bluuebunny/crossref_metadata_2025 huggingface dataset:
Pretargeted Alpha Therapy of Disseminated Cancer Combining Click Chemistry and Astatine-211
Authors: Timperanza Chiara , Jensen Holger , Bäck Tom , Lindegren Sture , Aneheim Emma
Published: April 2023

To enhance targeting efficacy in the radioimmunotherapy of disseminated cancer, several pretargeting strategies have been developed. In pretargeted radioimmunotherapy, the tumor is pretargeted with a modified monoclonal antibody that has an affinity for both tumor antigens and radiolabeled carriers. In this work, we aimed to synthesize and evaluate poly-L-lysine-based effector molecules for pretargeting applications based on the tetrazine and trans-cyclooctene reaction using 211At for targeted alpha therapy and 125I as a surrogate for the imaging radionuclides 123, 124I. Poly-L-lysine in two sizes was functionalized with a prosthetic group, for the attachment of both radiohalogens, and tetrazine, to allow binding to the trans-cyclooctene-modified pretargeting agent, maintaining the structural integrity of the polymer. Radiolabeling resulted in a radiochemical yield of over 80% for astatinated poly-L-lysines and a range of 66–91% for iodinated poly-L-lysines. High specific astatine activity was achieved without affecting the stability of the radiopharmaceutical or the binding between tetrazine and transcyclooctene. Two sizes of poly-L-lysine were evaluated, which displayed similar blood clearance profiles in a pilot in vivo study. This work is a first step toward creating a pretargeting system optimized for targeted alpha therapy with 211At.
Zebrafish Patient-Derived Xenograft Model as a Preclinical Platform for Uveal Melanoma Drug Discovery
Authors: Yin Jie , Zhao Gangyin , Kalirai Helen , Coupland Sarah E. , Jochemsen Aart G. , Forn-Cuní Gabriel , Wierenga Annemijn P. A. , Jager Martine J. , Snaar-Jagalska B. Ewa , Groenewoud Arwin
Published: April 2023

Uveal melanoma (UM) is a rare malignant cancer of the eye, with up to 50% of patients dying from metastasis, for which no effective treatment is available. Due to the rarity of the disease, there is a great need to harness the limited material available from primary tumors and metastases for advanced research and preclinical drug screening. We established a platform to isolate, preserve, and transiently recover viable tissues, followed by the generation of spheroid cultures derived from primary UM. All assessed tumor-derived samples formed spheroids in culture within 24 h and stained positive for melanocyte-specific markers, indicating the retention of their melanocytic origin. These short-lived spheroids were only maintained for the duration of the experiment (7 days) or re-established from frozen tumor tissue acquired from the same patient. Intravenous injection of fluorescently labeled UM cells derived from these spheroids into zebrafish yielded a reproducible metastatic phenotype and recapitulated molecular features of the disseminating UM. This approach allowed for the experimental replications required for reliable drug screening (at least 2 individual biological experiments, with n > 20). Drug treatments with navitoclax and everolimus validated the zebrafish patient-derived model as a versatile preclinical tool for screening anti-UM drugs and as a preclinical platform to predict personalized drug responses.
The Role of Natural Flavonoids as Telomerase Inhibitors in Suppressing Cancer Growth
Authors: Parekh Neel , Garg Ashish , Choudhary Renuka , Gupta Madhu , Kaur Ginpreet , Ramniwas Seema , Shahwan Moyad , Tuli Hardeep Singh , Sethi Gautam
Published: April 2023

Cancer is a complex and multifaceted group of diseases characterized by the uncontrolled growth and spread of abnormal cells. While cancer can be challenging and life-altering, advances in research and development have led to the identification of new promising anti-cancer targets. Telomerase is one such target that is overexpressed in almost all cancer cells and plays a critical role in maintaining telomere length, which is essential for cell proliferation and survival. Inhibiting telomerase activity can lead to telomere shortening and eventual cell death, thus presenting itself as a potential target for cancer therapy. Naturally occurring flavonoids are a class of compounds that have already been shown to possess different biological properties, including the anti-cancer property. They are present in various everyday food sources and richly present in fruits, nuts, soybeans, vegetables, tea, wine, and berries, to name a few. Thus, these flavonoids could inhibit or deactivate telomerase expression in cancer cells by different mechanisms, which include inhibiting the expression of hTERT, mRNA, protein, and nuclear translocation, inhibiting the binding of transcription factors to hTERT promoters, and even telomere shortening. Numerous cell line studies and in vivo experiments have supported this hypothesis, and this development could serve as a vital and innovative therapeutic option for cancer. In this light, we aim to elucidate the role of telomerase as a potential anti-cancer target. Subsequently, we have illustrated that how commonly found natural flavonoids demonstrate their anti-cancer activity via telomerase inactivation in different cancer types, thus proving the potential of these naturally occurring flavonoids as useful therapeutic agents.
Electrospun Magnetic Nanofiber Mats for Magnetic Hyperthermia in Cancer Treatment Applications—Technology, Mechanism, and Materials
Authors: Mamun Al , Sabantina Lilia
Published: April 2023

