bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemically Mediated Plant–Plant Interactions: Allelopathy and Allelobiosis
Authors: Kong Chui-Hua , Li Zheng , Li Feng-Li , Xia Xin-Xin , Wang Peng
Published: February 2024

Plant–plant interactions are a central driver for plant coexistence and community assembly. Chemically mediated plant–plant interactions are represented by allelopathy and allelobiosis. Both allelopathy and allelobiosis are achieved through specialized metabolites (allelochemicals or signaling chemicals) produced and released from neighboring plants. Allelopathy exerts mostly negative effects on the establishment and growth of neighboring plants by allelochemicals, while allelobiosis provides plant neighbor detection and identity recognition mediated by signaling chemicals. Therefore, plants can chemically affect the performance of neighboring plants through the allelopathy and allelobiosis that frequently occur in plant–plant intra-specific and inter-specific interactions. Allelopathy and allelobiosis are two probably inseparable processes that occur together in plant–plant chemical interactions. Here, we comprehensively review allelopathy and allelobiosis in plant–plant interactions, including allelopathy and allelochemicals and their application for sustainable agriculture and forestry, allelobiosis and plant identity recognition, chemically mediated root–soil interactions and plant–soil feedback, and biosynthesis and the molecular mechanisms of allelochemicals and signaling chemicals. Altogether, these efforts provide the recent advancements in the wide field of allelopathy and allelobiosis, and new insights into the chemically mediated plant–plant interactions.
Overexpression of Phosphoserine Aminotransferase (PSAT)-Enhanced Cadmium Resistance and Accumulation in Duckweed (Lemna turionifera 5511)
Authors: Ma Xu , Jiang Yumeng , Qu Ziyang , Yang Yunwen , Wang Wenqiao , He Yuman , Yu Yiqi , Luo Ximeng , Liu Yuanyuan , Han Wenqian , Di Qiqi , Yang Lin , Wang Yong
Published: February 2024

Cadmium (Cd) hampers plant growth and harms photosynthesis. Glutamate (Glu) responds to Cd stress and activates the Ca2+ signaling pathway in duckweed, emphasizing Glu’s significant role in Cd stress. In this study, we overexpressed phosphoserine aminotransferase (PSAT), a crucial enzyme in Glu metabolism, in duckweed. We investigated the response of PSAT-transgenic duckweed to Cd stress, including growth, Glu metabolism, photosynthesis, antioxidant enzyme activity, Cd2+ flux, and gene expression. Remarkably, under Cd stress, PSAT-transgenic duckweed prevented root abscission, upregulated the expression of photosynthesis ability, and increased Chl a, Chl b, and Chl a + b levels by 13.9%, 7%, and 12.6%, respectively. Antioxidant enzyme activity (CAT and SOD) also improved under Cd stress, reducing cell membrane damage in PSAT-transgenic duckweeds. Transcriptomic analysis revealed an upregulation of Glu metabolism-related enzymes in PSAT-transgenic duckweed under Cd stress. Moreover, metabolomic analysis showed a 68.4% increase in Glu content in PSAT duckweed exposed to Cd. This study sheds novel insights into the role of PSAT in enhancing plant resistance to Cd stress, establishing a theoretical basis for the impact of Glu metabolism on heavy metal tolerance in plants.
The American Cherimoya Genome Reveals Insights into the Intra-Specific Divergence, the Evolution of Magnoliales, and a Putative Gene Cluster for Acetogenin Biosynthesis
Authors: Li Tang , Zheng Jinfang , Nousias Orestis , Yan Yuchen , Meinhardt Lyndel W. , Goenaga Ricardo , Zhang Dapeng , Yin Yanbin
Published: February 2024

Annona cherimola (cherimoya) is a species renowned for its delectable fruit and medicinal properties. In this study, we developed a chromosome-level genome assembly for the cherimoya ‘Booth’ cultivar from the United States. The genome assembly has a size of 794 Mb with a N50 = 97.59 Mb. The seven longest scaffolds account for 87.6% of the total genome length, which corresponds to the seven pseudo-chromosomes. A total of 45,272 protein-coding genes (≥30 aa) were predicted with 92.9% gene content completeness. No recent whole genome duplications were identified by an intra-genome collinearity analysis. Phylogenetic analysis supports that eudicots and magnoliids are more closely related to each other than to monocots. Moreover, the Magnoliales was found to be more closely related to the Laurales than the Piperales. Genome comparison revealed that the ‘Booth’ cultivar has 200 Mb less repeats than the Spanish cultivar ‘Fino de Jete’, despite their highly similar (>99%) genome sequence identity and collinearity. These two cultivars were diverged during the early Pleistocene (1.93 Mya), which suggests a different origin and domestication of the cherimoya. Terpene/terpenoid metabolism functions were found to be enriched in Magnoliales, while TNL (Toll/Interleukin-1-NBS-LRR) disease resistance gene has been lost in Magnoliales during evolution. We have also identified a gene cluster that is potentially responsible for the biosynthesis of acetogenins, a class of natural products found exclusively in Annonaceae. The cherimoya genome provides an invaluable resource for supporting characterization, conservation, and utilization of Annona genetic resources.
Genome-Wide Identification and Expression Analysis Unveil the Involvement of the Cold Shock Protein (CSP) Gene Family in Cotton Hypothermia Stress
Authors: Yang Yejun , Zhou Ting , Xu Jianglin , Wang Yongqiang , Pu Yuanchun , Qu Yunfang , Sun Guoqing
Published: February 2024

