bluuebunny/crossref_metadata_2025 huggingface dataset:
Cordycepin Attenuates Testosterone-Induced Benign Prostatic Hyperplasia in Rats via Modulation of AMPK and AKT Activation
Authors: Alamoudi Abdulmohsin J. , Alessi Sami A. , Rizg Waleed Y. , Jali Abdulmajeed M. , Safhi Awaji Y. , Sabei Fahad Y. , Alshehri Sameer , Hosny Khaled M. , Abdel-Naim Ashraf B.
Published: August 2022

Benign prostatic hyperplasia (BPH) is a disease that commonly affects elderly men. Cordycepin is an adenosine analog with a wide range of pharmacological activities including antiproliferative and prostatic smooth muscle relaxant effects. This study was designed to assess the actions of cordycepin in testosterone-induced BPH in rats. Animals were divided into six treatment groups: control, cordycepin-alone (10 mg/kg), testosterone-alone (3 mg/kg), cordycepin (5 mg/kg) + testosterone, cordycepin (10 mg/kg) + testosterone, and finasteride (0.5 mg/kg) + testosterone. Treatments were continued daily, 5 days a week, for 4 weeks. Cordycepin significantly prevented the increase in prostate weight and prostate index induced by testosterone. This was confirmed by histopathological examinations. Cordycepin antiproliferative activity was further defined by its ability to inhibit cyclin-D1 and proliferating cell nuclear antigen (PCNA) expression. In addition, cordycepin exhibited significant antioxidant properties as proven by the prevention of lipid peroxidation, reduced glutathione diminution, and superoxide dismutase exhaustion. This was paralleled by anti-inflammatory activity as shown by the inhibition of interleukin-6, tumor necrosis factor-α, and nuclear factor-κB expression in prostatic tissues. It also enhanced apoptosis as demonstrated by its ability to enhance and inhibit mRNA expression of Bax and Bcl2, respectively. Western blot analysis indicated that cordycepin augmented phospho-AMP-activated protein kinase (p-AMPK) and inhibited p-AKT expression. Collectively, cordycepin has the ability to prevent testosterone-induced BPH in rats. This is mediated, at least partially, by its antiproliferative, antioxidant, anti-inflammatory, and pro-apoptotic actions in addition to its modulation of AMPK and AKT activation.
Solid State Nanostructured Metal Oxides as Photocatalysts and Their Application in Pollutant Degradation: A Review
Authors: Díaz Carlos , Segovia Marjorie , Valenzuela Maria Luisa
Published: August 2022

Most dyes used in various industries are toxic and carcinogenic, thus posing a serious hazard to humans as well as to the marine ecosystem. Therefore, the impact of dyes released into the environment has been studied extensively in the last few years. Heterogeneous photocatalysis has proved to be an efficient tool for degrading both atmospheric and aquatic organic contaminants. It uses the sunlight in the presence of a semiconductor photocatalyst to accelerate the remediation of environmental contaminants and the destruction of highly toxic molecules. To date, photocatalysis has been considered one of the most appealing options for wastewater treatment due to its great potential and high efficiency by using sunlight to remove organic pollutants and harmful bacteria with the aid of a solid photocatalyst. Among the photocatalysts currently used, nanostructured metal oxide semiconductors have been among the most effective. This review paper presents an overview of the recent research improvements on the degradation of dyes by using nanostructured metal oxide semiconductors obtained by a solid-state method. Metal oxides obtained by this method exhibited better photocatalytic efficiency than nanostructured metal oxides obtained using other solution methods in several cases. The present review discusses examples of various nanostructured transition metal oxides—such as TiO2, Fe2O3, NiO, ReO3, IrO2, Rh2O3, Rh/RhO2, and the actinide ThO2—used as photocatalysts on methylene blue. It was found that photocatalytic efficiency depends not only on the bandgap of the metal oxide but also on its morphology. Porous nanostructured metal oxides tend to present higher photocatalytic efficiency than metal oxides with a similar band gap.
Multifunction Web-like Polymeric Network Bacterial Cellulose Derived from SCOBY as Both Electrodes and Electrolytes for Pliable and Low-Cost Supercapacitor
Authors: Hamsan Muhamad Hafiz , Halim Norhana Abdul , Demon Siti Zulaikha Ngah , Sa’aya Nurul Syahirah Nasuha , Kadir Mohd Fakhrul Zamani , Abidin Zul Hazrin Zainal , Poad Nursaadah Ahmad , Kasim Nurul Farhana Abu , Razali Nur Amira Mamat , Aziz Shujahadeen B. , Ahmad Khairol Amali , Miskon Azizi , Nor Norazman Mohamad
Published: August 2022