The number of cancer patients is rapidly increasing worldwide. Among the leading causes of human death, cancer can be regarded as one of the major threats to humans. Although many new cancer treatment procedures such as chemotherapy, radiotherapy, and surgical methods are nowadays being developed and used for testing purposes, results show limited efficiency and high toxicity, even if they have the potential to damage cancer cells in the process. In contrast, magnetic hyperthermia is a field that originated from the use of magnetic nanomaterials, which, due to their magnetic properties and other characteristics, are used in many clinical trials as one of the solutions for cancer treatment. Magnetic nanomaterials can increase the temperature of nanoparticles located in tumor tissue by applying an alternating magnetic field. A very simple, inexpensive, and environmentally friendly method is the fabrication of various types of functional nanostructures by adding magnetic additives to the spinning solution in the electrospinning process, which can overcome the limitations of this challenging treatment process. Here, we review recently developed electrospun magnetic nanofiber mats and magnetic nanomaterials that support magnetic hyperthermia therapy, targeted drug delivery, diagnostic and therapeutic tools, and techniques for cancer treatment.
Thermal Properties and In Vitro Biodegradation of PLA-Mg Filaments for Fused Deposition Modeling
Authors: Leonés Adrián , Salaris Valentina , Ramos Aranda Ignacio , Lieblich Marcela , López Daniel , Peponi Laura
Published: April 2023

Additive manufacturing, in particular the fused deposition method, is a quite new interesting technique used to obtain specific 3D objects by depositing layer after layer of material. Generally, commercial filaments can be used in 3D printing. However, the obtention of functional filaments is not so easy to reach. In this work, we obtain filaments based on poly(lactic acid), PLA, reinforced with different amounts of magnesium, Mg, microparticles, using a two-step extrusion process, in order to study how processing can affect the thermal degradation of the filaments; we additionally study their in vitro degradation, with a complete release of Mg microparticles after 84 days in phosphate buffer saline media. Therefore, considering that we want to obtain a functional filament for further 3D printing, the simpler the processing, the better the result in terms of a scalable approach. In our case, we obtain micro-composites via the double-extrusion process without degrading the materials, with good dispersion of the microparticles into the PLA matrix without any chemical or physical modification of the microparticles.
Poloxamer/Carboxymethyl Pullulan Aqueous Systems—Miscibility and Thermogelation Studies Using Viscometry, Rheology and Dynamic Light Scattering
Authors: Popescu Irina , Constantin Marieta , Bercea Maria , Coșman Bogdan-Paul , Suflet Dana Mihaela , Fundueanu Gheorghe
Published: April 2023

Thermally-induced gelling systems based on Poloxamer 407 (PL) and polysaccharides are known for their biomedical applications; however, phase separation frequently occurs in mixtures of poloxamer and neutral polysaccharides. In the present paper, the carboxymethyl pullulan (CMP) (here synthesized) was proposed for compatibilization with poloxamer (PL). The miscibility between PL and CMP in dilute aqueous solution was studied by capillary viscometry. CMP with substitution degrees higher than 0.5 proved to be compatible with PL. The thermogelation of concentrated PL solutions (17%) in the presence of CMP was monitored by the tube inversion method, texture analysis and rheology. The micellization and gelation of PL in the absence or in the presence of CMP were also studied by dynamic light scattering. The critical micelle temperature and sol–gel transition temperature decrease with the addition of CMP, but the concentration of CMP has a peculiar influence on the rheological parameters of the gels. In fact, low concentrations of CMP decrease the gel strength. With a further increase in polyelectrolyte concentration, the gel strength increases until 1% CMP, then the rheological parameters are lowered again. At 37 °C, the gels are able to recover the initial network structure after high deformations, showing a reversible healing process.
Optimization of Process Parameters for Carbon Fiber Reinforced Polyamide 6 Composites Fabricated by Self-Resistance Electric Heating Technology
Authors: Zhai Zhanyu , Du Yu , Wang Xiaoyu
Published: April 2023

As an energy-saving and efficient composites-forming technology, the properties of carbon fiber self-resistance electric (SRE) heating technology still need to be improved, which is not conducive to the popularization and application of this technology. To deal with this problem, the SRE heating technology was combined with a compression molding process to form carbon-fiber-reinforced polyamide 6 (CF/PA 6) composite laminates in this study. Orthogonal experiments of three factors (temperature, pressure, and impregnation time) were designed to study the effect of process parameters on the impregnation quality and mechanical properties of CF/PA 6 composite laminates and to obtain the optimized set of process parameters. Furthermore, the effect of the cooling rate on crystallization behaviors and mechanical properties of laminates was studied according to the optimized settings. The results show that the laminates possess a good comprehensive forming quality under process parameters using a forming temperature of 270 °C, forming pressure of 2.5 MPa, and an impregnation time of 15 min. The ununiform impregnation rate is due to the ununiform temperature field in the cross-section. When the cooling rate decreases from 29.56 °C/min to 2.64 °C/min, the crystallinity of the PA 6 matrix increases from 25.97% to 37.22%; the α-phase of the matrix crystal phase also increases significantly. The effect of the cooling rate on crystallization properties also further affects the impact properties; laminates with a faster cooling rate have stronger impact resistance.
Economic Dispatch of Combined Heat and Power Plant Units within Energy Network Integrated with Wind Power Plant
Authors: Kaur Paramjeet , Chaturvedi Krishna Teerth , Kolhe Mohan Lal
Published: April 2023