Cold shock proteins (CSPs) are DNA/RNA binding proteins with crucial regulatory roles in plant growth, development, and stress responses. In this study, we employed bioinformatics tools to identify and analyze the physicochemical properties, conserved domains, gene structure, phylogenetic relationships, cis-acting elements, subcellular localization, and expression patterns of the cotton CSP gene family. A total of 62 CSP proteins were identified across four cotton varieties (Gossypium arboreum, Gossypium raimondii, Gossypium barbadense, Gossypium hirsutum) and five plant varieties (Arabidopsis thaliana, Brassica chinensis, Camellia sinensis, Triticum aestivum, and Oryza sativa). Phylogenetic analysis categorized cotton CSP proteins into three evolutionary branches, revealing similar gene structures and motif distributions within each branch. Analysis of gene structural domains highlighted the conserved CSD and CCHC domains across all cotton CSP families. Subcellular localization predictions indicated predominant nuclear localization for CSPs. Examination of cis-elements in gene promoters revealed a variety of elements responsive to growth, development, light response, hormones, and abiotic stresses, suggesting the potential regulation of the cotton CSP family by different hormones and their involvement in diverse stress responses. RT-qPCR results suggested that GhCSP.A1, GhCSP.A2, GhCSP.A3, and GhCSP.A7 may play roles in cotton’s response to low-temperature stress. In conclusion, our findings underscore the significant role of the CSP gene family in cotton’s response to low-temperature stress, providing a foundational basis for further investigations into the functional aspects and molecular mechanisms of cotton’s response to low temperatures.
Identification of Hypoglycemic Glycolipids from Ipomoea murucoides by Affinity-Directed Fractionation, In Vitro, In Silico and Dynamic Light Scattering Analysis
Authors: Rosas-Ramírez Daniel , Arreguín-Espinosa Roberto , Escandón-Rivera Sonia , Andrade-Cetto Adolfo , Mata-Torres Gerardo , Pérez-Solís Ricardo
Published: February 2024

In the pursuit of identifying the novel resin glycoside modulators glucose-6-phosphatase and α-glucosidase enzymes, associated with blood sugar regulation, methanol-soluble extracts from the flowers of Ipomoea murucoides (cazahuate, Nahuatl), renowned for its abundance of glycolipids, were employed. The methanol-soluble extracts were fractionated by applying the affinity-directed method with glucose-6-phosphatase enzymes from a rat’s liver and α-glucosidase enzymes from its intestines. Mass spectrometry and nuclear magnetic resonance were employed to identify the high-affinity compound as a free ligand following the release from the enzymatic complex. Gel permeation through a spin size-exclusion column allowed the separated high-affinity molecules to bind to glucose-6-phosphatase and α-glucosidase enzymes in solution, which led to the identification of some previously reported resin glycosides in the flowers of cazahuate, where a glycolipid mainly structurally related to murucoidin XIV was observed. In vitro studies demonstrated the modulating properties of resin glycosides on the glucose-6-phosphatase enzyme. Dynamic light scattering revealed conformational variations induced by resin glycosides on α-glucosidase enzyme, causing them to become more compact, akin to observations with the positive control, acarbose. These findings suggest that resin glycosides may serve as a potential source for phytotherapeutic agents with antihyperglycemic properties.
Methods for Improving the Germination of Rhodotypos scandens (Thunb.) Makino Seeds through Endocarp Removal
Authors: Kim Hyeon Min , Kim Jun Hyeok , Lee Mi Hyun , Kim Gun Mo , Park Chung Youl , Lee Da Hyun , Na Chae Sun
Published: February 2024

