bluuebunny/crossref_metadata_2025 huggingface dataset:
Sowerby’s Beaked Whales (Mesoplodon bidens) in the Skagerrak and Adjacent Waters: Historical Records and Recent Post-Mortem Findings
Authors: Stavenow Jasmine , Roos Anna Maria , Ågren Erik Olof , Kinze Carl , Englund William F. , Neimanis Aleksija
Published: June 2022

In contrast to sparse historical observational records, five Sowerby’s beaked whales (SBW) stranded and died in Swedish waters between 2015 and 2020. Here we summarize historical records of SBWs in the Skagerrak basin and adjacent waters. The three recent stranding events from Sweden are described, and the post-mortem findings, including diet analysis, from the five SBWs are presented. Of 30 historical records of SBWs observations since 1869, 13 (43%) were documented between 2010 and 2021, and records between July and November were the most frequent. The recent stranding events occurred in October 2015 (n = 1), August 2019 (n = 3) and July 2020 (n = 1). Four of the SBWs were examined through necropsy, and one was sampled in the field. They were all sub-adults and included a single female and four males. The causes of death were emaciation, euthanasia due to traumatic injury, and live stranding of undetermined cause. Two SBWs each had a focal bone lesion consistent with osteomyelitis. Other findings included pox-like dermatitis, trauma, focal granulomas in a lymph node and intestine, and ulceration of the stomach. CT scans were performed on the heads of two animals, with inconclusive results. Three SBWs had hard parts in the gastrointestinal tract that mainly consisted of otoliths from several fish species. An eDNA-analysis confirmed and supplemented the diet analysis, revealing 17 fish species in total, including species not previously described as prey for SBW, such as Pleuronectidae spp. The apparent increase in observational records since 2010 may indicate a shift in SBW distribution or changing threats to these animals. Our results support and expand theories on SBW movements and provide data on the biology and health of this poorly known species, which are valuable for conservation and legislation efforts.
MgAl and ZnAl-Hydrotalcites as Materials for Cosmetic and Pharmaceutical Formulations: Study of Their Cytotoxicity on Different Cell Lines
Authors: Ceccarini Maria Rachele , Puccetti Matteo , Pagano Cinzia , Nocchetti Morena , Beccari Tommaso , di Michele Alessandro , Ricci Maurizio , Perioli Luana
Published: June 2022

The knowledge about the effect of hydrotalcites (HTlcs), largely used in pharmaceutics, on non-malignant cell lines is limited. The effect of MgAl-HTlc-and ZnAl-HTlc- (NO3−/Cl−/CO32−) on the cell viability of HaCat, fibroblasts and HepG2 was studied by MTT assay. Cells were incubated either with HTlc suspensions in the culture media and with the supernatant obtained from the suspension being centrifuged. MgAl-HTlcs suspensions resulted in being cytotoxic. As SEM and TEM analyses showed the presence of sub-micrometric particles in all the MgAl-HTlc examined, it could be hypothesized that this fraction can be internalized into cells reducing the viability. MgAl-HTlc-NO3 is the most cytotoxic probably due to the additional effect of NO3− anions. ZnAl-HTlcs are cytotoxic, especially for HaCat and HepG2 cells (viability <60% at all the concentrations assayed). The effect is attributable both to the sub-micrometric fraction (identified by TEM) and to the high Zn2+ levels found in the culture medium by ICP-OES analysis, suggesting that ZnAl-HTlcs are less stable than MgAl-HTlc in the used media. The obtained results suggest that it is very important to perform ad hoc studies in order to evaluate HTlc safety before to be introduced in a formulation.
Beam Smoothing Based on Prism Pair for Multistep Pulse Compressor in PW Lasers
Authors: Du Shuman , Shen Xiong , Liang Wenhai , Wang Peng , Liu Jun
Published: June 2022

Ultra-short, ultra-intense lasers provide unprecedented experimental tools and extreme physical conditions, enabling the exploration of the frontiers of basic physics. Recently, a multistep pulse compressor (MPC) method was proposed to overcome the limitations of the size and the damage threshold of gratings in the compressor for the realization of a higher-peak-power laser. In the MPC method, beam smoothing is an important process in the pre-compressor. In this study, beam smoothing based on prism pairs is investigated, and the spatial profiles, as well as spectral dispersion properties, are analyzed. The simulation results demonstrate that the prism pair can effectively smooth the laser beam. Furthermore, beam smoothing is found to be more efficient with a shorter separation distance if two prism pairs are arranged to induce spatial dispersion in one or two directions. The beam smoothing results obtained in this study will help optimize optical designs in petawatt (PW) laser systems, thereby improving their output and operational safety.
OsMLP423 Is a Positive Regulator of Tolerance to Drought and Salt Stresses in Rice
Authors: Zhou Zhanmei , Fan Jiangbo , Zhang Jia , Yang Yanmei , Zhang Yifan , Zan Xiaofei , Li Xiaohong , Wan Jiale , Gao Xiaoling , Chen Rongjun , Huang Zhengjian , Xu Zhengjun , Li Lihua
Published: June 2022

