particularly for species that inhabit the vast ocean worldwide. Previous investigations primarily focused on determining skipjack habitats without considering the feasible size for sustainable fisheries. To define habitat hotspots and movement patterns for decently sized skipjack tuna (≥50 cm) in Indonesia Fisheries Management Area (IFMA) 713, Indonesia, we examined the remote sensing of synoptic sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) measurements with catch data from 2007 to 2016. A new skipjack tuna habitat model was developed based on the link between the key satellite-based environmental data and the best tuna fishery performance using a combination of generalized additive models (GAMs) and kernel density estimates. The findings reveal that feasible skipjack catch sizes were found in approximately 27% of total fishing grounds and were significantly captured in areas with a Chl-a of 0.15–0.28 mg m−3 and an SST of 29.5–31.0 °C, corresponding with an elevated skipjack habitat index (SHI). The habitat hotspots for the commercial skipjack were particularly produced by favorable Chl-a and SST, in association with Chl-a front and anticyclonic and cyclonic eddies, especially in October, which coincided with the highest skipjack catch per unit effort (CPUE). Skipjack distributions were mostly found within 10 km of favorable feeding habitats. They used the hotspot area as an indicator of their dynamics and movement pattern in the environment. The observed CPUEs cross-validated the predicted SHI values, suggesting that the model provided a reliable proxy for defining the potential habitats and the spatial movement of mature skipjack schools. Our findings have global significance for locating ecological hotspots, monitoring sustainable skipjack fisheries, and tracking skipjack migration, especially within the western tropical Pacific.
Assessment of AIRS Version 7 Temperature Profiles and Low-Level Inversions with GRUAN Radiosonde Observations in the Arctic
Authors: Zhang Lei , Ding Minghu , Zheng Xiangdong , Chen Junming , Guo Jianping , Bian Lingen
Published: February 2023

The extensive global climate observing system (GCOS) reference upper-air network (GRUAN) datasets provide a chance to validate newly released Atmospheric Infrared Sounder (AIRS) version 7 (v7) products over the Arctic. This manuscript reports on the analysis performed to evaluate errors from AIRS version 6 (v6) and v7 temperature profiles and to characterize the derived low-level temperature inversion (LLI) representativeness in the Arctic region. The AIRS averaging kernel, representing the AIRS measurement sensitivity, is applied to reduce the vertical resolution of the radiosonde profiles for comparison. Due to improved retrieval algorithms, v7 produces smaller biases in the troposphere and suppresses the cold bias in v6. Nevertheless, the profile-averaged root mean square error (RMSE) increased by over 30% in v7, particularly in the winter half-year when v7 showed a larger RMSE below 800 hPa. The AIRS temperature retrieval accuracy is primarily sensitive to surface type and cloud fraction. Compared to v6, v7 has less bias over frozen land and sea ice in different cloud fraction conditions. However, the RMSEs of v7 are more sensitive to the effective cloud fraction (ECF) and are highly influenced by a more significant contribution from nonfrozen land samples. Compared to the kernel-averaged radiosonde profiles, more than 80% of the temperature profiles from v6 and v7 accurately detect LLIs. The discreteness of the AIRS’s predefined pressure level results is consistent with the radiosondes only 65% of the time for LLI depth calculation. In contrast, the AIRS can obtain LLI intensity with a relatively high correlation (>0.9). With the AIRS temperature retrieval in the boundary layer further improved, it has the potential to be used as an independent LLI detector in the Arctic region.
Contribution of Land Cover Classification Results Based on Sentinel-1 and 2 to the Accreditation of Wetland Cities
Authors: Wang Xiaoya , Jiang Weiguo , Deng Yawen , Yin Xiaogan , Peng Kaifeng , Rao Pinzeng , Li Zhuo
Published: February 2023

Wetland cities were proposed by the International Wetland Convention Organization for the protection of urban wetlands. Few studies have performed land cover classifications for internationally recognized wetland cities or explored what contribution the classification results can make to the establishment of additional wetland cities to date. Based on Sentinel-1 and 2 data, this study used a 10-fold random forest method to classify the land cover of the first six wetland cities recognized in China. A land cover dataset, which had a resolution of 10 m and included four wetland types, was obtained and the wetland area and protected wetland areas of the six cities were calculated. The results showed that (1) the classification accuracy of six cities was good, the overall accuracy was above 90%, and the Kappa coefficient was above 0.88. (2) Cropland or forested areas were the most common non-wetland land coverage type in wetland cities and accounted for more than 20% or 40% of the land coverages, while water was the most common wetland type and accounted for more than 2% of the land coverages. From 2015 to 2020, the built area in most cities increased, while cropland and forest decreased significantly. (3) The wetland rate was 6.68–37.56% and the wetland protection rate was 49.48–73.74% in the six wetland cities. From 2015 to 2020, the wetland rate of the six cities were relatively stable, and the wetland protection rate of inland cities (Yinchuan, Changde, Harbin and Changshu) increased significantly, while those of coastal cities (Haikou and Dongying) decreased, which might be related to the change in coastline. Therefore, we found that the wetlands in these cities were well protected. Land cover classification for wetland cities can provide a reference for using remote sensing techniques used to monitor internationally wetland cities while also supporting the creation of additional wetland cities.
A Multi-Satellite Mapping Framework for Floating Kelp Forests
Authors: Gendall Lianna , Schroeder Sarah B. , Wills Peter , Hessing-Lewis Margot , Costa Maycira
Published: February 2023