Cogeneration, also known as a combined heat and power (CHP) system, produces both power and heat simultaneously. It reduces the operating costs and emissions by utilising waste heat from steam turbines and contributes to incapacitating the intermittency of renewable energy. The CHP-economic dispatch (CHP-ED) is needed to overcome the load dynamics as well as renewable intermittency. In this work, a CHP system connected with a wind power plant is considered for analysing the CHPED within a typical power system area. This study examines, the CHPED with and without a wind integrated energy network. The main objective of this work is to minimise the total operating cost, while meeting the generators’ constraints and prioritising the wind power output. The feasible operating region, valve point loading impact, and prohibited working regions of the CHP plants are taken while finding a CHPED solution with an integrated wind turbine. To find a CHPED solution, an optimisation algorithm was applied and the algorithm was based on selecting the best and worst scenarios. A typical 48-unit structure was used for validating the considered technique’s success for CHPED with/without a wind power plant. In our investigation, we found that operational costs were significantly reduced with a wind energy system. The presented methodology will be useful for the CHPED process of the decentralised CHP units for promoting further integration of the wind turbines and other distributed clean energy resources.
Survival of Single Immediate Implants and Reasons for Loss: A Systematic Review
Authors: Soegiantho Patricia , Suryawinata Patricia Gillian , Tran Wendy , Kujan Omar , Koyi Bryar , Khzam Nabil , Algarves Miranda Leticia
Published: April 2023

Background: Immediate implant placement (IIP) or Type I implants have become more attractive than conventional implant placements as it reduces the number of surgical procedures and allows faster delivery of the final restoration compared to conventional implant placements. However, the survival of Type I implants varies depending on multiple factors. Purpose: To evaluate the survival rate of Type I implants, and to describe the factors influencing their failure. Materials and methods: A developed search strategy was applied to identify randomised controlled trials on single-unit immediate implants including at least six human participants with a minimum follow-up time of 12 months and published between 1 January 1999 and 1 January 2020 in several databases. The data were extracted independently using validated data extraction forms. Information on survival rates, number of implants placed, loading protocols, setting of the study, location of implants in the jaw, antibiotic protocol, grafting methods, and implant geometry were obtained and assessed. Results: Twenty-six randomised controlled trials with an average follow-up time of 24 months (range = 12–120 months) were included and analysed to give a survival rate ranging between 83.7 and 100%. Fifteen studies reported implant failures, of which twelve reported early losses (loss before definitive restoration). Nine early losses were due to lack of osseointegration, two did not report the reason for implant failure, and one was reported as iatrogenic. Of the eleven studies with 100% survival rates, the common trend observed was the use of titanium implants and an antibiotic regimen using amoxicillin. Conclusions: The survival rate for immediate single implant placement ranged from 83.7 to 100%. Implant failure was not consistently reported and when reported, failure due to lack of osseointegration prior to placement of the definitive restoration was the most common descriptor. Other attributed reasons included infection abscess, mobility after immediate loading, and iatrogenic complications.
Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning
Authors: Kong Daqian , Yuan Dekun , Li Haojie , Zhang Jiahua , Yang Shanshan , Li Yue , Bai Yun , Zhang Sha
Published: April 2023

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUEmax (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R2 = 0.86 and RMSE = 1.79 gC m−2 d−1 on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models.
First Results from the WindRAD Scatterometer on Board FY-3E: Data Analysis, Calibration and Wind Retrieval Evaluation
Authors: Li Zhen , Verhoef Anton , Stoffelen Ad , Shang Jian , Dou Fangli
Published: April 2023

FY-3E WindRAD (Fengyun-3E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The diversity of the radar view geometry varies across the swaths, with maximum diversity in the sweet swaths and limited diversity in the outer and nadir swaths. When NOC backscatter calibration coefficients are computed as a function of incidence angle only (NOCint), a smooth correction is found. However, when relative antenna azimuth angle is included (NOCant), it appears that the corrections as a function of relative azimuth angle vary harmonically and substantially for a specific incidence angle. NOCant corrections yield a better fit of the measurements to the GMF (Geophysical Model Function). Hence, NOCant is applied for the analysis of wind retrieval from the Ku-band and C-band. An extra engineering correction of 0.15 dB and 0.20 dB is applied on Ku-band and C-band backscatter values, respectively, to reduce the wind speed bias without increasing the standard deviation. Overall, NOCant is the best option for both channels. In addition, the instrument backscatter data stability over time is good, and the retrieved winds can fulfill operational requirements.
Detector–Tracker Integration Framework for Autonomous Vehicles Pedestrian Tracking
Authors: Wang Huanhuan , Jin Lisheng , He Yang , Huo Zhen , Wang Guangqi , Sun Xinyu
Published: April 2023