Rice (Oryza sativa L.) is one of the main food crops for human survival, and its yield is often restricted by abiotic stresses. Drought and soil salinity are among the most damaging abiotic stresses affecting today’s agriculture. Given the importance of abscisic acid (ABA) in plant growth and abiotic stress responses, it is very important to identify new genes involved in ABA signal transduction. We screened a drought-inducing gene containing about 158 amino acid residues from the transcriptome library of rice exposed to drought treatment, and we found ABA-related cis-acting elements and multiple drought-stress-related cis-acting elements in its promoter sequence. The results of real-time PCR showed that OsMLP423 was strongly induced by drought and salt stresses. The physiological and biochemical phenotype analysis of transgenic plants confirmed that overexpression of OsMLP423 enhanced the tolerance to drought and salt stresses in rice. The expression of OsMLP423-GFP fusion protein indicated that OsMLP423 was located in both the cell membrane system and nucleus. Compared with the wild type, the overexpressed OsMLP423 showed enhanced sensitivity to ABA. Physiological analyses showed that the overexpression of OsMLP423 may regulate the water loss efficiency and ABA-responsive gene expression of rice plants under drought and salt stresses, and it reduces membrane damage and the accumulation of reactive oxygen species. These results indicate that OsMLP423 is a positive regulator of drought and salinity tolerance in rice, governing the tolerance of rice to abiotic stresses through an ABA-dependent pathway. Therefore, this study provides a new insight into the physiological and molecular mechanisms of OsMLP423-mediated ABA signal transduction participating in drought and salt stresses.
Co-Application of TiO2 Nanoparticles and Arbuscular Mycorrhizal Fungi Improves Essential Oil Quantity and Quality of Sage (Salvia officinalis L.) in Drought Stress Conditions
Authors: Ostadi Ali , Javanmard Abdollah , Amani Machiani Mostafa , Sadeghpour Amir , Maggi Filippo , Nouraein Mojtaba , Morshedloo Mohammad Reza , Hano Christophe , Lorenzo Jose M.
Published: June 2022

Drought stress is known as a major yield-limiting factor in crop production that threatens food security worldwide. Arbuscular mycorrhizal fungi (AMF) and titanium dioxide (TiO2) have shown to alleviate the effects of drought stress on plants, but information regarding their co-addition to minimize the effects of drought stress on plants is scant. Here, a two-year field experiment was conducted in 2019 and 2020 to evaluate the influence of different irrigation regimes and fertilizer sources on the EO quantity and quality of sage (Salvia officinalis L.). The experiment was laid out as a split plot arranged in a randomized complete block design with three replicates. The irrigation treatments were 25, 50, and 75% maximum allowable depletion (MAD) percentage of the soil available water as non-stress (MAD25), moderate (MAD50), and severe (MAD75) water stress, respectively. Subplots were four fertilizer sources including no-fertilizer control, TiO2 nanoparticles (100 mg L−1), AMF inoculation, and co-addition of TiO2 and AMF (TiO2 + AMF). Moderate and severe drought stress decreased sage dry matter yield (DMY) by 30 and 65%, respectively. In contrast, application of TiO2 + AMF increased DMY and water use efficiency (WUE) by 35 and 35%, respectively, compared to the unfertilized treatment. The highest EO content (1.483%), yield (2.52 g m−2), and cis-thujone (35.84%, main EO constituent of sage) was obtained in MAD50 fertilized with TiO2 + AMF. In addition, the net income index increased by 44, 47, and 76% with application of TiO2 nanoparticles, AMF, and co-addition of TiO2 + AMF, respectively. Overall, the integrative application of the biofertilizer and nanoparticles (TiO2 + AMF) can be recommended as a sustainable strategy for increasing net income and improving EO productivity and quality of sage plants in drought stress conditions. Future policy discussions should focus on incentivizing growers for replacing synthetic fertilizers with proven nano and biofertilizers to reduce environmental footprints and enhance the sustainability of sage production, especially in drought conditions.
Is Tempranillo Blanco Grapevine Different from Tempranillo Tinto Only in the Color of the Grapes? An Updated Review
Authors: Kizildeniz Tefide , Pascual Inmaculada , Hilbert Ghislaine , Irigoyen Juan José , Morales Fermín
Published: June 2022

