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The cement industry, as one of the primary contributors to global greenhouse gas emissions, accounts for 7% of the world's carbon dioxide emissions. There is an urgent need to establish a rapid method for detecting cement plants to facilitate effective monitoring. In this study, a comprehensive method based on YOLOv5-IEG and the Thermal Signature Detection module using Google Earth optical imagery and SDGSAT-1 thermal infrared imagery was proposed to detect large-scale cement plant information, including geographic location and operational status. The improved algorithm demonstrated an increase of 4.8% in accuracy and a 7.7% improvement in MAP@.5:95. In a specific empirical investigation in China, we successfully detected 781 large-scale cement plants with an accuracy of 90.8%. Specifically, of the 55 cement plants in Shandong Province, we identified 46 as operational and nine as non-operational. The successful application of advanced models and remote sensing technology in efficiently and accurately tracking the operational status of cement plants provides crucial support for environmental protection and sustainable development. [ABSTRACT FROM AUTHOR]

(1) Background: DNA damage in cumulus cells hinders oocyte maturation and affects steroid hormone secretion. It is crucial to identify the key factors that regulate cellular DNA damage and steroid hormone secretion. (2) Methods: Treatment of bovine cumulus cells with bleomycin to induce DNA damage. The effects of DNA damage on cell biology were determined by detecting changes in DNA damage degree, cell cycle, viability, apoptosis, and steroid hormones. It was verified that mir-302d targeted regulation of CDKN1A expression, and then affected DNA damage and steroid hormone secretion in cumulus cells. (3) Results: Bleomycin induced increased DNA damage, decreased G1-phase cells, increased S-phase cells, inhibited proliferation, promoted apoptosis, affected E2 and P4 secretion, increased CDKN1A expression, and decreased miR-302d expression. Knockdown of CDKN1A reduced DNA damage, increased G1-phase cells, decreased G2-phase cells, promoted proliferation, inhibited apoptosis, increased E2 and P4 secretion, and increased the expression of BRCA1, MRE11, ATM, CDK1, CDK2, CCNE2, STAR, CYP11A1, and HSD3B1. The expression of RAD51, CCND1, p53, and FAS was decreased. Overexpression of CDKN1A resulted in the opposite results. miR-302d targets CDKN1A expression to regulate DNA damage and then affects the cell cycle, proliferation, apoptosis, steroid hormone secretion, and the expression of related genes. (4) Conclusions: miR-302d and CDKN1A were candidate molecular markers for the diagnosis of DNA damage in bovine cumulus cells. [ABSTRACT FROM AUTHOR]

Soil water shortage has become a severe issue in ecological restoration and sustainable development in the Loess Plateau, facing the challenges of climate change and vegetation restoration. This study monitored the soil water content in surface soil (0–40 cm) with different sub-shrub component treatments, including the natural condition (NC), the canopy plus the roots (CR) and only the roots (OR), to analyze the change in soil water storage (∆W) and its response to precipitation (P) and air temperature (Ta) on a daily scale. P was the main factor controlling the daily ∆W, contributing 49–52% to the variation in the daily ∆W, and Ta only explained 6–21% of the variation. Minimum P amounts of 0.74–1.12 mm and maximum Ta of 29.09–32.00 °C were the thresholds required to increase soil water storage (W). Sub-shrub components showed significant influences on soil water conservation. We found that the ∆W hierarchy for each sub-shrub treatment was NC (1.73 mm) > CR (0.71 mm) > OR (0.56 mm) on rainy days and NC (−0.53 mm) < CR (−0.36 mm) < OR (−0.06 mm) on no-rain days. Additionally, the hierarchy of the rainwater retention rate was NC (26.43%) > OR (13.71%) > CR (4.58%). Thus, a canopy could increase infiltration and hugely consume soil water at the same time, while litter could weaken or offset the canopy's effects and the roots promote infiltration with little evaporation loss. [ABSTRACT FROM AUTHOR]