Rhodotypos scandens (Thunb.) Makino is known to have a seed dispersal that is thick and stony (endocarp + seeds) and has potential as a landscaping tree seed. In several Rosaceae species, seeds are covered with a hard endocarp, making the internal seeds water-impermeable and germination difficult. Here, we analyzed the morphoanatomical traits and germination properties of R. scandens seeds. To identify ideal seed propagation conditions, we immersed R. scandens seeds in sulfuric acid for varying durations and subjected them to phytohormone (gibberellic acid A3 and fluridone) and a cold stratification (CS) (5 °C) treatment after endocarp removal (ER). The R. scandens stony seeds did not increase in mass by ≥25.0%. Following ER, the seed mass increased by ≥50.0% with water absorption when compared to the initial dry mass. Seed surfaces showed damage and cracks through scarification after 1 h of immersion in sulfuric acid, failing to germinate. A combination of ER, phytohormone treatment, and CS improved seed germination compared to ER alone (26.0 ± 5.3%). Overall, R. scandens seeds showed a dispersal with a hard endocarp from the parent plant, and a pre-treatment with ER, phytohormones, and CS was required for effective seed propagation.
NMR Analyses and Statistical Modeling of Biobased Polymer Microstructures—A Selected Review
Authors: Cheng Huai N. , Asakura Tetsuo , Suganuma Koto , Lagaron Jose M. , Melendez-Rodriguez Beatriz , Biswas Atanu
Published: February 2024

NMR analysis combined with statistical modeling offers a useful approach to investigate the microstructures of polymers. This article provides a selective review of the developments in both the NMR analysis of biobased polymers and the statistical models that can be used to characterize these materials. The information obtained from NMR and statistical models can provide insights into the microstructure and stereochemistry of appropriate biobased polymers and establish a systematic approach to their analysis. In suitable cases, the analysis can help optimize the synthetic procedures and facilitate the development of new or modified polymeric materials for various applications. Examples are given of the studies of poly(hydroxyalkanoates), poly(lactic acid), and selected polysaccharides, e.g., alginate, pectin, and chitosan. This article may serve as both a reference and a guide for future workers interested in the NMR sequence analysis of biobased materials.
Accelerated Weathering Testing (AWT) and Bacterial Biodegradation Effects on Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)/Rapeseed Microfiber Biocomposites Properties
Authors: Žiganova Madara , Merijs-Meri Remo , Zicāns Jānis , Ābele Agnese , Bochkov Ivan , Ivanova Tatjana
Published: February 2024

In the context of sustainable materials, this study explores the effects of accelerated weathering testing and bacterial biodegradation on poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)/rapeseed microfiber biocomposites. Accelerated weathering, simulating outdoor environmental conditions, and bacterial biodegradation, representing natural degradation processes in soil, were employed to investigate the changes in the mechanical, thermal and morphological properties of these materials during its post-production life cycle. Attention was paid to the assessment of the change of structural, mechanical and calorimetric properties of alkali and N-methylmorpholine N-oxide (NMMO)-treated rapeseed microfiber (RS)-reinforced plasticized PHBV composites before and after accelerated weathering. Results revealed that accelerated weathering led to an increase in stiffness, but a reduction in tensile strength and elongation at break, of the investigated PHBV biocomposites. Additionally, during accelerated weathering, the crystallinity of PHBV biocomposites increased, especially in the presence of RS, due to both the hydrolytic degradation of the polymer matrix and the nucleating effect of the filler. It has been observed that an increase in PHBV crystallinity, determined by DSC measurements, correlates with the intensity ratio I1225/1180 obtained from FTIR-ATR data. The treatment of RS microfibers increased the biodegradation capability of the developed PHBV composites, especially in the case of chemically untreated RS. All the developed PHBV composites demonstrated faster biodegradation in comparison to neat PHBV matrix.
Performance and Damage Study of Composite Rotor Blades under Impact
Authors: Yu Guorui , Li Xiaobin , Huang Wenjun
Published: February 2024

A military helicopter is easily attacked by bullets in a battlefield environment. The composite blade is the main lifting surface and control surface of the helicopter. Its ballistic performance directly determines the vulnerability and survivability of the helicopter in the battlefield environment. To study the ballistic performance of the composite helicopter blade, the damage characteristics of the impacted composite rotor blade are obtained by experiments. A numerical simulation model is established by applying Abaqus software to predict the blade ballistic damage. The three-dimensional progressive damage failure model is used to analyze the ballistic damage under the experimental conditions. The effectiveness and accuracy of the numerical simulation model are verified through a comparison with the experimental results. The ballistic damage of composite blades under three experimental conditions was investigated. The results show that the ballistic damage type of composite blade mainly includes delamination, fiber breakage, and foam collapse. The damage to the composite material at the position of bullet incidence is mainly local shear fracture, while the damage to the composite material at the exit position is mainly fiber tensile fracture. The ballistic damage size of the composite blade is closely related to the ballistic position, incident angle, and structure characteristics along the ballistic path. The larger the incident angle, the smaller the ballistic damage size of the blade. The greater the structural stiffness of the structure near the exit, the greater the damage size of the exit. The numerical simulation model presented in this paper can provide a reference for research on the ballistic performance of composite helicopter blades.
Characterization of Antimicrobial Poly(Lactic Acid)- and Polyurethane-Based Materials Enduring Closed-Loop Recycling with Applications in Space
Authors: D’Ovidio Andrew J. , Knarr Brian , Blanchard Alexander J. , Bennett Gregory W. , Leiva William , Duan Bin , Zuniga Jorge M.
Published: February 2024