Tempranillo Blanco is a somatic variant of Tempranillo Tinto that appeared as a natural, spontaneous mutation in 1988 in a single shoot of a single plant in an old vineyard. It was vegetatively propagated, and currently wines from Tempranillo Blanco are commercially available. The mutation that originated Tempranillo Blanco comprised single-nucleotide variations, chromosomal deletions, and reorganizations, losing hundreds of genes and putatively affecting the functioning and regulation of many others. The most evident, visual change in Tempranillo Blanco is the anthocyanin lost, producing this grapevine variety bunches of colorless grapes. This review aims to summarize from the available literature differences found between Tempranillo Blanco and Tinto in addition to the color of the grapes, in a climate change context and using fruit-bearing cuttings grown in temperature-gradient greenhouses as research-oriented greenhouses. The differences found include changes in growth, water use, bunch mass, grape quality (both technological and phenolic maturity), and some aspects of their photosynthetic response when grown in an atmosphere of elevated CO2 concentration and temperature, and low water availability. Under field conditions, Tempranillo Blanco yields less than Tempranillo Tinto, the lower weight of their bunches being related to a lower pollen viability and berry and seed setting.
Cross-Linked Luminescent Polymers Based on β-Diketone-Modified Polysiloxanes and Organoeuropiumsiloxanes
Authors: Kim Eleonora E. , Kononevich Yuriy N. , Dyuzhikova Yulia S. , Ionov Dmitry S. , Khanin Dmitry A. , Nikiforova Galina G. , Shchegolikhina Olga I. , Vasil’ev Viktor G. , Muzafarov Aziz M.
Published: June 2022

Nowadays, luminescent materials attract wide attention due to their valuable characteristics and broad area of potential application. Luminescent silicone-based polymers possess unique properties, such as flexibility, hydrophobicity, thermal and chemical stabilities, etc., which allow them to be utilized in various fields, such as optoelectronics, solid-state lasers, luminescent solar concentrators, sensors, and others. In the present work, a metal-ligand interaction approach was applied to obtain new cross-linked luminescent polymers based on multiligand polysiloxanes with grafted β-diketone fragments and organoeuropiumsiloxanes containing various organic substituents. Organoeuropiumsiloxanes were utilized as a source of Eu3+ ions due to their compatibility with the silicon matrix. All synthesized polymers were fully characterized and their physicochemical, mechanical, self-healing, optical, and thermal properties were studied.
Thermal Conductivity of Nanoporous Materials: Where Is the Limit?
Authors: Merillas Beatriz , Vareda João Pedro , Martín-de León Judith , Rodríguez-Pérez Miguel Ángel , Durães Luisa
Published: June 2022

Nowadays, our society is facing problems related to energy availability. Owing to the energy savings that insulators provide, the search for effective insulating materials is a focus of interest. Since the current insulators do not meet the increasingly strict requirements, developing materials with a greater insulating capacity is needed. Until now, several nanoporous materials have been considered as superinsulators achieving thermal conductivities below that of the air 26 mW/(m K), like nanocellular PMMA/TPU, silica aerogels, and polyurethane aerogels reaching 24.8, 10, and 12 mW/(m K), respectively. In the search for the minimum thermal conductivity, still undiscovered, the first step is understanding heat transfer in nanoporous materials. The main features leading to superinsulation are low density, nanopores, and solid interruptions hindering the phonon transfer. The second crucial condition is obtaining reliable thermal conductivity measurement techniques. This review summarizes these techniques, and data in the literature regarding the structure and thermal conductivity of two nanoporous materials, nanocellular polymers and aerogels. The key conclusion of this analysis specifies that only steady-state methods provide a reliable value for thermal conductivity of superinsulators. Finally, a theoretical discussion is performed providing a detailed background to further explore the lower limit of superinsulation to develop more efficient materials.
Investigation of the High-Temperature and Rheological Properties for Asphalt Sealant Modified by SBS and Rubber Crumb
Authors: Gong Yafeng , Wu Shuzheng , Zhang Yuwei , Pang Yunze , Ma Yulin
Published: June 2022

Crack sealing is an important measure for pavement maintenance. Hot-poured crack sealant is the most utilized material for crack sealing. However, its poor high-temperature and rheological properties seriously weaken the mechanical properties of repaired pavement. Thus, to overcome the disadvantage of the poor high-temperature and rheological properties of sealant, styrene–butadiene–styrene (SBS) and rubber crumb (CR) were utilized for modifying the asphalt-based sealants. Softening point tests, temperature tests, frequency scan tests, and multiple stress creep recovery tests (MSCR) were conducted to evaluate the high-temperature and rheological properties of the modified sealant. Additionally, the influence of SBS and CR on the high-temperature performance of the modified sealant was quantitatively analyzed by the grey relational analysis method. The results reveal that the SBS has a greater enhancement effect on the high-temperature performance of sealant than CR. Increasing the SBS and CR content in the sealant could enhance the sealant’s high-temperature performance, stiffness, and elasticity. Compared with asphalt-based sealant and one-component modified asphalt-based sealant, SBS/CR-modified asphalt sealant has greater viscosity and higher temperature deformation resistance. Additionally, SBS can increase the stress level of the sealant, thereby enhancing the resistance of the sealant to permanent deformation.
Current Trends in Biomedical Hydrogels: From Traditional Crosslinking to Plasma-Assisted Synthesis
Authors: Taaca Kathrina Lois M. , Prieto Eloise I. , Vasquez Magdaleno R.
Published: June 2022