In this work, bacterial cellulose (BC)-based polymer derived from a symbiotic culture of bacteria and yeast (SCOBY) are optimized as both electrodes and electrolytes to fabricate a flexible and free-standing supercapacitor. BC is a multifunction and versatile polymer. Montmorillonite (MMT) and sodium bromide (NaBr) are used to improve mechanical strength and as the ionic source, respectively. From XRD analysis, it is found that the addition of MMT and NaBr has reduced the crystallinity of the electrolyte. Most interaction within the electrolyte happens in the region of the OH band, as verified using FTIR analysis. A maximum room temperature conductivity of (1.09 ± 0.02) × 10−3 S/cm is achieved with 30 wt.% NaBr. The highest conducting SCOBY-based electrolytes have a decompose voltage and ionic transference number of 1.48 V and 0.97, respectively. The multiwalled carbon nanotube is employed as the active material held by the fibrous network of BC. Cyclic voltammetry shows a rectangular shape CV plot with the absence of a redox peak. The supercapacitor is charged and discharged in a zig-zag-shaped Perspex plate for 1000 cycles with a decent performance.
The Analysis of Acute and Subacute Toxicity of Silver Selenide Nanoparticles Encapsulated in Arabinogalactan Polymer Matrix
Authors: Titov Evgeniy A. , Sosedova Larisa M. , Novikov Mikhail A. , Zvereva Marina V. , Rukavishnikov Viktor S. , Lakhman Oleg L.
Published: August 2022

The acute and subacute toxicity of a newly synthesized silver selenide nanoparticles encapsulated in a natural polymeric matrix of arabinogalactan study has been studied. The nanocomposite is a promising material for the design of diagnostic and therapeutic drugs. It can also be used for the preparation of fluorescent labels and in thermal oncotherapy. The employment of binary nanocomposites enables one to unveil the potential hidden in metals which constitute these composites. The study of acute toxicity, carried out by the oral administration of nanocomposites at a dose of 2000 mg/kg, has shown that the compound belongs to low-toxic substances of the 5th hazard class. With the subacute oral administration of nanocomposites at a dose of 500 μg/kg, slight changes are observed in the brain tissue and liver of experimental animals, indicating the development of compensatory–adaptive reactions. In the kidneys, the area of the Shumlyansky–Bowman chamber decreases by 40.5% relative to the control group. It is shown that the application of the protective properties of selenium, which is contained in the composite, helps to reduce the toxicity of silver.
The Study of Manufacturing Thermal Insulation Materials Based on Inorganic Polymers under Microwave Exposure
Authors: Rymar Tatyana , Tatarchenko Halyna , Fomin Oleksij , Píštěk Václav , Kučera Pavel , Beran Martin , Burlutskyy Oleksij
Published: August 2022

The work is devoted to the creation of an energy-saving microwave technology of composite materials for thermal insulation based on an inorganic polymer—liquid glass—and the establishment of the formation patterns of their structure and properties, depending on the parameters of microwave radiation. Due to volumetric heating and the mechanism of "non-thermal" action of microwave radiation on processed objects, the duration of their heating is significantly reduced, and the performance properties of products are improved due to the modification of the structure of the liquid glass matrix under the influence of its irradiation with this type of energy. In the course of the research, the relationship between the processing conditions and the obtained properties of heat-insulating materials was established. The prospects of using the microwave electromagnetic field in the production of new building materials, expanding their range, and improving quality and competitiveness are shown, which is a priority for the modernization and innovative development of the construction industry.
Fabrication of Low-Molecular-Weight Hyaluronic Acid–Carboxymethyl Cellulose Hybrid to Promote Bone Growth in Guided Bone Regeneration Surgery: An Animal Study
Authors: Lin Chun-Yu , Kuo Po-Jan , Lin Ya-Hui , Lin Chi-Yu , Lin Jerry Chin-Yi , Chiu Hsien-Chung , Hung Tsung-Fu , Lin Hung-Yun , Huang Haw-Ming
Published: August 2022

Guided bone regeneration surgery is an important dental operation used to regenerate enough bone to successfully heal dental implants. When this technique is performed on maxilla sinuses, hyaluronic acid (HLA) can be used as an auxiliary material to improve the graft material handling properties. Recent studies have indicated that low-molecular hyaluronic acid (L-HLA) provides a better regeneration ability than high-molecular-weight (H-HLA) analogues. The aim of this study was to fabricate an L-HLA-carboxymethyl cellulose (CMC) hybrid to promote bone regeneration while maintaining viscosity. The proliferation effect of fabricated L-HLA was tested using dental pulp stem cells (DPSCs). The mitogen-activated protein kinase (MAPK) pathway was examined using cells cultured with L-HLA combined with extracellular-signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and p38 inhibitors. The bone growth promotion of fabricated L-HLA/CMC hybrids was tested using an animal model. Micro-computer tomography (Micro-CT) and histological images were evaluated quantitatively to compare the differences in the osteogenesis between the H-HLA and L-HLA. Our results show that the fabricated L-HLA can bind to CD44 on the DPSC cell membranes and affect MAPK pathways, resulting in a prompt proliferation rate increase. Micro CT images show that new bone formation in rabbit calvaria defects treated with L-HLA/CMC was almost two times higher than in defects filled with H-HLA/CMC (p < 0.05) at 4 weeks, a trend that remained at 8 weeks and was confirmed by HE-stained images. According to these findings, it is reasonable to conclude that L-HLA provides better bone healing than H-HLA, and that the L-HLA/CMC fabricated in this study is a potential candidate for improving bone healing efficiency when a guided bone regeneration surgery was performed.
The Separation of Chlorobenzene Compounds from Environmental Water Using a Magnetic Molecularly Imprinted Chitosan Membrane
Authors: Li Guizhen , Wang Jinyao , Zhu Peng , Han Ying , Yu Anqi , Li Junhong , Sun Zhaomei , Row Kyung Ho
Published: August 2022