Recent studies have shown that astronauts experience altered immune response behavior during spaceflight, resulting in heightened susceptibility to illness. Resources and resupply shuttles will become scarcer with longer duration spaceflight, limiting access to potentially necessary medical treatment and facilities. Thus, there is a need for preventative health countermeasures that can exploit in situ resource utilization technologies during spaceflight, such as additive manufacturing (i.e., 3D printing). The purpose of the current study was to test and validate recyclable antimicrobial materials compatible with additive manufacturing. Antimicrobial poly(lactic acid)- and polyurethane-based materials compatible with 3D printing were assessed for antimicrobial, mechanical, and chemical characteristics before and after one closed-loop recycling cycle. Our results show high biocidal efficacy (>90%) of both poly(lactic acid) and polyurethane materials while retaining efficacy post recycling, except for recycled-state polyurethane which dropped from 98.91% to 0% efficacy post 1-year accelerated aging. Significant differences in tensile and compression characteristics were observed post recycling, although no significant changes to functional chemical groups were found. Proof-of-concept medical devices developed show the potential for the on-demand manufacturing and recyclability of typically single-use medical devices using antimicrobial materials that could serve as preventative health countermeasures for immunocompromised populations, such as astronauts during spaceflight.
Electrical Conduction Mechanisms in Ethyl Cellulose Films under DC and AC Electric Fields
Authors: Puente-Córdova Jesús G. , Luna-Martínez Juan F. , Mohamed-Noriega Nasser , Miranda-Valdez Isaac Y.
Published: February 2024

This work reports the dielectric behavior of the biopolymer ethyl cellulose (EC) observed from transient currents experiments under the action of a direct current (DC) electric field (~107 V/m) under vacuum conditions. The viscoelastic response of the EC was evaluated using dynamic mechanical analysis (DMA), observing a mechanical relaxation related to glass transition of around ~402 K. Furthermore, we propose a mathematical framework that describes the transient current in EC using a fractional differential equation, whose solution involves the Mittag–Leffler function. The fractional order, between 0 and 1, is related to the energy dissipation rate and the molecular mobility of the polymer. Subsequently, the conduction mechanisms are considered, on the one hand, the phenomena that occur through the polymer–electrode interface and, on the other hand, those which manifest themselves in the bulk material. Finally, alternating current (AC) conductivity measurements above the glass transition temperature (~402 K) and in a frequency domain from 20 Hz to 2 MHz were carried out, observing electrical conduction described by the segmental movements of the polymeric chains. Its electrical properties also position EC as a potential candidate for electrical, electronics, and mechatronics applications.
Hygrothermal Effect on GF/VE and GF/UP Composites: Durability Performance and Laboratory Assessment
Authors: Wang Dengxia , Sun Yan , Duan Jian , Xie Keyong , Li Jikai , An Qi , Wang Xinbo
Published: February 2024

In order to investigate the durability of two kinds of fiber-reinforced composite materials, and obtain the degradation mechanism and failure model in a hygrothermal environment, E-glass- fiber-reinforced composite materials, glass fiber-reinforced epoxy vinyl ester and glass fiber-reinforced unsaturated polyester (named GF/VE and GF/UP, respectively) were chosen to suffer rigorous hygrothermal aging. Their mechanical performance was monitored during the aging process to evaluate their durability. The cause of deterioration of the composite was comprehensively analyzed. Based on the analysis results of attenuated total-reflectance-Fourier-transform infrared spectroscopy (ATR-FTIR), thermogravimetric analysis (TGA) and dynamic mechanical analysis (DMA), the change mechanism of chain structure of the resin molecule was proposed. SEM (scanning electron microscopy), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) were used to analyze the microstructure and degradation mechanism of the fiber and the interface between fiber and matrix. The degradation mechanism of the composite system, including the resin, the fiber and the interface, was obtained, and it was found that the deterioration of the matrix resin caused by the hygrothermal environment was the main factor leading to the decline in composites performance.
Design and Study of Novel Composites Based on EPDM Rubber Containing Bismuth (III) Oxide and Graphene Nanoplatelets for Gamma Radiation Shielding
Authors: Álvarez-Cortez Gabriela , Molina Francisco , Urbano Bruno F. , Dahrouch Mohamed , Hernández Santana Marianella , Manchado Miguel A. Lopez , Verdejo Raquel , Aguilar Bolados Héctor
Published: February 2024