Pedestrian tracking is an important aspect of autonomous vehicles environment perception in a vehicle running environment. The performance of the existing pedestrian tracking algorithms is limited by the complex traffic environment, the changeable appearance characteristics of pedestrians and the frequent occlusion interaction, which leads to the insufficient accuracy and stability of tracking. Therefore, this paper proposes a detector–tracker integration framework for autonomous vehicle pedestrian tracking. Firstly, a pedestrian objects detector based on the improved YOLOv7 network was established. Space-to-Depth convolution layer was adopted to improve the backbone network of YOLOv7. Then, a novel appearance feature extraction network is proposed, which integrates the convolutional structural re-parameterization idea to construct a full-scale feature extraction block, which is the optimized DeepSORT tracker. Finally, experiments were carried out on MOT17 and MOT20 public datasets and driving video sequences, and the tracking performance of the proposed framework was evaluated by comparing it with the most advanced multi-object tracking algorithms. Quantitative analysis results show that the framework has high tracking accuracy. Compared with DeepSORT, MOTA improves by 2.3% in the MOT17 dataset and MOTA improves by 4.2% in the MOT20 dataset. Through qualitative evaluation on real driving video sequences, the framework proposed in this paper is robust in a variety of climate environments, and can be effectively applied to the pedestrian tracking of autonomous vehicles.
Surface Defect Detection of Nanjing City Wall Based on UAV Oblique Photogrammetry and TLS
Authors: Wu Jiayi , Shi Yufeng , Wang Helong , Wen Yajuan , Du Yiwei
Published: April 2023

Ancient architecture, with its long history, has a high cultural value, artistic achievement, and scientific value. The Nanjing City Wall was constructed in the mid-to-late 14th century, and it ranks first among the world’s city walls in terms of both length and size, whether historically or in the contemporary era. However, these sites are subject to long-term degradation and are sensitive to disturbances from the surrounding landscape, resulting in the potential deterioration of the architecture. Therefore, it is urgent to detect the defects and repair and protect Nanjing City Wall. In this paper, a novel method is proposed to detect the surface defects of the city walls by using the unmanned aerial vehicle (UAV) oblique photogrammetry and terrestrial laser scanning (TLS) data. On the one hand, the UAV oblique photogrammetry was used to collect the image data of the city wall, and a three-dimensional (3D) model of the wall was created using the oblique images. With this model, 43 cracks with lengths greater than 30 cm and 15 shedding surfaces with an area greater than 300 cm2 on the wall can be effectively detected. On the other hand, the point cloud data obtained by TLS were firstly preprocessed, and then, the KNN algorithm was used to construct a local neighborhood for each sampling point, and the neighborhood was fitted using the least squares method. Next, five features of the point cloud were calculated, and the results were visualized. Based on the visualization results, surface defects of the wall were identified, and 18 cracks with lengths greater than 30 cm and 5 shedding surfaces with an area greater than 300 cm2 on the wall were detected. To verify the accuracy of these two techniques in measuring cracks, the coordinates of some cracks were surveyed using a prism-free total station, and the lengths were calculated. The root mean square error (RMSE) of crack lengths based on the UAV oblique photogrammetry model and TLS point cloud model were calculated to be 0.73 cm and 0.34 cm, respectively. The results of the study showed that both techniques were able to detect the defects on the wall surface, and the measurement accuracy could meet the accuracy requirements of the surface defect detection of the city wall. Considering their low cost and high efficiency, these two techniques provide help for the mapping and conservation of historical buildings, which is of great significance for the conservation and repair of ancient buildings.
A Novel Atmospheric Correction for Turbid Water Remote Sensing
Authors: Wang Dian , Xiang Xiangyu , Ma Ronghua , Guo Yongqin , Zhu Wangyuan , Wu Zhihao
Published: April 2023

For the remote sensing of turbid waters, the atmospheric correction (AC) is a key issue. The “black pixel” assumption helps to solve the AC for turbid waters. It has proved to be inaccurate to regard all water pixels in the SWIR (Short Wave Infrared) band as black pixels. It is necessary to perform atmospheric correction in the visible bands after removing the radiation contributions of water in the SWIR band. Here, the modified ACZI (m-ACZI) algorithm was developed. The m-ACZI assumes the spatial homogeneity of aerosol types and employs the BPI (Black Pixel Index) and PIFs (Pseudo-Invariant Features) to identify the “black pixel”. Then, the radiation contributions of waters in the SWIR band are removed to complete the atmospheric correction for turbid waters. The results showed that the m-ACZI had better performance than the SeaDAS (SeaWiFS Data Analysis System) -SWIR and the EXP (exponential extrapolation) algorithm in the visible band (sMAPE < 30.71%, RMSE < 0.0111 sr−1) and is similar to the DSF (Dark Spectrum Fitting) algorithm in floating algae waters. The m-ACZI algorithm is suitable for turbid inland waters.
A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images
Authors: Parelius Eleonora Jonasova
Published: April 2023