In this work, a magnetic molecularly imprinted chitosan membrane (MMICM) was synthesized for the extraction of chlorobenzene compounds in environmental water using the membrane separation method. The optimal extraction amount for chlorobenzene (9.64 mg·L−1) was found to be a 1:2 solid to liquid ratio, with a 20 min extraction time and 35 °C extraction temperature. This method proved to be successfully applied for the separation and trace quantification of chlorobenzene compounds in environmental water, with the limit of detection (LOD) (0.0016–0.057 ng·L−1), limit of quantification (LOQ) (0.0026–0.098 ng·L−1), and the recoveries ranging (89.02–106.97%).
Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models
Authors: Liao Zhenhao , Qiu Cheng , Yang Jun , Yang Jinglei , Yang Lei
Published: August 2022

Experimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorporation of the HFT and CLT were used to generate the training dataset. Instead of directly mapping the relationship between the ply angles of the laminate and its strength and stiffness, a multi-layer interconnected neural network (NN) system was built following the logical sequence of composite theories. With the forward prediction by the NN system and the inverse optimization by genetic algorithm (GA), a benchmark case of designing a composite tube subjected to the combined loads of bending and torsion was studied. The ML models successfully provided the optimal layup sequences and the required fiber modulus according to the preset design targets. Additionally, it shows that the machine learning models, with the guidance of composite theories, realize a faster optimization process and requires less training data than models with direct simple NNs. Such results imply the importance of domain knowledge in helping improve the ML applications in engineering problems.
A Systematic Classification Method for Grassland Community Division Using China’s ZY1-02D Hyperspectral Observations
Authors: Wei Dandan , Liu Kai , Xiao Chenchao , Sun Weiwei , Liu Weiwei , Liu Lidong , Huang Xizhi , Feng Chunyong
Published: August 2022

The main feature of grassland degradation is the change in the vegetation community structure. Hyperspectral-based grassland community identification is the basis and a prerequisite for large-area high-precision grassland degradation monitoring and management. To obtain the distribution pattern of grassland communities in Xilinhot, Inner Mongolia Autonomous Region, China, we propose a systematic classification method (SCM) for hyperspectral grassland community identification using China’s ZiYuan 1-02D (ZY1-02D) satellite. First, the sample label data were selected from the field-collected samples, vegetation map data, and function zoning data for the Nature Reserve. Second, the spatial features of the images were extracted using extended morphological profiles (EMPs) based on the reduced dimensionality of principal component analysis (PCA). Then, they were input into the random forest (RF) classifier to obtain the preclassification results for grassland communities. Finally, to reduce the influence of salt-and-pepper noise, the label similarity probability filter (LSPF) method was used for postclassification processing, and the RF was again used to obtain the final classification results. The results showed that, compared with the other seven (e.g., SVM, RF, 3D-CNN) methods, the SCM obtained the optimal classification results with an overall classification accuracy (OCA) of 94.56%. In addition, the mapping results of the SCM showed its ability to accurately identify various ground objects in large-scale grassland community scenes.
Validation of IMERG Oceanic Precipitation over Kwajalein
Authors: Wang Jianxin , Wolff David B. , Tan Jackson , Marks David A. , Pippitt Jason L. , Huffman George J.
Published: August 2022

The integrated Multi-satellitE Retrievals for GPM (IMERG) Version V05B and V06B precipitation products from the Global Precipitation Measurement (GPM) mission are validated against ground-based observations from the Kwajalein Polarimetric S-band Weather Radar (KPOL) deployed at Kwajalein Atoll in the central Pacific Ocean. Such a validation is particularly important as comprehensive surface measurements over the oceans are practically infeasible, which hampers the identification of possible errors, and improvement of future versions of IMERG and other satellite-based retrieval algorithms. The V05B and V06B IMERG products are validated at their native 0.1°, 30 min resolution from 2014 to 2018 based on both volumetric and categorical metrics. This validation study indicates that precipitation rates from both IMERG V05B and V06B are underestimated with respect to radar surface estimates, but the underestimation is much reduced from V05B to V06B. IMERG V06B outperforms V05B with reduced systematic bias and improved precipitation detectability. The IMERG performance is further traced back to its individual sensors and morphing-based algorithms. The overall underestimation in V05B is mainly driven by the negative relative biases from morphing-based algorithms which are largely corrected in V06B. Imagers perform generally better than sounders because of the usage of low-frequency channels in imagers which can better detect emission signals by the hydrometeors. Among imagers, the GPM Microwave Imager (GMI) and Advanced Microwave Scanning Radiometer Version 2 (AMSR2) are the best, followed by Special Sensor Microwave Imager/Sounder (SSMIS). Among sounders, the Microwave Humidity Sounder (MHS) is the best, followed by Advanced Technology Microwave Sounder (ATMS) and the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) for V06B. Among all categories, morph-only and IR + morph only perform better than SAPHIR. SAPHIR shows the worst performance among all categories, likely due to its limited channel selection. It is envisaged that these results will improve our understanding of IMERG performance over oceans and aid in the improvement of future versions of IMERG.
Relationship between Urban Three-Dimensional Spatial Structure and Population Distribution: A Case Study of Kunming’s Main Urban District, China
Authors: Wang Yang , Yue Xiaoli , Li Cansong , Wang Min , Zhang Hong’ou , Su Yongxian
Published: August 2022