The use of materials to restore or replace the functions of damaged body parts has been proven historically. Any material can be considered as a biomaterial as long as it performs its biological function and does not cause adverse effects to the host. With the increasing demands for biofunctionality, biomaterials nowadays may not only encompass inertness but also specialized utility towards the target biological application. A hydrogel is a biomaterial with a 3D network made of hydrophilic polymers. It is regarded as one of the earliest biomaterials developed for human use. The preparation of hydrogel is often attributed to the polymerization of monomers or crosslinking of hydrophilic polymers to achieve the desired ability to hold large amounts of aqueous solvents and biological fluids. The generation of hydrogels, however, is shifting towards developing hydrogels through the aid of enabling technologies. This review provides the evolution of hydrogels and the different approaches considered for hydrogel preparation. Further, this review presents the plasma process as an enabling technology for tailoring hydrogel properties. The mechanism of plasma-assisted treatment during hydrogel synthesis and the current use of the plasma-treated hydrogels are also discussed.
Development of 3D Thermoplastic Polyurethane (TPU)/Maghemite (ϒ-Fe2O3) Using Ultra-Hard and Tough (UHT) Bio-Resin for Soft Tissue Engineering
Authors: Fallahiarezoudar Ehsan , Ngadiman Nor Hasrul Akhmal , Yusof Noordin Mohd , Idris Ani , Ishak Mohamad Shaiful Ashrul
Published: June 2022

The use of soft tissue engineering scaffolds is an advanced approach to repairing damaged soft tissue. To ensure the success of this technique, proper mechanical and biocompatibility properties must be taken into consideration. In this study, a three-dimensional (3D) scaffold was developed using digital light processing (DLP) and ultra-hard and tough (UHT) bio-resin. The 3D scaffold structure consisted of thermoplastic polyurethane (TPU) and maghemite (ϒ-Fe2O3) nanoparticles mixed with UHT bio-resin. The solution sample for fabricating the scaffolds was varied with the concentration of the TPU (10, 12.5, and 15% wt/v) and the amount of ϒ-Fe2O3 (1, 3, and 5% v/v) added to 15% wt/v of TPU. Before developing the real geometry of the sample, a pre-run of the DLP 3D printing process was done to determine the optimum curing time of the structure to be perfectly cured, which resulted in 30 s of curing time. Then, this study proceeded with a tensile test to determine the mechanical properties of the developed structure in terms of elasticity. It was found that the highest Young’s Modulus of the scaffold was obtained with 15% wt/v TPU/UHT with 1% ϒ-Fe2O3. Furthermore, for the biocompatibility study, the degradation rate of the scaffold containing TPU/UHT was found to be higher compared to the TPU/UHT containing ϒ-Fe2O3 particles. However, the MTT assay results revealed that the existence of ϒ-Fe2O3 in the scaffold improved the proliferation rate of the cells.
Cadmium in Rice Is Affected by Fertilizer-Borne Chloride and Sulfate Anions: Long-Term Field Versus Pot Experiments
Authors: Hussain Babar , Ma Yibing , Li Jumei , Gao Jusheng , Ullah Aman , Tahir Nazia
Published: June 2022

In order to investigate the effects of Cl− and SO42− based fertilizers on the accumulation of cadmium (Cd) in rice plants, a long-term experiment, which has been conducted since 1975, and a short-term pot experiment were designed. The results of the long-term experiment showed that the highest total grain Cd was found in the treatment of fertilizers with rich Cl−, which was 72.7% higher compared to conventional fertilization (CF). However, there was no significant difference between the CF and fertilization with rich SO42− treatments. This phenomenon can be explained by the concentrations of the EDTA extractable Cd being significantly increased by 60% under Cl− treatment, while SO42− treatment showed no significant effect. In the short-term trial, compared to CF, Cd concentrations in the roots increased by 1.07 and 0.93 times in the Cl− and SO42− treated soils, respectively, under Cd1.2 exposure. Meanwhile, Cd concentrations in the shoots enhanced by 96% in Cl− treated soil but decreased by 34.6% in SO42− treated soil. It was therefore concluded that fertilizer-borne Cl− significantly increased the Cd concentration in rice grains in the long-term experiment, but fertilizer-borne SO42− had no significant effect on the Cd concentration in rice grains. However, in the pot experiment, SO42− based fertilizers decreased Cd transport to the shoots of a rice plant grown in a Cd contaminated soil. These findings will improve the rational fertilization of Cd contaminated soils and the production of safer rice.
Extraction of Floating Raft Aquaculture Areas from Sentinel-1 SAR Images by a Dense Residual U-Net Model with Pre-Trained Resnet34 as the Encoder
Authors: Gao Long , Wang Chengyi , Liu Kai , Chen Shaohui , Dong Guannan , Su Hongbo
Published: June 2022