Remote sensing is a tool of interest for a large variety of applications. It is becoming increasingly more useful with the growing amount of available remote sensing data. However, the large amount of data also leads to a need for improved automated analysis. Deep learning is a natural candidate for solving this need. Change detection in remote sensing is a rapidly evolving area of interest that is relevant for a number of fields. Recent years have seen a large number of publications and progress, even though the challenge is far from solved. This review focuses on deep learning applied to the task of change detection in multispectral remote-sensing images. It provides an overview of open datasets designed for change detection as well as a discussion of selected models developed for this task—including supervised, semi-supervised and unsupervised. Furthermore, the challenges and trends in the field are reviewed, and possible future developments are considered.
Simulation of Cooling Island Effect in Blue-Green Space Based on Multi-Scale Coupling Model
Authors: Pan Ziwu , Xie Zunyi , Wu Liyang , Pan Yu , Ding Na , Liang Qiushuang , Qin Fen
Published: April 2023

The mitigation of the urban heat island effect is increasingly imperative in light of climate change. Blue–green space, integrating water bodies and green spaces, has been demonstrated to be an effective strategy for reducing the urban heat island effect and enhancing the urban environment. However, there is a lack of coupled analysis on the cooling island effect of blue–green space at the meso-micro scale, with previous studies predominantly focusing on the heat island effect. This study coupled the single urban canopy model (UCM) with the mesoscale Weather Research and Forecasting (WRF) numerical model to simulate the cooling island effect of blue–green space in the Eastern Sea-River-Stream-Lake Linkage Zone (ESLZ) within the northern subtropical zone. In particular, we comparatively investigated the cooling island effect of micro-scale blue–green space via three mitigation strategies of increasing vegetation, water bodies, and coupling blue–green space, using the temperature data at the block scale within 100 m square of the urban center on the hottest day in summer. Results showed that the longitudinally distributed lakes and rivers in the city had a significant cooling effect on the ambient air temperature (Ta) at the mesoscale, with the largest cooling range occurring during the daytime and ranging from 1.01 to 2.15 °C. In contrast, a 5~20% increase in vegetation coverage or 5~15% increase in water coverage at the micro-scale was observed to reduce day and night Ta by 0.71 °C. Additionally, the most significant decrease in physiologically equivalent temperature (PET) was found in the mid-rise building environment, with a reduction of 2.65–3.26 °C between 11:00 and 13:00 h, and an average decrease of 1.25°C during the day. This study aims to guide the optimization of blue–green space planning at the meso-micro scale for the fast-development and expansion of new urban agglomerations.
Evaluating the Losses and Recovery of GPP in the Subtropical Mangrove Forest Directly Attacked by Tropical Cyclone: Case Study in Hainan Island
Authors: Wu Lan , Guo Enliang , An Yinghe , Xiong Qian , Shi Xian , Zhang Xiang , Sun Zhongyi
Published: April 2023

The gross primary production (GPP) of the mangrove ecosystem determines the upper limit of the scale of its “blue carbon” sink. Tropical cyclones (TCs) are among the most important extreme events that threaten the subtropical mangrove ecosystem and have a serious impact on mangrove ecosystem GPP. However, there are somewhat insufficient scientific findings on regional-scale mangrove ecosystem GPP responding to large-scale weather events such as TCs. Therefore, we selected the subtropical Hainan Island mangrove ecosystem, where more than two TCs pass through per year, as the research area; selected direct-attack TCs as the research object; and took the mangrove vegetation photosynthesis light-use efficiency model established based on the eddy covariance observation data as the tool to evaluate the loss and recovery of mangrove ecosystem GPP after TCs attacked at a regional scale. We found that the TC impacted the mangrove ecosystem GPP through the photosynthetic area and rate, and the recovery of the rate occurred prior to the recovery of the area; the loss of mangrove ecosystem GPP is inversely proportional to the distance to the center of the TC and the distance to the coastline; and the canopy height, diameter at breast height, and aspect where the tree stands significantly influence the response of the mangrove ecosystem GPP to TCs. However, the response varies for different mangrove community compositions, soil conditions, and planting densities as well as different frequencies and intensities of TCs, and they should be analyzed in detail. This study is expected to provide technical and data support for the protection of blue carbon in a subtropical island mangrove ecosystem in response to extreme events and post-disaster recovery.
Evapotranspiration of Winter Wheat in the Semi-Arid Southeastern Loess Plateau Based on Multi-Source Satellite Data
Authors: He Peng , Bi Rutian , Xu Lishuai , Liu Zhengchun , Yang Fan , Wang Wenbiao , Cui Zhengnan , Wang Jingshu
Published: April 2023