The three-dimensional (3D) spatial structure within cities can reveal more information about land development than the two-dimensional spatial structure. Studying the relationship between the urban 3D spatial structure and the population distribution is a crucial aspect of the relationship between people and land within cities. However, a few relevant studies focus on the differences between employment population and night population distribution in relation to urban 3D spatial structure. Therefore, this study proposes a new concept of 3D space-filling degree (3DSFD), which is applicable to evaluate the city’s 3D spatial structure. We took 439 blocks in Kunming’s Main Urban District as a sample and analyzed the 3D spatial structure based on geographic information data at the scale of a single building. The characteristics and differences of the daytime and night population distribution in Kunming’s Main Urban District were identified using cell phone signaling big data. Accordingly, a cross-sectional dataset of the relationship between the city’s 3D spatial structure and the population distribution was constructed, with the 3D space-filling degree of the block as the dependent variable, two indicators of population distribution (daytime and night population density) as the explanatory variables, and seven indicators of distance from the city center, and building, road, and functional place densities, proportion of undevelopable land area, housing prices, and land use type as the control variables. We used spatial regression models to explore the significance, strength, and direction of the relationship between urban 3D spatial structure and population distribution. We found that the spatial error model (SEM) was the most effective. The results show that only night population distribution is significantly and positively related to 3DSFD. Every 1% increase in night population density in a block will increase the value of 3DSFD by 2.8307%. The night population distribution is the core factor affecting the 3D spatial structure of Kunming’s Main Urban District. The correlation between daytime population distribution and 3DSFD is not significant. This variability has been ignored in previous studies. The findings are informative for further understanding of the relationship between urban 3D space and population distribution, especially the difference between night and daytime populations. This study can help city managers reasonably plan urban land development intensity and construction height, guide the population layout and formulate management policies to improve urban population and space matching, enhancing the livability and attractiveness of cities.
Smart Count System Based on Object Detection Using Deep Learning
Authors: Moon Jiwon , Lim Sangkyu , Lee Hakjun , Yu Seungbum , Lee Ki-Baek
Published: August 2022

Object counting is an indispensable task in manufacturing and management. Recently, the development of image-processing techniques and deep learning object detection has achieved excellent performance in object-counting tasks. Accordingly, we propose a novel small-size smart counting system composed of a low-cost hardware device and a cloud-based object-counting software server to implement an accurate counting function and overcome the trade-off presented by the computing power of local hardware. The cloud-based object-counting software consists of a model adapted to the object-counting task through a novel DBC-NMS (our own technique) and hyperparameter tuning of deep-learning-based object-detection methods. With the power of DBC-NMS and hyperparameter tuning, the performance of the cloud-based object-counting software is competitive over commonly used public datasets (CARPK and SKU110K) and our custom dataset of small pills. Our cloud-based object-counting software achieves an mean absolute error (MAE) of 1.03 and a root mean squared error (RMSE) of 1.20 on the Pill dataset. These results demonstrate that the proposed smart counting system accurately detects and counts densely distributed object scenes. In addition, the proposed system shows a reasonable and efficient cost–performance ratio by converging low-cost hardware and cloud-based software.
Real-Time Source Apportionment of PM2.5 Highlights the Importance of Joint Controls on Atmospheric Pollution in Cold Region of China
Authors: Chen Weiwei , Zhang Mengduo , Liu Wei , Fu Jing , Guo Li
Published: August 2022

Harbin is a northmost megacity in the cold regions of China and experiences severe PM2.5 pollution. However, comprehensive investigations for severe haze formation are few. In this study, we simultaneously measured aerosol composition in real time to assess the sources apportionment, regional transport and its interaction with meteorology from 1 October 2018 to 1 May 2019 by using the single particle aerosol mass spectrometer (SPAMS). The daily average PM2.5 concentration was 51.21 µg/m3 with the hourly maximum of 900.45 µg/m3. Winter coal combustion was the largest source of PM2.5 aerosols during this period. Open straw burning from surrounding and adjacent areas by short-distance transport could aggravate air quality deterioration in Harbin. Three extreme haze events (i.e., Ep1, Ep2 and Ep3) were observed in this study, showing the typical characteristics of local winter pollution. The pollutants of PM2.5 and SO2 emitted from coal combustion played an important role in haze episode during Ep1, whereas Ep2 was caused by the joint effect of coal combustion and straw burning. Ep3 was characterized by long-distance transport of windblown dust from southeast Inner Mongolia and northwest Harbin. Real-time source apportionment of fine particulate matter highlights the importance of joint control of coal and straw burning from the surrounding cities of Harbin.
The Nature of the Ny-Ålesund Wind Field Analysed by High-Resolution Windlidar Data
Authors: Graßl Sandra , Ritter Christoph , Schulz Alexander
Published: August 2022