The mechanical, thermal and gamma radiation attenuation properties of ethylene–propylene–diene monomer (EPDM)-based composites containing graphene nanoplatelets (GNs) and bismuth (III) oxide nanoparticles (B) were investigated. The use of polyethylene glycol (PEG) as a compatibilizer to improve the dispersion of the fillers was also investigated. The results showed that the combined use of these fillers resulted in a drastic increase in mechanical properties, reaching 123% and 83% of tensile strength and elongation at break, respectively, compared to those of EPDM. In contrast, the addition of PEG to composites containing EPDM GNs and B resulted in composites with lower values of mechanical properties compared to the EPDM/B/GN-based composite. However, the presence of PEG leads to obtaining a composite (EPDM/B/GNP) with a mass attenuation coefficient to gamma radiation (137Cs, 662 keV) superior to that composite without PEG. In addition, the composite EPDM, B and PEG exhibited an elongation at break 153% superior to unfilled EPDM. Moreover, the binary filler system consisting of 100 phr of bismuth (III) oxide and 10 phr of GN leads to reaching 61% of the linear damping coefficient of the EPDM composite compared to that value of the unfilled EPDM. The study of the morphology and the state of filler dispersion in the polymer matrix, obtained using scanning electron microscopy and energy-dispersive X-ray spectroscopy, respectively, provides a useful background for understanding the factors affecting the gamma radiation attenuation properties. Finally, the results also indicated that by adjusting the formulation, it is possible to tune the mechanical and thermal properties of EPDM composites reinforced with bismuth oxide and graphene nanoplatelets.
Reshaping Abraham’s Image in Early Qing China: A Comparative Study of Catholic and Jewish Interpretations
Authors: Li Lixin , Ni Aixia
Published: February 2024

In the early Qing dynasty, the Jesuit missionary Louis de Poirot’s (He Qingtai 賀清泰, 1735–1814) Chinese rendition of the Bible, Guxin Shengjing (The Ancient and New Testament) (古新聖經), reshaped the figure of Abraham. Contrary to the depiction by Chinese Jews of the era, de Poirot portrayed Abraham as a sage, resonating with the traditional Chinese concept of the “five cardinal relationships” (rulers to subjects, fathers to sons, husbands to wives, among siblings, and between friends), and an exemplar of virtue and faith, devoid of human flaws. Key differences emerged in translating Abraham’s name, religious stature, and national identity, influenced by distinct belief systems, attitudes towards Confucian culture, and political dynamics. Analyzing these Catholic and Jewish perspectives on Abraham enhances our understanding of the Bible’s contextualization and informs contemporary religious localization.
Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions
Authors: Parsons Tyler , Baghyari Farhad , Seo Jaho , Kim Wongun , Lee Myeonggyu
Published: February 2024

In recent years, autonomous mobile platforms have seen an increase in usage in several applications. One of which is street-sweeping. Although street-sweeping is a necessary process due to health and cleanliness, fleet operations are difficult to plan optimally. Since each vehicle has several constraints (battery, debris, and water), path planning becomes increasingly difficult to perform manually. Additionally, in real-world applications vehicles may become inactive due to a breakdown, which requires real-time scheduling technology to update the paths for the remaining vehicles. In this paper, the fleet street-sweeping problem can be solved using the proposed lower-level and higher-level path generation methods. For the lower level, a Smart Selective Navigator algorithm is proposed, and a modified genetic algorithm is used for the higher-level path planning. A case study was presented for Uchi Park, South Korea, where the proposed methodology was validated. Specifically, results generated from the ideal scenario (all vehicles operating) were compared to the breakdown scenario, where little to no difference in the overall statistics was observed. Additionally, the lower-level path generation could yield solutions with over 94% area coverage.
A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images
Authors: Abady Lydia , Dimitri Giovanna Maria , Barni Mauro
Published: February 2024