Continuous monitoring of evapotranspiration (ET) at high spatio-temporal resolutions is vital for managing agricultural water resources in arid and semi-arid regions. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to calculate the ET of winter wheat between the green-up and milk stages in Linfen Basin, a typical, semi-arid area of the Loess Plateau, at temporal and spatial resolutions of 30 m and 8 d, respectively. We then analyzed the impact of meteorological factors on ET and its variation during the main growth period of winter wheat. The fused ET data displayed the spatial details of the OLI ET data better and could accurately reflect ET variation and local sudden variations during the main growth period of winter wheat. Moreover, winter wheat ET in rain-fed areas is more heavily influenced by meteorological factors, and the effect is more direct. Affected by the synergistic effect of wind velocity, precipitation, and temperature, the ET of winter wheat in rain-fed area was lower in the green-up stage. Then, ET gradually increased, reaching its maximum in the heading–grain filling stage. At the jointing stage, temperature had a significant effect on ET. A combination of precipitation and temperature had the greatest impact on the ET of winter wheat in the heading–filling stage. In the milk stage, meteorological factors had a minor impact on ET. This study serves as a reference for ET in winter wheat in semi-arid areas and its influencing meteorological factors, which can assist in drought mitigation and regional food security strategies.
A Multi-Feature Fusion and Attention Network for Multi-Scale Object Detection in Remote Sensing Images
Authors: Cheng Yong , Wang Wei , Zhang Wenjie , Yang Ling , Wang Jun , Ni Huan , Guan Tingzhao , He Jiaxin , Gu Yakang , Tran Ngoc Nguyen
Published: April 2023

Accurate multi-scale object detection in remote sensing images poses a challenge due to the complexity of transferring deep features to shallow features among multi-scale objects. Therefore, this study developed a multi-feature fusion and attention network (MFANet) based on YOLOX. By reparameterizing the backbone, fusing multi-branch convolution and attention mechanisms, and optimizing the loss function, the MFANet strengthened the feature extraction of objects at different sizes and increased the detection accuracy. The ablation experiment was carried out on the NWPU VHR-10 dataset. Our results showed that the overall performance of the improved network was around 2.94% higher than the average performance of every single module. Based on the comparison experiments, the improved MFANet demonstrated a high mean average precision of 98.78% for 9 classes of objects in the NWPU VHR-10 10-class detection dataset and 94.91% for 11 classes in the DIOR 20-class detection dataset. Overall, MFANet achieved an mAP of 96.63% and 87.88% acting on the NWPU VHR-10 and DIOR datasets, respectively. This method can promote the development of multi-scale object detection in remote sensing images and has the potential to serve and expand intelligent system research in related fields such as object tracking, semantic segmentation, and scene understanding.
Remote Sensing-Based Estimates of Changes in Stored Groundwater at Local Scales: Case Study for Two Groundwater Subbasins in California’s Central Valley
Authors: Ahamed Aakash , Knight Rosemary , Alam Sarfaraz , Morphew Michael , Susskind Tea
Published: April 2023

Sustainable groundwater management requires high-quality and low-latency estimates of changes in groundwater storage (∆Sgw). However, estimates of ∆Sgw produced using traditional methods, including groundwater models and well-based measurements, typically lag years behind the present because collecting the required on-the-ground data is a time consuming, expensive, and labor-intensive task. Satellite remote sensing measurements provide potential pathways to overcome these limitations by quantifying ∆Sgw through closing the water balance. However, the range of spatial scales over which ∆Sgw can be accurately estimated using remote sensing products remains unclear. To bridge this knowledge gap, this study quantified ∆Sgw for the period of 2002 through to 2021 using the water balance method and multiple remote sensing products in two subbasins (~2700 km2–3500 km2) within California’s Central Valley: (1) the Kaweah–Tule Subbasin, a region where the pumping of groundwater to support agriculture has resulted in decades of decline in head levels, resulting in land subsidence, damage to infrastructure, and contamination of drinking water and (2) the Butte Subbasin, which receives considerably more rainfall and surface water and has not experienced precipitous drops in groundwater. The remote sensing datasets which we utilized included multiple sources for key hydrologic components in the study area: precipitation, evapotranspiration, and soil moisture. To assess the fidelity of the remote sensing-based model, we compared estimates of ∆Sgw to alternative estimates of ∆Sgw derived from independent sources of data: groundwater wells as well as a widely used groundwater flow model. The results showed strong agreement in the Kaweah–Tule Subbasin in long-term ∆Sgw trends and shorter-term trends during droughts, and modest agreement in the Butte Subbasin with remote sensing datasets suggesting more seasonal variability than validation datasets. Importantly, our analysis shows that the timely availability of remote sensing data can potentially enable ∆Sgw estimates at sub-annual latencies, which is timelier than estimates derived through alternate methods, and thus can support adaptive management and decision making. The models developed herein can aid in assessing aquifer dynamics, and can guide the development of sustainable groundwater management practices at spatial scales relevant for management and decision making.
A Method to Downscale MODIS Surface Reflectance Using Convolutional Neural Networks
Authors: Zhang Yunteng , Xiao Zhiqiang
Published: April 2023