In this work we present windlidar data for the research village Ny-Ålesund located on Svalbard in the European Arctic (78.923°N, 11.928°E) from 2013 to 2021. The data have a resolution of 50 m and 10 min with an overlapping height of about 150 m. The maximum range depends on the meteorologic situation. Up to 1000 m altitude the data availability is better than 71%. We found that the highest wind speeds occur in November and December, the lowest ones in June and July, up to 500 m altitude the wind is channelled strongly in ESE to NW direction parallel to the fjord axis and the synoptic conditions above 1000 m altitude already dominate. While the fraction of windy days (v>10ms) varies significantly from month to month, there is no overall trend of the wind visible in our data set. We define gusts and jets by the requirement of wind maxima v>2ms above and below a wind maximum. In total, more than 24,000 of these events were identified (corresponding to 6% of the time), of which 223 lasted for at least 100 min (“Long Jets”). All of these events are fairly equally distributed over the months relatively to the available data. Further, gusts and jets follow different distributions (in terms of altitude or depths) and occur more frequently for synoptic flow from roughly a southerly direction. Jets do not show a clear correlation between occurrence and synoptic flow. Gusts and jets are not related to cloud cover. We conclude that the atmosphere from 400 m to 1000 m above Ny-Ålesund is dominated by a turbulent wind shear zone, which connects the micrometeorology in the atmospheric boundary layer (ABL) with the synoptic flow.
MDESNet: Multitask Difference-Enhanced Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images
Authors: Zheng Jiaxiang , Tian Yichen , Yuan Chao , Yin Kai , Zhang Feifei , Chen Fangmiao , Chen Qiang
Published: August 2022

Building change detection is a prominent topic in remote sensing applications. Scholars have proposed a variety of fully-convolutional-network-based change detection methods for high-resolution remote sensing images, achieving impressive results on several building datasets. However, existing methods cannot solve the problem of pseudo-changes caused by factors such as “same object with different spectrums” and “different objects with same spectrums” in high-resolution remote sensing images because their networks are constructed using simple similarity measures. To increase the ability of the model to resist pseudo-changes and improve detection accuracy, we propose an improved method based on fully convolutional network, called multitask difference-enhanced Siamese network (MDESNet) for building change detection in high-resolution remote sensing images. We improved its feature extraction ability by adding semantic constraints and effectively utilized features while improving its recognition performance. Furthermore, we proposed a similarity measure combining concatenation and difference, called the feature difference enhancement (FDE) module, and designed comparative experiments to demonstrate its effectiveness in resisting pseudo-changes. Using the building change detection dataset (BCDD), we demonstrate that our method outperforms other state-of-the-art change detection methods, achieving the highest F1-score (0.9124) and OA (0.9874), indicating its advantages for high-resolution remote sensing image building change detection tasks.
An Improved Vicarious Calibration Method Based on Multi-Grayscale Targets
Authors: Bao Shiwei , Chen Hongyao , Li Yan , Zhang Liming , Huang Wenxin , Si Xiaolong , Wang Xianhua , Fang Zhou , Chen Yuanwei , Wang Xinrong , Zhao Xiaowen
Published: August 2022

Vicarious calibration is a well-developed method for electro-optical (EO) sensor calibration that has been used since the early 1980s. The radiometric calibration of reflectance solar band is mainly applied to reflection inversion. In this paper, a radiometric calibration-reflectance inversion (RCRII) model is proposed as an improved vicarious calibration method. Taking the reflectance of grayscale targets with constant spectrum, suitable uniformity, and near-Lambertian characteristics as the known information, the grayscale target calibration is realized, and the initial value of calibration coefficient and offset are calculated. Then, the adjacency effect is evaluated and corrected by reflectance inversion, and the results are fed back to the calibration process to realize the iterative process of calibration inversion rescaling. The results indicate that the absolute difference between the reflectance calculated with the RCRII model and measured reflectance is less than 0.01. By comparing with Sentinel-2A images, it is cross-verified that the difference of radiance between them is within 4%, and the absolute reflectance difference is less than 0.01, in the range of 0.1~0.3 reflectance.
Mapping Dynamic Turbidity Maximum Zone of the Yellow River Estuary from 38 Years of Landsat Imagery
Authors: Chang Maoxiang , Li Peng , Sun Yue , Wang Houjie , Li Zhenhong
Published: August 2022

Turbidity maximum zone (TMZ) plays a crucial role in estuarine ecosystems, exerting effects on erosion, environment evolution and socioeconomic activities in the coastal area. However, the long-term understanding of the TMZ in large river estuary such as the Yellow River estuary is still lacking. In this study, we focus on the TMZ distribution, variation and regulation mechanisms in the Yellow River estuary from different time scales. Based on time series Landsat images during the period 1984 to 2021 and Google Earth Engine (GEE), we proposed a TMZ extracting method in the Yellow River estuary to generate 322 TMZ maps. The overall accuracy of our algorithm reached 97.4%. The results show that there are clear decadal and seasonal TMZ variations during the 38-year period in the Yellow River estuary. Morphology, currents and wind speeds combined with seawater stratification have direct effects on TMZ at different time scales, while the direct impacts of tides and fluvial output of the Yellow River on TMZ are limited. In this article, the highly robust method provides a cost-effective alternative to accurately map the TMZ in global large river estuaries and systematically reveals the spatiotemporal evolution of TMZ, shedding light on the response mechanism of coastal geomorphology, marine ecological environment and biogeochemical cycle.
Many-Objective RadarCom Signal Design via NSGA-II Genetic Algorithm Implementation and Simulation Analysis
Authors: Washington Richard , Garmatyuk Dmitriy , Mudaliar Saba , Narayanan Ram M.
Published: August 2022