The current image generative models have achieved a remarkably realistic image quality, offering numerous academic and industrial applications. However, to ensure these models are used for benign purposes, it is essential to develop tools that definitively detect whether an image has been synthetically generated. Consequently, several detectors with excellent performance in computer vision applications have been developed. However, these detectors cannot be directly applied as they areto multi-spectral satellite images, necessitating the training of new models. While two-class classifiers generally achieve high detection accuracies, they struggle to generalize to image domains and generative architectures different from those encountered during training. In this paper, we propose a one-class classifier based on Vector Quantized Variational Autoencoder 2 (VQ-VAE 2) features to overcome the limitations of two-class classifiers. We start by highlighting the generalization problem faced by binary classifiers. This was demonstrated by training and testing an EfficientNet-B4 architecture on multiple multi-spectral datasets. We then illustrate that the VQ-VAE 2-based classifier, which was trained exclusively on pristine images, could detect images from different domains and generated by architectures not encountered during training. Finally, we conducted a head-to-head comparison between the two classifiers on the same generated datasets, emphasizing the superior generalization capabilities of the VQ-VAE 2-based detector, wherewe obtained a probability of detection at a 0.05 false alarm rate of 1 for the blue and red channels when using the VQ-VAE 2-based detector, and 0.72 when we used the EfficientNet-B4 classifier.
AFRNet: Anchor-Free Object Detection Using Roadside LiDAR in Urban Scenes
Authors: Wang Luyang , Lan Jinhui , Li Min
Published: February 2024

In urban settings, roadside infrastructure LiDAR is a ground-based remote sensing system that collects 3D sparse point clouds for the traffic object detection of vehicles, pedestrians, and cyclists. Current anchor-free algorithms for 3D point cloud object detection based on roadside infrastructure face challenges related to inadequate feature extraction, disregard for spatial information in large 3D scenes, and inaccurate object detection. In this study, we propose AFRNet, a two-stage anchor-free detection network, to address the aforementioned challenges. We propose a 3D feature extraction backbone based on the large sparse kernel convolution (LSKC) feature set abstraction module, and incorporate the CBAM attention mechanism to enhance the large scene feature extraction capability and the representation of the point cloud features, enabling the network to prioritize the object of interest. After completing the first stage of center-based prediction, we propose a refinement method based on attentional feature fusion, where fused features incorporating raw point cloud features, voxel features, BEV features, and key point features are used for the second stage of refinement to complete the detection of 3D objects. To evaluate the performance of our detection algorithms, we conducted experiments using roadside LiDAR data from the urban traffic dataset DAIR-V2X, based on the Beijing High-Level Automated Driving Demonstration Area. The experimental results show that AFRNet has an average of 5.27 percent higher detection accuracy than CenterPoint for traffic objects. Comparative tests further confirm that our method achieves high accuracy in roadside LiDAR object detection.
Fast Digital Orthophoto Generation: A Comparative Study of Explicit and Implicit Methods
Authors: Lv Jianlin , Jiang Guang , Ding Wei , Zhao Zhihao
Published: February 2024

A digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields. This paper classifies the algorithms for digital orthophoto generation into two groups: explicit methods and implicit methods. Explicit methods rely on traditional geometric methods, obtaining geometric structure presented with explicit parameters with Multi-View Stereo (MVS) theories, as seen in our proposed Top view constrained Dense Matching (TDM). Implicit methods rely on neural rendering, obtaining implicit neural representation of scenes through the training of neural networks, as exemplified by Neural Radiance Fields (NeRFs). Both of them obtain digital orthophotos via rendering from a top-view perspective. In addition, this paper conducts an in-depth comparative study between explicit and implicit methods. The experiments demonstrate that both algorithms meet the measurement accuracy requirements and exhibit a similar level of quality in terms of generated results. Importantly, the explicit method shows a significant advantage in terms of efficiency, with a time consumption reduction of two orders of magnitude under our latest Compute Unified Device Architecture (CUDA) version TDM algorithm. Although explicit and implicit methods differ significantly in their representation forms, they share commonalities in the implementation across algorithmic stages. These findings highlight the potential advantages of explicit methods in orthophoto generation while also providing beneficial references and practical guidance for fast digital orthophoto generation using implicit methods.
Stripe Extraction of Oceanic Internal Waves Using PCGAN with Small-Data Training
Authors: Duan Bohuai , Barintag Saheya , Meng Junmin , Gong Maoguo
Published: February 2024

Playing a crucial role in ocean activities, internal solitary waves (ISWs) are of significant importance. Currently, the use of deep learning for detecting ISWs in synthetic aperture radar (SAR) imagery is gaining growing attention. However, these approaches often demand a considerable number of labeled images, which can be challenging to acquire in practice. In this study, we propose an innovative method employing a pyramidal conditional generative adversarial network (PCGAN). At each scale, it employs the framework of a conditional generative adversarial network (CGAN), comprising a generator and a discriminator. The generator works to produce internal wave patterns as authentically as possible, while the discriminator is designed to differentiate between images generated by the generator and reference images. The architecture based on pyramids adeptly captures the encompassing as well as localized characteristics of internal waves. The incorporation of upsampling further bolsters the model’s ability to recognize fine-scale internal wave stripes. These attributes endow the PCGAN with the capacity to learn from a limited amount of internal wave observation data. Experimental results affirm that the PCGAN, trained with just four internal wave images, can accurately detect internal wave stripes in the test set. Through comparative experiments with other segmentation models, we demonstrate the effectiveness and robustness of PCGAN.
Open-Pit Granite Mining Area Extraction Using UAV Aerial Images and the Novel GIPNet
Authors: Meng Xiaoliang , Zhang Ding , Dong Sijun , Yao Chunjing
Published: February 2024