Surface reflectance is an important indicator for the physical states of the Earth’s surface. The Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product at 500 m resolution (MOD09A1) includes seven spectral bands and has been widely used to derive many high-level parameter products, such as leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR). However, the MODIS surface reflectance product at 250 m resolution (MOD09Q1) is only available for the red and near-infrared (NIR) bands, which greatly limits its applications. In this study, a downscaling reflectance convolutional neural network (DRCNN) is proposed to downscale the surface reflectance of the MOD09A1 product and derive 250 m surface reflectance in the blue, green, shortwave infrared (SWIR1, 1628–1652 nm) and shortwave infrared (SWIR2, 2105–2155 nm) bands for generating high-level parameter products at 250 m resolution. The surface reflectance of the MOD09A1 and MOD09Q1 products are preprocessed to obtain cloud-free continuous surface reflectance. Additionally, the surface reflectance in the blue, green, SWIR1 and SWIR2 bands from the preprocessed MOD09A1 product were upsampled to obtain surface reflectance in the corresponding bands at 1 km resolution. Then, a database was generated from the upsampled surface reflectance and the preprocessed MOD09A1 product over the On Line Validation Exercise (OLIVE) sites to train the DRCNN. The surface reflectance in the blue, green, SWIR1 and SWIR2 bands from the preprocessed MOD09A1 product and the surface reflectance in the red and NIR bands from the preprocessed MOD09Q1 product were entered into the trained DRCNN to obtain the surface reflectance in the blue, green, SWIR1 and SWIR2 bands at 250 m resolution. The downscaled surface reflectance from the DRCNN were compared with the surface reflectance from the MOD09A1 product and Landsat 7. The results show that the DRCNN can effectively downscale the surface reflectance of the MOD09A1 product to generate the surface reflectance at 250 m resolution.
Detection of Aphids on Hyperspectral Images Using One-Class SVM and Laplacian of Gaussians
Authors: Peignier Sergio , Lacotte Virginie , Duport Marie-Gabrielle , Baa-Puyoulet Patrice , Simon Jean-Christophe , Calevro Federica , Heddi Abdelaziz , da Silva Pedro
Published: April 2023

Aphids cause severe damage to agricultural crops, resulting in significant economic losses, and an increased use of pesticides with decreased efficiency. Monitoring aphid infestations through regular field surveys is time-consuming and does not always provide an accurate spatiotemporal representation of the distribution of pests. Therefore, an automated, non-destructive method to detect and evaluate aphid infestation would be beneficial for targeted treatments. In this study, we present a machine learning model to identify and quantify aphids, localizing their spatial distribution over leaves, using a One-Class Support Vector Machine and Laplacian of Gaussians blob detection. To train this model, we built the first large database of aphids’ hyperspectral images, which were captured in a controlled laboratory environment. This database contains more than 160 images of three aphid lines, distinctive in color, shape, and developmental stages, and are displayed laying on leaves or neutral backgrounds. This system exhibits high-quality validation scores, with a Precision of 0.97, a Recall of 0.91, an F1 score of 0.94, and an AUPR score of 0.98. Moreover, when assessing this method on new and challenging images, we did not observe any false negatives (and only a few false positives). Our results suggest that a machine learning model of this caliber could be a promising tool to detect aphids for targeted treatments in the field.
A Target Imaging and Recognition Method Based on Raptor Vision
Authors: Xu Bitong , Li Zhengzhou , Cheng Bei , Yang Yuxin , Siddique Abubakar
Published: April 2023

It is a big challenge to quickly and accurately recognize targets in a complex background. The mutual constraints between a wide field of vision (FOV) and high resolution affect the optical tracking and imaging ability in a wide area. In nature, raptors possess unique imaging structures and optic nerve systems that can accurately recognize prey. This paper proposes an imaging system combined with a deep learning algorithm based on the visual characteristics of raptors, aiming to achieve wide FOV, high spatial resolution, and accurate recognition ability. As for the imaging system, two sub-optical systems with different focal lengths and various-size photoreceptor cells jointly simulate the deep fovea of a raptor’s eye. The one simulating the peripheral region has a wide FOV and high sensitivity for capturing the target quickly by means of short focal length and large-size photoreceptor cells, and the other imitating the central region has high resolution for recognizing the target accurately through the long focal length and small-size photoreceptor cells. Furthermore, the proposed algorithm with an attention and feedback network based on octave convolution (AOCNet) simulates the mechanism of the optic nerve pathway by adding it into the convolutional neural network (CNN), thereby enhancing the ability of feature extraction and target recognition. Experimental results show that the target imaging and recognition system eliminates the limitation between wide FOV and high spatial resolution, and effectively improves the accuracy of target recognition in complex backgrounds.
Geometric Prior-Guided Self-Supervised Learning for Multi-View Stereo
Authors: Liu Liman , Zhang Fenghao , Su Wanjuan , Qi Yuhang , Tao Wenbing
Published: April 2023