Biomolecular channels on the cell membrane are essential for transporting substances across the membrane to maintain cell physiological activity. Artificial transmembrane channels used to mimic biological membrane channels can regulate intra/extracellular ionic and molecular homeostasis, and they elucidate cellular structures and functionalities. Due to their program design, facile preparation, and high biocompatibility, DNA nanostructures have been widely used as scaffolds for the design of artificial transmembrane channels and exploited for ionic and molecular transport and biomedical applications. DNA-based artificial channels can be designed from two structural modules: DNA nanotubes/nanopores as transport modules for mass transportation and hydrophobic segments as anchor modules for membrane immobilization. In this review, various lipophilic modification strategies for the design of DNA channels and membrane insertion are outlined. Several types of DNA transmembrane channels are systematically summarized, including DNA wireframe channels, DNA helix bundle channels, DNA tile channels, DNA origami channels, and so on. We then discuss efforts to exploit them in biosensor and biomedical applications. For example, ligand-gated and environmental stimuli-responsive artificial transmembrane channels have been designed for transmembrane signal transduction. DNA-based artificial channels have been developed for cell mimicry and the regulation of cell behaviors. Finally, we provide some perspectives on the challenges and future developments of artificial transmembrane channel research in biomimetic science and biomedical applications. [ABSTRACT FROM AUTHOR]

In the field of SAR-to-optical image synthesis, current methods based on conditional generative adversarial networks (CGANs) have satisfying performance under simple scenarios, but the performance drops severely under complicated scenarios. Considering that SAR images can form a robust time series due to SAR's all-weather imaging ability, we take advantage of this and extract a temporal correlation from bi-temporal SAR images to guide the translation. To achieve this, we introduce a co-attention mechanism into the CGAN that learns the correlation between optically-available and optically-absent time points, selectively enhances the features of the former time point, and eventually guides the model to a better optical image synthesis on the latter time point. Additionally, we adopt a strategy to balance the weight of optical and SAR features to extract better features from the SAR input. With these strategies, the quality of synthesized images is notably improved in complicated scenarios. The synthesized images can increase the spatial and temporal resolution of optical imagery, greatly improving the availability of data for the applications of crop monitoring, change detection, and visual interpretation. [ABSTRACT FROM AUTHOR]

Patulin (PAT), a type of mycotoxin existing in foodstuffs, is harmful to food safety and human health. Thus, it is necessary to develop sensitive, selective and reliable analytical methods for PAT detection. In this study, a sensitive aptasensor based on a dual-signaling strategy was fabricated, in which a methylene-blue-labeled aptamer and ferrocene monocarboxylic acid in the electrolyte acted as a dual signal, for monitoring PAT. To improve the sensitivity of the aptasensor, an in-plane gold nanoparticles–black phosphorus heterostructure (AuNPs-BPNS) was synthesized for signal amplification. Due to the combination of AuNPs-BPNS nanocomposites and the dual-signaling strategy, the proposed aptasensor has a good analytical performance for PAT detection with the broad linear range of 0.1 nM–100.0 μM and the low detection limit of 0.043 nM. Moreover, the aptasensor was successfully employed for real sample detection, such as apple, pear and tomato. It is expected that BPNS-based nanomaterials hold great promise for developing novel aptasensors and may provide a sensing platform for food safety monitoring. [ABSTRACT FROM AUTHOR]