The ability to rapidly and accurately delineate open-pit granite mining areas is pivotal for effective production planning and environmental impact assessment. Over the years, advancements in remote sensing techniques, including the utilization of satellite imagery, LiDAR technology and unmanned aerial vehicles, have revolutionized the way mining areas are monitored and managed. Simultaneously, in the context of the open-pit mining area extraction task, deep learning-based automatic recognition is gradually replacing manual visual interpretation. Leveraging the potential of unmanned aerial vehicles (UAVs) for real-time, low-risk remote sensing, this study employs UAV-derived orthophotos for mining area extraction. Central to the proposed approach is the novel Gather–Injection–Perception (GIP) module, designed to overcome the information loss typically associated with conventional feature pyramid modules during feature fusion. The GIP module effectively enriches semantic features, addressing a crucial information limitation in existing methodologies. Furthermore, the network introduces the Boundary Perception (BP) module, uniquely tailored to tackle the challenges of blurred boundaries and imprecise localization in mining areas. This module capitalizes on attention mechanisms to accentuate critical high-frequency boundary details in the feature map and synergistically utilizes both high- and low-dimensional feature map data for deep supervised learning. The suggested method demonstrates its superiority in a series of comparative experiments on a specially assembled dataset of research area images. The results are compelling, with the proposed approach achieving 90.67% precision, 92.00% recall, 91.33% F1-score, and 84.04% IoU. These figures not only underscore the effectiveness of suggested model in enhancing the extraction of open-pit granite mining areas but also provides a new idea for the subsequent application of UAV data in the mining scene.
Evaluation of Six Data Products of Surface Downward Shortwave Radiation in Tibetan Plateau Region
Authors: Li Tianci , Xin Xiaozhou , Zhang Hailong , Yu Shanshan , Li Li , Ye Zhiqiang , Liu Qinhuo , Cai He
Published: February 2024

The quantitative characterization of the thermal conditions in the Tibetan Plateau has long been a focal point of global research. Downward shortwave radiation, as a crucial component, plays an important role in numerous land surface processes while also serving as a significant indicator of the plateau’s thermal state. In order to gain a more comprehensive understanding of the Earth’s radiation budget in the Tibetan Plateau region, this study undertook an evaluation of six radiation products (ISCCP-FH, CERES-SYN, GLASS DSR, Himawari-8, MCD18A1, and ERA5). Two sets of ground measurements (downward shortwave radiation values from 10 CMA sites and 6 sites provided by the National Tibetan Plateau Data Center) in 2015 and 2016 were used as validation data to verify the accuracy of the remote sensing products. The results show that in the Tibetan Plateau region, CERESC products show the highest accuracy among the six data products with a bias (relative bias) of −7.57 W/m2 (3.46%), RMSE (relative RMSE) of 32.77 W/m2 (14.99%), and coefficient of determination of 0.80. Among all products, only the ERA5 products overestimated the value of downward shortwave radiation in the Tibetan Plateau region with a bias (relative bias) of 15.62 W/m2 (7.14%). By employing a spatial resolution upscaling approach, we assessed the influence of varying spatial resolutions on the validation accuracy, with the results indicating minimal impact. Through an analysis of the impact of cloud factors and aerosol factors on the validation accuracy, it is deduced that ERA5, Himawari-8, and MCD18A1 products are significantly influenced by cloud factors, whereas the CERES-SYN product is notably affected by aerosol factors.
Automating Ground Control Point Detection in Drone Imagery: From Computer Vision to Deep Learning
Authors: Muradás Odriozola Gonzalo , Pauly Klaas , Oswald Samuel , Raymaekers Dries
Published: February 2024