In this communication, we investigate the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) in many-objective optimization scenarios pertaining to joint radar and communication functionality. We introduce five objectives relevant to sensing and secure communications and develop a cost function where these objectives can be individually prioritized by a user. We consider three scenarios: Radar Priority, Communication Priority, and All (Objectives) Equal; we then demonstrate the optimization results using an orthogonal frequency-division multiplexing (OFDM) radarcom signal. The objectives with selected weights are shown to improve system performance and thereby validate the viability of our approach. The Radar Priority scenario showed the best improvement in probability of detection, PSLR, and PAPR. Compared to the baseline performance values, the improvements were: from 94.05% to 96%, from 11.7 to 13.6 dB, and from 9.46 to 7.09 dB, respectively. The communication scenario saw the best improvement in BER and clutter similarity (measured by NRMSE) from 3.52% to 0.39% and 0.87 to 0.59, respectively.
First Ever Observations of Mineral Dust in Wintertime over Warsaw, Poland
Authors: Szczepanik Dominika M. , Ortiz-Amezcua Pablo , Heese Birgit , D’Amico Giuseppe , Stachlewska Iwona S.
Published: August 2022

The long-range transport of desert dust over the area of the temperate climate zone is associated with the influx of hot air masses due to the location of the sources of this aerosol in the tropical climate zone. Between 24–26 February 2021, such an aerosol outbreak took place and reached Central Europe. The mean temperature of +11.7 °C was recorded during the event. A comparison of this value to the 20-year (2000–2020) average February temperature for Warsaw (−0.2 °C) indicates the uniqueness of the meteorological conditions. It was the first wintertime inflow of Saharan dust over Warsaw, the presence of which was confirmed by lidar and sun-photometer measurements. The properties of the desert dust layers were obtained; the mean values of the particle depolarization for the fully developed mineral dust layer were 13 ± 3% and 22 ± 4% for 355 and 532 nm, respectively. The aerosol optical thickness was high with average values >0.36 for all wavelengths smaller than 500 nm. The three-modal, aerosol size distribution was dominated by coarse-mode particles, with a visible contribution of accumulation-mode particles. It suggests the possible presence of other aerosol types.
A Survey on Visual Navigation and Positioning for Autonomous UUVs
Authors: Qin Jiangying , Li Ming , Li Deren , Zhong Jiageng , Yang Ke
Published: August 2022

Autonomous navigation and positioning are key to the successful performance of unmanned underwater vehicles (UUVs) in environmental monitoring, oceanographic mapping, and critical marine infrastructure inspections in the sea. Cameras have been at the center of attention as an underwater sensor due to the advantages of low costs and rich content information in high visibility ocean waters, especially in the fields of underwater target recognition, navigation, and positioning. This paper is not only a literature overview of the vision-based navigation and positioning of autonomous UUVs but also critically evaluates the methodologies which have been developed and that directly affect such UUVs. In this paper, the visual navigation and positioning algorithms are divided into two categories: geometry-based methods and deep learning-based. In this paper, the two types of SOTA methods are compared experimentally and quantitatively using a public underwater dataset and their potentials and shortcomings are analyzed, providing a panoramic theoretical reference and technical scheme comparison for UUV visual navigation and positioning research in the highly dynamic and three-dimensional ocean environments.
Influence of Plasma Sheath’s Velocity Field on ISAR Imaging of Hypersonic Target
Authors: Xie Yaocong , Li Xiaoping , Shen Fangfang , Mao Zheng , Bai Bowen , Chen Xuyang
Published: August 2022

Plasma sheath poses a serious challenge to inverse synthetic aperture radar (ISAR) imaging of hypersonic targets. This paper investigated the distribution characteristics of the electron density and velocity field in the plasma sheath surrounding the hypersonic target in various flight scenes. The incident depth and reflective surface of electromagnetic (EM) waves with X-band, Ku-band, and Ka-band can be determined based on the plasma frequency. We established the echo model coupled with the velocity field of the plasma sheath on the reflective surface and obtained one-dimensional range profiles and ISAR images of the hypersonic target in various flight scenes. The simulation results indicated that the non-uniform velocity field on the reflective surface induced displacement and diffusion in the one-dimensional range profile, resulting in ISAR image distortion. A changing flight scene and radar frequency can have an impact on imaging results. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) were utilized to assess the impact of plasma sheath on ISAR images. This study revealed the defocus mechanism of the ISAR image caused by the velocity field of the plasma sheath and provided a theoretical reference for the selection of radar frequency for hypersonic targets in various flight scenes.
Why Do Inverse Eddy Surface Temperature Anomalies Emerge? The Case of the Mediterranean Sea
Authors: Moschos Evangelos , Barboni Alexandre , Stegner Alexandre
Published: August 2022