Marine floating raft aquaculture (FRA) monitoring is significant for marine ecological environment and food security assessment. Synthetic aperture radar-based monitoring is considered to be an effective means of FRA identification because of its capability for all-weather applications. Considering the poor generalization and extraction accuracy of traditional monitoring methods, a semantic segmentation model called D-ResUnet is proposed to extract FRA areas from Sentinel-1 images. The proposed model has a U-Net-like structure but combines the pre-trained ResNet34 as the encoder and adds dense residual units into the decoder. For this model, the final layer and cropping operation of the original U-Net model are removed to eliminate the model parameters. The mean and standard deviation of Precision, Recall, Intersection over Union (IoU), and F1 score are calculated under a five-fold training strategy to evaluate the model accuracy. The test experiments indicated that the proposed model performs well with the F1 of 92.6% and IoU of 86.24% in FRA extraction tasks. In particular, the ablation experiments and application experiments proved the effectiveness of the improvement strategy and the portability of the proposed D-ResUnet model, respectively. Compared with the other three state-of-the-art semantic segmentation models, the experiments demonstrate a clear accuracy advantage of the D-ResUnet model. For the FRA extraction task, this paper presents a promising approach that has refined extraction capability, high accuracy, and acceptable model complexity.
Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains
Authors: Wei Shiqi , Xu Tianfang , Niu Guo-Yue , Zeng Ruijie
Published: June 2022

Groundwater-based irrigation has dramatically expanded over the past decades. It has important implications for terrestrial water, energy fluxes, and food production, as well as local to regional climates. However, irrigation water use is hard to monitor at large scales due to various constraints, including the high cost of metering equipment installation and maintenance, privacy issues, and the presence of illegal or unregistered wells. This study estimates irrigation water amounts using machine learning to integrate in situ pumping records, remote sensing products, and climate data in the Kansas High Plains. We use a random forest regression to estimate the annual irrigation water amount at a reprojected spatial resolution of 6 km based on various data, including remotely sensed vegetation indices and evapotranspiration (ET), land cover, near-surface meteorological forcing, and a satellite-derived irrigation map. In addition, we assess the value of ECOSTRESS ET products for irrigation water use estimation and compare with the baseline results by using MODIS ET. The random forest regression model can capture the temporal and spatial variability of irrigation amounts with a satisfactory accuracy (R2 = 0.82). It performs reasonably well when it is calibrated on the western portion of the study area and tested on the eastern portion that receives more rain than the western one, suggesting its potential transferability to other regions. ECSOTRESS ET and MODIS ET yield a similar irrigation estimation accuracy.
Using InSAR and PolSAR to Assess Ground Displacement and Building Damage after a Seismic Event: Case Study of the 2021 Baicheng Earthquake
Authors: Sun Xiaolin , Chen Xi , Yang Liao , Wang Weisheng , Zhou Xixuan , Wang Lili , Yao Yuan
Published: June 2022

During unexpected earthquake catastrophes, timely identification of damaged areas is critical for disaster management. On the 24 March 2021, Baicheng county was afflicted by a Mw 5.3 earthquake. The disaster resulted in three deaths and many human injuries. As an active remote sensing technology independent of light and weather, the increasingly accessible Synthetic Aperture Radar (SAR) is an attractive data for assessing building damage. This paper aims to use Sentinel-1A radar images to rapidly assess seismic damage in the early phases after the disaster. A simple and robust method is used to complete the task of surface displacement analysis and building disaster monitoring. In order to obtain the coseismic deformation field, differential interferometry, filtering and phase unwrapping are performed on images before and after the earthquake. In order to detect the damage area of buildings, the Interferometric Synthetic Aperture Radar (InSAR) and Polarimetric Synthetic Aperture Radar (PolSAR) techniques are used. A simple and fast method combining coherent change detection and polarimetric decomposition is proposed, and the complete workflow is introduced in detail. In our experiment, we compare the detection results with the ground survey data using an unmanned aerial vehicle (UAV) after the earthquake to verify the performance of the proposed method. The results indicate that the experiment can accurately obtain the coseismic deformation field and identify the damaged and undamaged areas of the buildings. The correct identification accuracy of collapsed and severely damaged areas is 86%, and that of slightly damaged and undamaged areas is 84%. Therefore, the proposed method is extremely effective in monitoring seismic-affected areas and immediately assessing post-earthquake building damage. It provides a considerable prospect for the application of SAR technology.
A Robust Strategy for Large-Size Optical and SAR Image Registration
Authors: Li Zeyi , Zhang Haitao , Huang Yihang , Li Haifeng
Published: June 2022

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images.
Genus-Level Mapping of Invasive Floating Aquatic Vegetation Using Sentinel-2 Satellite Remote Sensing
Authors: Ade Christiana , Khanna Shruti , Lay Mui , Ustin Susan L. , Hestir Erin L.
Published: June 2022