Recently, self-supervised multi-view stereo (MVS) methods, which are dependent primarily on optimizing networks using photometric consistency, have made clear progress. However, the difference in lighting between different views and reflective objects in the scene can make photometric consistency unreliable. To address this issue, a geometric prior-guided multi-view stereo (GP-MVS) for self-supervised learning is proposed, which exploits the geometric prior from the input data to obtain high-quality depth pseudo-labels. Specifically, two types of pseudo-labels for self-supervised MVS are proposed, based on the structure-from-motion (SfM) and traditional MVS methods. One converts the sparse points of SfM into sparse depth maps and combines the depth maps with spatial smoothness constraints to obtain a sparse prior loss. The other generates initial depth maps for semi-dense depth pseudo-labels using the traditional MVS, and applies a geometric consistency check to filter the wrong depth in the initial depth maps. We conducted extensive experiments on the DTU and Tanks and Temples datasets, which demonstrate that our method achieves state-of-the-art performance compared to existing unsupervised/self-supervised approaches, and even performs on par with traditional and supervised approaches.
Land Subsidence Phenomena vs. Coastal Flood Hazard—The Cases of Messolonghi and Aitolikon (Greece)
Authors: Antoniadis Nikolaos , Alatza Stavroula , Loupasakis Constantinos , Kontoes Charalampos (Haris)
Published: April 2023

Land subsidence in coastal and delta cities often results in infrastructure and residential building damages, while also increasing the area’s flooding vulnerability. The coastal cities of Messolonghi and Aitolikon are typical examples, as they are built on top of old stream deposits near the coast. In the last several years, the gradual subsidence of the sites, combined with the impact of climate change, resulted in multiple floods. The rush of seawater over the lowlands has also been reported. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor soil displacements. In this study, a novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing (BEYOND) of the National Observatory of Athens, as well as the Copernicus EGMS product were used to identify these displacements. The results were examined in correlation with other potential factors such as the overexploitation of the underground water, the natural compaction of the clay soil layers, the primary and secondary consolidation due to the external construction loading, the oxidation of the organic soils, tidal gauge data, precipitation data, and ground truth data. In Messolonghi, various deformation rates were recorded, with maximum mean values of −5 mm/year in the eastern part, whereas in Aitolikon, the maximum values were around −4.5 mm/year. The displacements were mostly attributed to the primary consolidation due to the building loads. Deformation patterns and their correlation with precipitation could also be witnessed. It was evident that the increased precipitation rates and sea level rise played a leading role in the constant flooding.
Characterization of Three Surges of the Kyagar Glacier, Karakoram
Authors: Zhang Zhen , Zhao Jinbiao , Liu Shiyin , Zhang Qibing , Jiang Zongli , Xu Yangyang , Su Haoran
Published: April 2023

Glaciers experience periodic variations in flow velocity called surges, each of which influences the glacier’s characteristics and the occurrence of downstream disasters (e.g., ice-dammed lake outburst floods). The Karakoram region contains many surging glaciers, yet there are few comprehensive studies of multiple surge cycles. In this work, Landsat, topographic map, Shuttle Radar Topography Mission (SRTM), TerraSAR-X/TanDEM-X, ITS_LIVE, and Sentinel-1 glacier velocity data were used to systematically analyze the characteristics of Kyagar Glacier since the 1970s. Three surging events were identified, with active phases in 1975–1978, 1995–1997, and 2014–2016. The timing of these surges was similar, with a cycle of 19–20 years, an active phase of 3–4 years, and a quiescent phase of 16–17 years. During the quiescent phase, a large amount of ice accumulates in the lower part of the accumulation zone, and the terminal of the tongue thins significantly. According to the most recent surge event (2014–2016), glacier flow accelerated suddenly in the active phase and reached a maximum velocity of 2 ± 0.08 m d−1. Then, the glacier terminal thickened sharply, the reservoir zone thinned by 12 ± 0.2 m, and the terminal receiving zone thickened by 28 ± 0.2 m. The glacier may have entered a quiescent phase after July 2016. The glacier surge causes a large amount of material to transfer from upstream to downstream, forming an ice dam and creating conditions for a glacial lake outburst flood (GLOF). At the termination of the active phase, the subglacial drainage channel became effective, triggering the GLOF. For a period of the quiescent phase, the glacier ablation intensifies and the GLOF repeats constantly. One surge caused 7–8 GLOFs, and then a continuous reduction in the ice dam elevation. Eventually, the ice dam disappeared, and the GLOF no longer continued before the next glacier-surging event.