Soil remediation agents (SRAs) such as biochar and hydroxyapatite (HAP) have shown a promising prospect in in situ soil remediation programs and safe crop production. However, the effects of SRAs on soil microbial communities still remain unclear, particularly under field conditions. Here, a field case study was conducted to compare the effects of biochar and HAP on soil bacterial communities in a slightly Cd-contaminated farmland grown with sweet sorghum of different planting densities. We found that both biochar and HAP decreased the diversity and richness of soil bacteria, but they differently altered bacterial community structure. Biochar decreased Chao1 (−7.3%), Observed_species (−8.6%), and Shannon indexes (−1.3%), and HAP caused Shannon (−2.0%) and Simpson indexes (−0.1%) to decline. The relative abundance (RA) of some specific taxa and marker species was differently changed by biochar and HAP. Overall, sweet sorghum cultivation did not significantly alter soil bacterial diversity and richness but caused changes in the RA of some taxa. Some significant correlations were observed between soil properties and bacterial abundance. In conclusion, soil remediation with biochar and HAP caused alterations in soil bacterial communities. Our findings help to understand the ecological impacts of SRAs in soil remediation programs. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, a case of infection with RVC was identified and the full-length genome of RVC in China was sequenced. A natural recombination event was observed in the RVC genome. Collectively, our data suggest that the RVC exists in China and it can be mixed with other diarrhea viruses, which brings new challenges to the prevention and control of diarrhea disease in pigs. Our data provide a new insight into the evolution of the rotavirus. Swine enteric disease is the predominant cause of morbidity and mortality, and viral species involved in swine enteric disease include rotaviruses and coronaviruses, among others. Awareness of the circulating porcine rotavirus group C (PoRVC) in pig herds is critical to evaluate the potential impact of infection. At present, due to the lack of disease awareness and molecular diagnostic means, the research on RVC infection in China is not well-studied. In this study, diarrhea samples collected from pig farms were detected positive for RVC by PCR, and the full-length RVC was not previously reported for Chinese pig farms. This rotavirus strain was designated as RVC/Pig/CHN/JS02/2018/G6P6. A natural recombination event was observed with breakpoints at nucleotides (nt) 2509 to 2748 of the VP2 gene. Phylogenetic analysis based on nsp1 revealed that a new branch A10 formed. Collectively, our data suggest a potentially novel gene recombination event of RVC in the VP2 gene. These findings provide a new insight into the evolution of the rotavirus. [ABSTRACT FROM AUTHOR]

Laser cladding Ni60/2.0 wt.%CeO2 self-lubricating anti-wear composite coatings on 35CrMoV steel were thermally treated at 25 °C, 500 °C, 600 °C and 700 °C for 1 h, respectively. The macroscopic morphology of composite coatings was characterized by optical microscopy. The evolution of microstructure was studied by scanning electron microscopy. The precipitated phase of the coating was analyzed by X-ray diffractometer. The microhardness and wear properties of the composite coatings were measured by microhardness tester and friction and wear test. The effects of thermal treatment and no thermal treatment on the mechanical properties of the composite coatings were systematically studied by the above experiments. The experimental analysis shows that the main phase composition of Ni60/2.0 wt.%CeO2 composite coating is γ-(Ni, Fe), Cr7C3, Cr23C6, CrB, CrFeB and Cr2Ni3. Meanwhile, in XRD, it can be seen that the diffraction peak has shifted to the left and the single diffraction peak is decomposed into double diffraction peaks with the thermal treatment at 700 °C, which indicates that the lattice in the coating phase has a certain distortion. Compared with the coatings without thermal treatment, Ni60 cladding layer has more uniform solute distribution and a denser and more uniform structure after thermal treatment at 700 °C. The laser cladding anti-wear composite coating demonstrates that the wear resistance performance shows the greatest improvement after thermal treatment at 700 °C. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhea virus (PEDV) infects piglets and causes serious diarrhea as well as vomiting, dehydration, and death. The trimeric S protein plays a crucial role in the induction of neutralizing antibodies, and many neutralizing monoclonal antibodies (mAbs) against PEDV S protein have been developed. However, these mAbs exclusively target the S1 protein. In this study, we obtained a neutralizing mAb, 5F7, against the S2 protein of PEDV, and this mAb could neutralize new variant genotype 2 PEDV strains (LNCT2), as well as a genotype 1 PEDV strain (CV777), in vitro. The core sequence of the epitope was found in amino acid sequence 1261 aa~1337 aa. These findings confirm that the S2 protein possessed neutralizing epitopes and provided knowledge to aid further research on this virus. [ABSTRACT FROM AUTHOR]