Invasive floating aquatic vegetation negatively impacts wetland ecosystems and mapping this vegetation through space and time can aid in designing and assessing effective control strategies. Current remote sensing methods for mapping floating aquatic vegetation at the genus level relies on airborne imaging spectroscopy, resulting in temporal gaps because routine hyperspectral satellite coverage is not yet available. Here we achieved genus level and species level discrimination between two invasive aquatic vegetation species using Sentinel 2 multispectral satellite data and machine-learning classifiers in summer and fall. The species of concern were water hyacinth (Eichornia crassipes) and water primrose (Ludwigia spp.). Our classifiers also identified submerged and emergent aquatic vegetation at the community level. Random forest models using Sentinel-2 data achieved an average overall accuracy of 90%, and class accuracies of 79–91% and 85–95% for water hyacinth and water primrose, respectively. To our knowledge, this is the first study that has mapped water primrose to the genus level using satellite remote sensing. Sentinel-2 derived maps compared well to those derived from airborne imaging spectroscopy and we also identified misclassifications that can be attributed to the coarser Sentinel-2 spectral and spatial resolutions. Our results demonstrate that the intra-annual temporal gaps between airborne imaging spectroscopy observations can be supplemented with Sentinel-2 satellite data and thus, rapidly growing/expanding vegetation can be tracked in real time. Such improvements have potential management benefits by improving the understanding of the phenology, spread, competitive advantages, and vulnerabilities of these aquatic plants.
Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning
Authors: Lin Xiwen , Ma Jinji , Chen Hao , Shen Fei , Ahmad Safura , Li Zhengqiang
Published: June 2022

Carbon emissions caused by the massive consumption of energy have brought enormous pressure on the Chinese government. Accurately and rapidly characterizing the spatiotemporal characteristics of Chinese city-level carbon emissions is crucial for policy decision making. Based on multi-dimensional data, including nighttime light (NTL) data, land use (LU) data, land surface temperature (LST) data, and added-value secondary industry (AVSI) data, a deep neural network ensemble (DNNE) model was built to analyze the nonlinear relationship between multi-dimensional data and province-level carbon emission statistics (CES) data. The city-level carbon emissions data were estimated, and the spatiotemporal characteristics were analyzed. As compared to the energy statistics released by partial cities, the results showed that the DNNE model based on multi-dimensional data could well estimate city-level carbon emissions data. In addition, according to a linear trend analysis and standard deviational ellipse (SDE) analysis of China from 2001 to 2019, we concluded that the spatiotemporal changes in carbon emissions at the city level were in accordance with the development of China’s economy. Furthermore, the results can provide a useful reference for the scientific formulation, implementation, and evaluation of carbon emissions reduction policies.
Unified Land–Ocean Quasi-Geoid Computation from Heterogeneous Data Sets Based on Radial Basis Functions
Authors: Liu Yusheng , Lou Lizhi
Published: June 2022

The determination of the land geoid and the marine geoid involves different data sets and calculation strategies. It is a hot issue at present to construct the unified land–ocean quasi-geoid by fusing multi-source data in coastal areas, which is of great significance to the construction of land–ocean integration. Classical geoid integral algorithms such as the Stokes theory find it difficult to deal with heterogeneous gravity signals, so scholars have gradually begun using radial basis functions (RBFs) to fuse multi-source data. This article designs a multi-layer RBF network to construct the unified land–ocean quasi-geoid fusing measured terrestrial, shipborne, satellite altimetry and airborne gravity data based on the Remove–Compute–Restore (RCR) technique. EIGEN-6C4 of degree 2190 is used as a reference gravity field. Several core problems in the process of RBF modeling are studied in depth: (1) the behavior of RBFs in the spatial domain; (2) the locations of RBFs; (3) ill-conditioned problems of the design matrix; (4) the effect of terrain masses. The local quasi-geoid with a 1′ resolution is calculated, respectively, on the flat east coast and the rugged west coast of the United States. The results show that the accuracy of the quasi-geoid computed by fusing four types of gravity data in the east coast experimental area is 1.9 cm inland and 1.3 cm on coast after internal verification (the standard deviation of the quasi-geoid w.r.t GPS/leveling data). The accuracy of the quasi-geoid calculated in the west coast experimental area is 2.2 cm inland and 2.1 cm on coast. The results indicate that using RBFs to calculate the unified land–ocean quasi-geoid from heterogeneous data sets has important application value.
Rockfall Hazard Assessment in the Taihang Grand Canyon Scenic Area Integrating Regional-Scale Identification of Potential Rockfall Sources
Authors: Zhan Jiewei , Yu Zhaoyue , Lv Yan , Peng Jianbing , Song Shengyuan , Yao Zhaowei
Published: June 2022