It is widely accepted that the signature of anticyclonic (cyclonic) eddies on the sea surface temperature corresponds to a warm (cold) core anomaly. Nevertheless, this statement has been put to question by recent regional studies showing the existence of inverse eddy SST anomalies: Cold Core anticyclones and, respectively, Warm Core cyclones. This study shows that the emergence of these inverse anomalies is a seasonal phenomenon that affects the life cycle of mesoscale eddies in the Mediterranean Sea. We use remote sensing observations and in situ data to analyse the eddy-induced SST anomaly over a 3-year period (2016–2018). We build an eddy core SST anomaly index to quantify the amount of Cold Core anticyclones and Warm Core cyclones all over the year and especially during the spring re-stratification period. We find that 70% of eddy anomalies are inverse in May and June both for cyclones and anticyclones. Regular temperature anomalies could reach 1.5 °C, while inverse ones are only present in the first 50 m of the oceanic layer and hardly exceed 1 °C. In order to understand the underlying dynamical processes, we construct a simple vertical column model to study the impact of the seasonal air–sea fluxes on the surface stratification inside and outside eddies. It is only by taking into account a differential diapycnal eddy mixing—increased in anticyclones and reduced in cyclones—that we reproduce correctly, in agreement with the observations, the surface temperature inversion in the eddy core. This simplified model suggests that vertical mixing modulation by mesoscale eddies might be the key mechanism that leads to the eddy–SSTA seasonal inversion in the ocean.
Deep Learning with Adaptive Attention for Seismic Velocity Inversion
Authors: Li Fangda , Guo Zhenwei , Pan Xinpeng , Liu Jianxin , Wang Yanyi , Gao Dawei
Published: August 2022

The subsurface velocity model is crucial for high-resolution seismic imaging. Although full-waveform inversion (FWI) is a high-accuracy velocity inversion method, it inevitably suffers from challenging problems, including human interference, strong nonuniqueness, and high computing costs. As an efficient and accurate nonlinear algorithm, deep learning (DL) has been used to estimate velocity models. However, conventional DL is insufficient to characterize detailed structures and retrieve complex velocity models. To address the aforementioned problems, we propose a hybrid network (AG-ResUnet) involving fully convolutional layers, attention mechanism, and residual unit to estimate velocity models from common source point (CSP) gathers. Specifically, the attention mechanism extracts the boundary information, which serves as a structural constraint in network training. We introduce the structural similarity index (SSIM) to the loss function, which minimizes the misfit between predicted velocity and ground truth. Compared with FWI and other networks, AG-ResUnet is more effective and efficient. Experiments on transfer learning and noisy data inversion demonstrate that AG-ResUnet makes a generalized and robust velocity prediction with rich structural details. The synthetic examples demonstrate that our method can improve seismic velocity inversion, contributing to guiding the imaging of geological structures.
Lateral Border of a Small River Plume: Salinity Structure, Instabilities and Mass Transport
Authors: Osadchiev Alexander , Gordey Alexandra , Barymova Alexandra , Sedakov Roman , Rogozhin Vladimir , Zhiba Roman , Dbar Roman
Published: August 2022

The interfaces between small river plumes and ambient seawater have extremely sharp horizontal and vertical salinity gradients, often accompanied by velocity shear. It results in formation of instabilities at the lateral borders of small plumes. In this study, we use high-resolution aerial remote sensing supported by in situ measurements to study these instabilities. We describe their spatial and temporal characteristics and then reconstruct their relation to density gradient and velocity shear. We report that Rayleigh–Taylor instabilities, with spatial scales ~5–50 m, are common features of the sharp plume-sea interfaces and their sizes are proportional to the Atwood number determined by the cross-shore density gradient. Kelvin–Helmholtz instabilities have a smaller size (~3–7 m) and are formed at the plume border in case of velocity shear >20–30 cm/s. Both instabilities induce mass transport across the plume-sea interfaces, which modifies salinity structure of the plume borders and induces lateral mixing of small river plumes. In addition, aerial observations revealed wind-driven Stokes transport across the sharp plume-sea interface, which occurs in the shallow (~2–3 cm) surface layer. This process limitedly affects salinity structure and mixing at the plume border, however, it could be an important issue for the spread of river-borne floating particles in the ocean.
Finite-Region Approximation of EM Fields in Layered Biaxial Anisotropic Media
Authors: Kang Zhuangzhuang , Wang Hongnian , Yin Changchun
Published: August 2022

A new algorithm is developed to accurately compute the electromagnetic (EM) fields in the layered biaxial anisotropic media. We enclose the computational region in an infinitely long rectangular region by four vertical truncation planes and establish the corresponding algorithm to approximate the EM fields in the entire space. The EM fields in this region are expanded as a two-dimensional (2-D) Fourier series of the transverse variables. By using the spectral state variable method, the generalized reflection coefficient matrices and transmission matrices are then derived to determine the Fourier coefficients per layer. Therefore, we can obtain the spatial-domain EM fields by summing the 2-D Fourier series. To enhance the accuracy and efficiency of this algorithm, we apply the method of images to estimate the influence of the artificial boundaries on the EM fields at the observer. We then further develop a quantitative principle to choose the proper size of the region according to the desired error tolerance. With the proper choice, the summation of the series can achieve satisfactory accuracy. This algorithm is finally applied to simulate the responses of the triaxial logging tool in transversely isotropic and biaxial anisotropic media and is verified through comparisons to the other method.
Real-Time Software for the Efficient Generation of the Clumping Index and Its Application Based on the Google Earth Engine
Authors: Li Yu , Fang Hongliang
Published: August 2022