Kelp forests provide key habitat on the Pacific Coast of Canada; however, the long-term changes in their distribution and abundance remain poorly understood. With advances in satellite technology, floating kelp forests can now be monitored across large-scale areas. We present a methodological framework using an object-based image analysis approach that enables the combination of imagery from multiple satellites at different spatial resolutions and temporal coverage, to map kelp forests with floating canopy through time. The framework comprises four steps: (1) compilation and quality assessment; (2) preprocessing; (3) an object-oriented classification; and (4) an accuracy assessment. Additionally, the impact of spatial resolution on the detectability of floating kelp forests is described. Overall, this workflow was successful in producing accurate maps of floating kelp forests, with global accuracy scores of between 88% and 94%. When comparing the impact of resolution on detectability, lower resolutions were less reliable at detecting small kelp forests in high slope areas. Based on the analysis, we suggest removing high slope areas (11.4%) from time series analyses using high- to medium-resolution satellite imagery and that error, in this case up to 7%, be considered when comparing imagery at different resolutions in low–mid slope areas through time.
A Novel Deep Stack-Based Ensemble Learning Approach for Fault Detection and Classification in Photovoltaic Arrays
Authors: Lodhi Ehtisham , Wang Fei-Yue , Xiong Gang , Zhu Lingjian , Tamir Tariku Sinshaw , Rehman Waheed Ur , Khan M. Adil
Published: February 2023

The widespread adoption of green energy resources worldwide, such as photovoltaic (PV) systems to generate green and renewable power, has prompted safety and reliability concerns. One of these concerns is fault diagnostics, which is needed to manage the reliability and output of PV systems. Severe PV faults make detecting faults challenging because of drastic weather circumstances. This research article presents a novel deep stack-based ensemble learning (DSEL) approach for diagnosing PV array faults. The DSEL approach compromises three deep-learning models, namely, deep neural network, long short-term memory, and Bi-directional long short-term memory, as base learners for diagnosing PV faults. To better analyze PV arrays, we use multinomial logistic regression as a meta-learner to combine the predictions of base learners. This study considers open circuits, short circuits, partial shading, bridge, degradation faults, and incorporation of the MPPT algorithm. The DSEL algorithm offers reliable, precise, and accurate PV-fault diagnostics for noiseless and noisy data. The proposed DSEL approach is quantitatively examined and compared to eight prior machine-learning and deep-learning-based PV-fault classification methodologies by using a simulated dataset. The findings show that the proposed approach outperforms other techniques, achieving 98.62% accuracy for fault detection with noiseless data and 94.87% accuracy with noisy data. The study revealed that the DSEL algorithm retains a strong generalization potential for detecting PV faults while enhancing prediction accuracy. Hence, the proposed DSEL algorithm detects and categorizes PV array faults more efficiently, reliably, and accurately.
A Remote Sensing View of the 2020 Extreme Lake-Expansion Flood Event into the Peace–Athabasca Delta Floodplain—Implications for the Future SWOT Mission
Authors: Desrochers Nicolas M. , Peters Daniel L. , Siles Gabriela , Cauvier Charest Elizabeth , Trudel Mélanie , Leconte Robert
Published: February 2023

The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of remote sensing platforms to map surface water expansion into vegetated landscape with complex surface connectivity. Firstly, multi-source remotely sensed data via satellites were used to create a temporal reconstruction of the event spanning May to September. Optical synthetic aperture radar (SAR) and altimeter data were used to reconstruct surface water area and elevation as seen from space. Lastly, temporal water surface area and level data obtained from the existing satellites and hydrometric stations were used as input data in the CNES Large-Scale SWOT Simulator, which provided an overview of the newly launched SWOT satellite ability to monitor such flood events. The results show a 25% smaller water surface area for optical instruments compared to SAR. Simulations show that SWOT would have greatly increased the spatio-temporal understanding of the flood dynamics with complete PAD coverage three to four times per month. Overall, seasonal vegetation growth was a major obstacle for water surface area retrieval, especially for optical sensors.
A Comparison of Modeling Methods for Predicting Forest Attributes Using Lidar Metrics
Authors: Adhikari Angel , Montes Cristian R. , Peduzzi Alicia
Published: February 2023

Recent advancements in laser scanning technology have demonstrated great potential for the precise characterization of forests. However, a major challenge in utilizing metrics derived from lidar data for the forest attribute prediction is the high degree of correlation between these metrics, leading to multicollinearity issues when developing multivariate linear regression models. To address this challenge, this study compared the performance of four different modeling methods for predicting various forest attributes using aerial lidar data: (1) Least Squares Regression (LSR), (2) Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO), (3) Random Forest (RF), and (4) Generalized Additive Modeling Selection (GAMSEL). The study used three primary plot-level forest attributes (volume, basal area, and dominant height) as response variables and thirty-nine plot-level lidar metrics as explanatory variables. A k-fold cross-validation approach was used, with consistent folds to assess the performance of each method. Our results revealed that no single method demonstrated a significant advantage over the others. Nonetheless, the highest R2 values of 0.88, 0.83, and 0.87 for volume, basal area, and dominant height, respectively, were achieved using the ALASSO method. This method was also found to be less biased, followed by GAMSEL and LSR.