Frequent rockfall events pose a major threat to the safe operation of the Taihang Grand Canyon Scenic Area (GCSA) in China. The traditional techniques for identifying potential rockfall sources and hazard assessment methods are often challenged in the alpine canyon landform. This study aims to establish an early identification framework for regional potential rockfall sources applicable to the canyon region and to assess rockfall hazards in potentially hazardous areas using unmanned aerial vehicle (UAV) photogrammetry. Specifically, by incorporating high-precision topographic information and geotechnical properties, the slope angle distribution method was used for static identification of potential rockfall sources. Moreover, SBAS-InSAR technology was used to describe the activity of potential rockfall sources. Finally, taking the key potentially hazardous area of the Sky City scenic spot as an example, the Rockfall Analyst tool was used to analyze the rockfall frequency, bounce height and energy characteristics based on the high-precision UAV 3D real scene model, and the analytic hierarchy process was introduced to achieve quantitative rockfall hazard assessment. The results show that the potential rockfall source areas in the Taihang GCSA is 33.47 km2 (21.47%), mainly distributed in strips on the cliffs on both sides of the canyon, of which the active rockfall source area is 2.96 km2 (8.84%). Taking the scenic spot of Sky City as example, the proposed UAV-based real scene modeling technology was proven to be able to quickly and accurately construct a 3D high-precision model of the canyon area. Moreover, the 3D rockfall simulation showed that the high-energy rockfall area was mainly distributed at the foot of the steep cliff, which mainly threatens the tourist distribution center below. The early identification and quantitative evaluation scheme of rockfall events proposed in this study can provide technical reference for the prevention and control of rockfall hazards in similar alpine valley areas.
Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing
Authors: Zhang Fanyi , Tian Xin , Zhang Haibo , Jiang Mi
Published: June 2022

Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.
An Advanced Framework for Multi-Scale Forest Structural Parameter Estimations Based on UAS-LiDAR and Sentinel-2 Satellite Imagery in Forest Plantations of Northern China
Authors: Wu Xiangqian , Shen Xin , Zhang Zhengnan , Cao Fuliang , She Guanghui , Cao Lin
Published: June 2022

Regarded as a marked category of global forests, forest plantations not only have great significance for the development of the global economy, but also contribute ecological and social benefits. The accurate acquisition of the multi-scale (from individual tree to landscape level) and near-real-time information of structural parameters in plantations is the premise of decision-making in sustainable management for the whole forest farm, and it is also the basis for the evaluation of forest productivity in stands. The development and synergetic applications of multi-source and multi-platform remote sensing technology provide a technical basis for the highly accurate estimation of multi-scale forest structural parameters. In this study, we developed an advanced framework for estimating these parameters of forest plantations in multiple scales (individual tree, plot and landscape levels) based on the Unmanned Aircraft System Light Detection and Ranging (UAS-LiDAR) transects and wall-to-wall Sentinel-2 imagery, combined with the sample plot data in a typical forest farm plantation (mainly Larch, Chinese pine) of Northern China. The position and height of individual trees within the plots were extracted by the LiDAR-based point cloud segmentation (PCS) algorithm, and then different approaches to the extrapolation of forest structural parameters from the plot to landscape level were assessed. The results demonstrate that, firstly, the individual tree height obtained by PCS was of relatively high accuracy (rRMSE = 1.5–3.3%); secondly, the accuracy of the forest structure parameters of the sample plot scale estimated by UAS-LiDAR is rRMSE = 4.4–10.6%; and thirdly, the accuracy of the two-stage upscaling approach by UAS-LiDAR transects as an intermediate stage (rRMSE = 14.5–20.2%) performed better than the direct usage of Sentinel-2 data (rRMSE = 22.9–27.3%). This study demonstrated an advanced framework for creating datasets of multi-scale forest structural parameters in a forest plantation, and proved that the synergetic usage of UAS-LiDAR transects and full coverage medium-resolution satellite imagery can provide a high-precision and low-cost technical basis for the multi-level estimation of forest structural parameters.
Sea Ice–Water Classification of RADARSAT-2 Imagery Based on Residual Neural Networks (ResNet) with Regional Pooling
Authors: Jiang Mingzhe , Xu Linlin , Clausi David A.
Published: June 2022

Sea ice mapping plays an integral role in ship navigation and meteorological modeling in the polar regions. Numerous published studies in sea ice classification using synthetic aperture radar (SAR) have reported high classification rates. However, many of these focus on numerical results based on sample points and ignore the quality of the inferred sea ice maps. We have designed and implemented a novel SAR sea ice classification algorithm where the spatial context, obtained by the unsupervised IRGS segmentation algorithm, is integrated with texture features extracted by a residual neural network (ResNet) and, using regional pooling, classifies ice and water. This algorithm is trained and tested on a published dataset and cross-validated using leave-one-out (LOO) strategy, obtaining an overall accuracy of 99.67% and outperforming several existing algorithms. In addition, visual results show that this new method produces sea ice maps with natural ice–water boundaries and fewer ice and water errors.
An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
Authors: Huang Pingping , Hao Lingxia , Tan Weixian , Xu Wei , Qi Yaolong
Published: June 2022