Canopy clumping index (CI) is a key structural parameter related to vegetation phenology and the absorption of radiation, and it is usually retrieved from remote sensing data based on an empirical relationship with the Normalized Difference between Hotspot and Darkspot (NDHD) index. A rapid production software was developed to implement the CI algorithm based on the Google Earth Engine (GEE) to update current CI products and promote the application of CI in different fields. Daily, monthly, and yearly global CI products are continuously generated and updated in real-time by the software. Users can directly download the product or work with CI without paying attention to data generation. For the application case study, a change detection algorithm, LandTrendr, was implemented on the GEE to examine the global CI trend from 2000 to 2020. The results indicate that the area of increase trend (28.7%, ΔCI > 0.02) is greater than that of the decrease trend (17.1%, ΔCI < −0.02). Our work contributes toward the retrieval, application, and validation of CI.
A Deep Learning-Based Method for Extracting Standing Wood Feature Parameters from Terrestrial Laser Scanning Point Clouds of Artificially Planted Forest
Authors: Shen Xingyu , Huang Qingqing , Wang Xin , Li Jiang , Xi Benye
Published: August 2022

The use of 3D point cloud-based technology for quantifying standing wood and stand parameters can play a key role in forestry ecological benefit assessment and standing tree cultivation and utilization. With the advance of 3D information acquisition techniques, such as light detection and ranging (LiDAR) scanning, the stand information of trees in large areas and complex terrain can be obtained more efficiently. However, due to the diversity of the forest floor, the morphological diversity of the trees, and the fact that forestry is often planted as large-scale plantations, efficiently segmenting the point cloud of artificially planted forests and extracting standing wood feature parameters remains a considerable challenge. An effective method based on energy segmentation and PointCNN is proposed in this work to address this issue. The network is enhanced for learning point cloud features by geometric feature balance model (GFBM), enabling the efficient segmentation of tree point clouds from forestry point cloud data collected by terrestrial laser scanning (TLS) in outdoor environments. The 3D Forest software is then used to obtain single wood point cloud after semantic segmentation, and the extracted single wood point cloud is finally employed to extract standing wood feature parameters using TreeQSM. The point cloud semantic segmentation method is the most important part of our research. According to our findings, this method can segment datasets of two different artificially planted woodland point clouds with an overall accuracy of 0.95 and a tree segmentation accuracy of 0.93. When compared with the manual measurements, the root-mean-square error (RMSE) for tree height in the two datasets are 0.30272 and 0.21015 m, and the RMSEs for the diameter at breast height are 0.01436 and 0.01222 m, respectively. Our method is a robust framework based on deep learning that is applicable to forestry for extracting the feature parameters of artificially planted trees. It solves the problem of segmenting tree point clouds in artificially planted trees and provides a reliable data processing method for tree information extraction, trunk shape analysis, etc.
Single Camera Face Position-Invariant Driver’s Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks
Authors: Lollett Catherine , Kamezaki Mitsuhiro , Sugano Shigeki
Published: August 2022

Estimating the driver’s gaze in a natural real-world setting can be problematic for different challenging scenario conditions. For example, faces will undergo facial occlusions, illumination, or various face positions while driving. In this effort, we aim to reduce misclassifications in driving situations when the driver has different face distances regarding the camera. Three-dimensional Convolutional Neural Networks (CNN) models can make a spatio-temporal driver’s representation that extracts features encoded in multiple adjacent frames that can describe motions. This characteristic may help ease the deficiencies of a per-frame recognition system due to the lack of context information. For example, the front, navigator, right window, left window, back mirror, and speed meter are part of the known common areas to be checked by drivers. Based on this, we implement and evaluate a model that is able to detect the head direction toward these regions having various distances from the camera. In our evaluation, the 2D CNN model had a mean average recall of 74.96% across the three models, whereas the 3D CNN model had a mean average recall of 87.02%. This result show that our proposed 3D CNN-based approach outperforms a 2D CNN per-frame recognition approach in driving situations when the driver’s face has different distances from the camera.
A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts
Authors: Moon Chang Bae , Kim Byeong Man , Kim Dong-Seong
Published: August 2022

For products such as smartphones, the technology gap between companies is gradually narrowing with the advancements in technology. Therefore, product design can be a visible strategy for differentiation. However, it is difficult to apply automated production and defect detection processes to the various designs that are being developed. This study proposes a high-speed circular measurement method for correcting the alignment of round parts, which is difficult in an automated process. For analyzing the performance of the proposed method, its processing speed and accuracy are compared with those of the existing methods. The results of the analysis indicate that the overall performance of the proposed method is better than those of the existing methods.