Drone-based photogrammetry typically requires the task of georeferencing aerial images by detecting the center of Ground Control Points (GCPs) placed in the field. Since this is a very labor-intensive task, it could benefit greatly from automation. In this study, we explore the extent to which traditional computer vision approaches can be generalized to deal with variability in real-world drone data sets and focus on training different residual neural networks (ResNet) to improve generalization. The models were trained to detect single keypoints of fixed-sized image tiles with a historic collection of drone-based Red–Green–Blue (RGB) images with black and white GCP markers in which the center was manually labeled by experienced photogrammetry operators. Different depths of ResNets and various hyperparameters (learning rate, batch size) were tested. The best results reached sub-pixel accuracy with a mean absolute error of 0.586. The paper demonstrates that this approach to drone-based mapping is a promising and effective way to reduce the human workload required for georeferencing aerial images.
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model’s Generalizability in Permafrost Mapping
Authors: Li Wenwen , Hsu Chia-Yu , Wang Sizhe , Yang Yezhou , Lee Hyunho , Liljedahl Anna , Witharana Chandi , Yang Yili , Rogers Brendan M. , Arundel Samantha T. , Jones Matthew B. , McHenry Kenton , Solis Patricia
Published: February 2024

This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models and their defining characteristics. Built upon the tremendous success achieved by Large Language Models (LLMs) as the foundation models for language tasks, this paper discusses the challenges of building foundation models for geospatial artificial intelligence (GeoAI) vision tasks. To evaluate the performance of large AI vision models, especially Meta’s Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model. A series of prompt strategies were developed to test SAM’s performance regarding its theoretical upper bound of predictive accuracy, zero-shot performance, and domain adaptability through fine-tuning. The analysis used two permafrost feature datasets, ice-wedge polygons and retrogressive thaw slumps because (1) these landform features are more challenging to segment than man-made features due to their complicated formation mechanisms, diverse forms, and vague boundaries; (2) their presence and changes are important indicators for Arctic warming and climate change. The results show that although promising, SAM still has room for improvement to support AI-augmented terrain mapping. The spatial and domain generalizability of this finding is further validated using a more general dataset EuroCrops for agricultural field mapping. Finally, we discuss future research directions that strengthen SAM’s applicability in challenging geospatial domains.
Multiscale Change Detection Domain Adaptation Model Based on Illumination–Reflection Decoupling
Authors: Fan Rongbo , Xie Jialin , Yang Jianhua , Hong Zenglin , Xu Yuqi , Hou Hong
Published: February 2024

In the change detection (CD) task, the substantial variation in feature distributions across different CD datasets significantly limits the reusability of supervised CD models. To alleviate this problem, we propose an illumination–reflection decoupled change detection multi-scale unsupervised domain adaptation model, referred to as IRD-CD-UDA. IRD-CD-UDA maintains its performance on the original dataset (source domain) and improves its performance on unlabeled datasets (target domain) through a novel CD-UDA structure and methodology. IRD-CD-UDA synergizes mid-level global feature marginal distribution domain alignment, classifier layer feature conditional distribution domain alignment, and an easy-to-hard sample selection strategy to increase the generalization performance of CD models on cross-domain datasets. Extensive experiments conducted on the LEVIR, SYSU, and GZ optical remote sensing image datasets demonstrate that the IRD-CD-UDA model effectively mitigates feature distribution discrepancies between source and target CD data, thereby achieving optimal recognition performance on unlabeled target domain datasets.
Urban Visual Localization of Block-Wise Monocular Images with Google Street Views
Authors: Li Zhixin , Li Shuang , Anderson John , Shan Jie
Published: February 2024

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day.
Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms
Authors: Wang Lukang , Zhang Min , Gao Xu , Shi Wenzhong
Published: February 2024

Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field.
Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition
Authors: Xing Kang , Li Shiyan , Qu Zhijie , Zhang Xiaojuan
Published: February 2024

Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques.
High-Precision Map Construction in Degraded Long Tunnel Environments of Urban Subways
Authors: Li Cheng , Pan Wenbo , Yuan Xiwen , Huang Wenyu , Yuan Chao , Wang Quandong , Wang Fuyuan
Published: February 2024

In response to the demand for high-precision point cloud mapping of subway trains in long tunnel degradation scenarios in major urban cities, we propose a map construction method based on LiDAR and inertial measurement sensors. This method comprises a tightly coupled frontend odometry system based on error Kalman filters and backend optimization using factor graphs. In the frontend odometry, inertial calculation results serve as predictions for the filter, and residuals between LiDAR points and local map plane point clouds are used for filter updates. The global pose graph is constructed based on inter-frame odometry and other constraint factors, followed by a smoothing optimization for map building. Multiple experiments in subway tunnel scenarios demonstrate that the proposed method achieves robust trajectory estimation in long tunnel scenes, where classical multi-sensor fusion methods fail due to sensor degradation. The proposed method achieves a trajectory consistency of 0.1 m in tunnel scenes, meeting the accuracy requirements for train arrival, parking, and interval operations. Additionally, in an industrial park scenario, the method is compared with ground truth provided by inertial navigation, showing an accumulated error of less than 0.2%, indicating high precision.