An empirical potential under the formalism of second-moment approximation of tight-binding potential is constructed for an Al-Ni-Ti ternary system and proven reliable in reproducing the physical properties of pure elements and their various compounds. Based on the constructed potential, molecular dynamic simulations are employed to study metallic glass formations and their local atomic arrangements. First, a glass-formation range is determined by comparing the stability of solid solutions and their corresponding counterparts, reflecting the possible composition region energetically favored for the formation of amorphous phases. Second, a favored glass-formation composition subregion around Al0.05Ni0.35Ti0.60 is determined by calculating the amorphous driving forces from crystalline-to-amorphous transition. Moreover, various structural analysis methods are used to characterize the local atomic arrangements of Al0.05NixTi0.95-x metallic glasses. We find that the amorphous driving force is positively correlated with glass-formation ability. It is worth noting that the addition of Ni significantly increases the amorphous driving force configurations of fivefold symmetry and structural disorder in Al0.05NixTi0.95-x metallic glasses until the content of Ni reaches approximately 35 at%. [ABSTRACT FROM AUTHOR]

As clean, renewable energy, photovoltaic (PV) energy can reduce the ozone-layer loss and climate deterioration caused by the use of traditional types of energy to generate electricity. At present, most PV energy products involve the influence of cloud cover on solar radiation. However, the resolution and precision of most cloud cover data are not fine enough to reflect the actual cloud distribution in local areas. This leads to incorrect distribution results of PV energy in areas with high-spatial-variability clouds. Using high-resolution and high-precision cloud cover data obtained by satellite remote sensing to estimate the distribution of PV energy can solve this problem. In this study, the Global Land High-Resolution Cloud Climatology (GLHCC), a 10-day cloud frequency product with a resolution of 1 km and located in China, was used to construct a cloud-based solar radiation estimation model. Using the inverse relationship between cloud cover and solar radiation, the GLHCC was converted into sunshine percentage data. Using meteorological station data in China, a Least Squares Fit (LSF) and error check were carried out on the A-P, Lqbal, Bahel and Sen Models to determine the optimal solar radiation estimation model (Sen Model). Based on the sunshine percentage data, the Sen Model and terrain shielding factors, the distribution of PV energy in China was estimated. Finally, comparing to the Global Horizontal Irradiance (GHI) of the World Bank and the yearly average global irradiance of the Photovoltaic Geographic Information System (PVGIS), PV energy data in this paper more accurately reflected the distribution of PV energy in China, especially in areas with high-spatial-variability clouds. [ABSTRACT FROM AUTHOR]

The partial transmit sequence (PTS) technique is a fairly suitable scheme to mitigate the high peak-to-average power ratio (PAPR) problem inherent in 5G multicarrier systems, especially considering a high-order QAM modulation design. However, the high computational complexity level and the speed of the convergence for optimizing the phases of the transmitting signal restrict this technique in practical applications. In this paper, a low-complexity frequency-domain-evaluated PTS (F-PTS) based on a spacing multiobjective (SMO) processing algorithm is proposed to reduce the PAPR values. The PAPR performance are accurately predicted in terms of modifying relative dispersion in the frequency domain. As a result, the complexity of searching the optimal phase factors and IFFT computing is simplified. Moreover, a frequency-domain- and time-domain-evaluating PTS (FTD-PTS) is employed to search the optimal solution with a reasonable complexity. Simulation results verify that the operation rate of F-PTS is significantly improved after transferring the exhaustive search strategy of PTS into the SMO algorithm, and the F-PTS PAPR reduction performance is just 0.3 dB away from theoretical optimal performance. The FTD-PTS spends an acceptable operation rate to obtain optimal PAPR reduction performance, which subtracts 0.5 and 0.6 dB more than PSO-PTS and conventional PTS at CCDF = 10 − 3 , respectively. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients' survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed. [ABSTRACT FROM AUTHOR]

Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification. [ABSTRACT FROM AUTHOR]

Industrial heat sources have made a great contribution to Chinese economic development. However, it has also been found that emissions from industrial heat sources are the main contribution to regional air pollution. Therefore, the detection of industrial heat sources and the expression of related information is becoming important. In this paper, the detection of industrial heat sources was used to express industrial information, thus that the accuracy of the detection of industrial thermal anomalies could be improved and the problems of noise and missing parameters addressed. A model for expressing industrial information based on object-oriented industrial heat sources and using multi-source thermal anomaly data in China was, therefore, proposed. It was a new real-time, objective, and real way to describe the production operation status of industrial heat sources on a large-scale area. First, 4340 working industrial heat sources in mainland China were detected by applying an adaptive k-means algorithm to ACF (NPP VIIRS 375-m active fire/hotspot data) data from the period 19 January 2012 to 31 December 2020. Secondly, several features of working industrial heat sources were extracted from NPP VIIRS 375-m active fire/hotspot data (ACF), VIIRS Nightfire data (VNF), and the Fires product based on Landsat-8 AIRCAS (L8F) data. Areas containing working industrial heat sources were then identified based on these different types of fire data. Light, land-surface temperature, and CO2 and N2O emissions data related to the working industrial heat sources were also extracted. The results show that feature parameters extracted from the multi-source thermal anomaly data mostly have a good positive correlation with the other parameters. [ABSTRACT FROM AUTHOR]

Smart Grids (SGs) expedite secure, large-scale and efficient two-way communication between the power supply and management, but under a sophisticated 5G communication infrastructure, the multi carrier system is the principle system. The high peak-to-average power ratio (PAPR) is one of the significant limitations of the 5G multi carrier (MC) system, as it impedes the efficient design of the 5G analogue front end. Tone reservation (TR) is a highly efficient scheme without signal distortion, which is designed by increasing the freedom in the frequency domain for PAPR reduction. In this paper, a particle swarm optimization (PSO) based TR (PSO-TR) scheme proceeding with an optimal input power back off-modulation error ratio (IBO-MER) convergence criterion is proposed to improve high power amplifier (HPA) efficiency for OFDM systems. A probabilistic analysis of TR predistribution and freedom in the frequency domain, in relationship with the amplitude of its constituent samples, is carried out. This yields the theoretical framework employed in design of the proposed high computing-enhanced solutions. The proposed PSO-TR essentially make the frequency domain distribution and operation itself adaptive, that is, it adjusts to comply with the changing HPA efficiency and redundant cost during application runtime. [ABSTRACT FROM AUTHOR]

Considering the complexity of landslide hazards, their manual investigation lacks efficiency and is time-consuming, especially in high-altitude plateau areas. Therefore, extracting landslide information using remote sensing technology has great advantages. In this study, comprehensive research was carried out on the landslide features of high-resolution remote sensing images on the Mangkam dataset. Based on the idea of feature-driven classification, the landslide extraction model of a fully convolutional spectral–topographic fusion network (FSTF-Net) based on a deep convolutional neural network of multi-source data fusion is proposed, which takes into account the topographic factor (slope and aspect) and the normalized difference vegetation index (NDVI) as multi-source data input by which to train the model. In this paper, a high-resolution remote sensing image classification method based on a fully convolutional network was used to extract the landslide information, thereby realizing the accurate extraction of the landslide and surrounding ground-object information. With Mangkam County in the southeast of the Qinghai–Tibet Plateau China as the study area, the proposed method was evaluated based on the high-precision digital elevation model (DEM) generated from stereoscopic images of Resources Satellite-3 and multi-source high-resolution remote sensing image data (Beijing-2, Worldview-3, and SuperView-1). Results show that our method had a landslide detection precision of 0.85 and an overall classification accuracy of 0.89. Compared with the latest DeepLab_v3+, our model increases the landslide detection precision by 5%. Thus, the proposed FSTF-Net model has high reliability and robustness. [ABSTRACT FROM AUTHOR]