Arc array synthetic aperture radar (AA-SAR), which can observe the scene in all directions, breaks through the single view of traditional SAR. However, the concealment of AA-SAR is poor. To mitigate this, arc array bistatic SAR (AA-BiSAR) with the moving transmitter is proposed, it has the advantages of good concealment and can expand the imaging scene, and improve the flexibility of the system. The imaging geometry including the signal model is established, and a range frequency-domain algorithm based on keystone transform (KT) is proposed in this paper. In the first step, the slant range equation is approximated by Taylor series expansion to compensate for the residual phase caused by the transmitter motion. In the second step, the range cell migration between the range and azimuth is eliminated through the KT method in the range frequency-domain. In the third step, using the data after range cell migration correction in step 2, an azimuth pulse compression is performed to obtain the focused image. In addition, the spatial resolution of the AA-BiSAR system is analyzed in detail. Finally, three simulation results verify the effectiveness of the proposed algorithm and the change in the spatial resolution.
Deep Learning-Based Vehicle Classification for Low Quality Images
Authors: Tas Sumeyra , Sari Ozgen , Dalveren Yaser , Pazar Senol , Kara Ali , Derawi Mohammad
Published: June 2022

This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 × 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.
Robust Nonlinear Tracking Control with Exponential Convergence Using Contraction Metrics and Disturbance Estimation
Authors: Zhao Pan , Guo Ziyao , Hovakimyan Naira
Published: June 2022

This paper presents a tracking controller for nonlinear systems with matched uncertainties based on contraction metrics and disturbance estimation that provides exponential convergence guarantees. Within the proposed approach, a disturbance estimator is proposed to estimate the pointwise value of the uncertainties, with a pre-computable estimation error bounds (EEB). The estimated disturbance and the EEB are then incorporated in a robust Riemannian energy condition to compute the control law that guarantees exponential convergence of actual state trajectories to desired ones. Simulation results on aircraft and planar quadrotor systems demonstrate the efficacy of the proposed controller, which yields better tracking performance than existing controllers for both systems.
Research on Chinese Speech Emotion Recognition Based on Deep Neural Network and Acoustic Features
Authors: Lee Ming-Che , Yeh Sheng-Cheng , Chang Jia-Wei , Chen Zhen-Yi
Published: June 2022

In recent years, the use of Artificial Intelligence for emotion recognition has attracted much attention. The industrial applicability of emotion recognition is quite comprehensive and has good development potential. This research uses voice emotion recognition technology to apply it to Chinese speech emotion recognition. The main purpose of this research is to transform gradually popularized smart home voice assistants or AI system service robots from a touch-sensitive interface to a voice operation. This research proposed a specifically designed Deep Neural Network (DNN) model to develop a Chinese speech emotion recognition system. In this research, 29 acoustic characteristics in acoustic theory are used as the training attributes of the proposed model. This research also proposes a variety of audio adjustment methods to amplify datasets and enhance training accuracy, including waveform adjustment, pitch adjustment, and pre-emphasize. This study achieved an average emotion recognition accuracy of 88.9% in the CASIA Chinese sentiment corpus. The results show that the deep learning model and audio adjustment method proposed in this study can effectively identify the emotions of Chinese short sentences and can be applied to Chinese voice assistants or integrated with other dialogue applications.
Increasing the Generalization of Supervised Fabric Anomaly Detection Methods to Unseen Fabrics
Authors: Rippel Oliver , Zwinge Corinna , Merhof Dorit
Published: June 2022

Fabric anomaly detection (AD) tries to detect anomalies (i.e., defects) in fabrics, and fabric AD approaches are continuously improved with respect to their AD performance. However, developed solutions are known to generalize poorly to previously unseen fabrics, posing a crucial limitation to their applicability. Moreover, current research focuses on adapting converged models to previously unseen fabrics in a post hoc manner, rather than training models that generalize better in the first place. In our work, we explore this potential for the first time. Specifically, we propose that previously unseen fabrics can be regarded as shifts in the underlying data distribution. We therefore argue that factors which reportedly improve a model’s resistance to distribution shifts should also improve the performance of supervised fabric AD methods on unseen fabrics. Hence, we assess the potential benefits of: (I) vicinal risk minimization (VRM) techniques adapted to the fabric AD use-case, (II) different loss functions, (III) ImageNet pre-training, (IV) dataset diversity, and (V) model architecture as well as model complexity. The subsequently performed large-scale analysis reveals that (I) only the VRM technique, AugMix, consistently improves performance on unseen fabrics; (II) hypersphere classifier outperforms other loss functions when combined with AugMix and (III) ImageNet pre-training, which is already beneficial on its own; (IV) increasing dataset diversity improves performance on unseen fabrics; and (V) architectures with better ImageNet performance also perform better on unseen fabrics, yet the same does not hold for more complex models. Notably, the results show that not all factors and techniques which reportedly improve a model’s resistance to distribution shifts in natural images also improve the generalization of supervised fabric AD methods to unseen fabrics, demonstrating the necessity of our work. Additionally, we also assess whether the performance gains of models which generalize better propagate to post hoc adaptation methods and show this to be the case. Since no suitable fabric dataset was publicly available at the time of this work, we acquired our own fabric dataset, called OLP, as the basis for the above experiments. OLP consists of 38 complex, patterned fabrics, more than 6400 images in total, and is made publicly available.