Clouds play an important role in the energy and moisture cycle of the earth–atmosphere system, which affects many important processes in nature and human societies. However, there are very few fine-grained and high-precision global cloud climatology data available for high-resolution models. In this paper, we produced a fine-grained (1 km resolution) global land cloud climatology (GLHCC) report based on MOD09 cloud masks from 2001 to 2016, with a temporal resolution of 10 days. The two improvements (short-wave infrared and Band 2/6 ratio threshold method) on the original MOD09 cloud mask have reduced the snow, ice, and bright areas mistakenly classified as clouds. The preliminary cloud products undergo the removal of orbital artifacts by Variational Stationary Noise Remover (VSNR) and the removal of abnormal albedo areas to generate the final cloud climatology data. The new product was directly validated by ground-based cloud observations collected from 3777 global weather stations. PATMOS-X from the Advanced Very High Resolution Radiometer (AVHRR) and MOD/MYD35 served as comparison products for consistency check of GLHCC. The assessment results show that GLHCC demonstrated a strong correlation with ground station observations, MOD/MYD35, and PATMOS-X. When the ground observations were taken as the truth value, GLHCC and MOD/MYD35 displayed higher accuracy than PATMOS-X. In most selected interested areas where the three behave differently, GLHCC matched the facts better than MOD/MYD35 and PATMOS-X. The GLHCC can well represent the cloud distribution over the past 16 years and will play an important role in the fine-grained demands of many aspects of nature and human society. [ABSTRACT FROM AUTHOR]

Aerosol Optical Depth (AOD) is a crucial parameter for various environmental and climate studies. Merging multi-sensor AOD products is an effective way to produce AOD products with more spatiotemporal integrity and accuracy. This study proposed a conditional generative adversarial network architecture (AeroCGAN) to improve the estimation of AOD. It first adopted MODIS Multiple Angle Implication of Atmospheric Correction (MAIAC) AOD data to training the initial model, and then transferred the trained model to Himawari data and obtained the estimation of 1-km-resolution, hourly Himawari AOD products. Specifically, the generator adopted an encoder–decoder network for preliminary resolution enhancement. In addition, a three-dimensional convolutional neural network (3D-CNN) was used for environment features extraction and connected to a residual network for improving accuracy. Meanwhile, the sampled data and environment data were designed as conditions of the generator. The spatial distribution feature comparison and quantitative evaluation over an area of the North China Plain during the year 2017 have shown that this approach can better model the distribution of spatial features of AOD data and improve the accuracy of estimation with the help of local environment patterns. [ABSTRACT FROM AUTHOR]

Uranium-Molybdenum alloy has been a promising option in the production of metallic nuclear fuels, where the introduction of Molybdenum enhances mechanical properties, corrosion resistance, and dimensional stability of fuel components. Meanwhile, few potential options for molecular dynamics simulations of U and its alloys have been reported due to the difficulty in the description of the directional effects within atomic interactions, mainly induced by itinerant f-electron behaviors. In the present study, a new angular dependent potential formalism proposed by the author's group has been further applied to the description of the U-Mo systems, which has achieved a moderately well reproduction of macroscopic properties such as lattice constants and elastic constants of reference phases. Moreover, the potential has been further improved to more accurately describe the threshold displacement energy surface at intermediate and short atomic distances. Simulations of primary radiation damage in solid solutions of the U-Mo system have also been carried out and an uplift in the residual defect population has been observed when the Mo content decreases to around 5 wt.%, which corroborates the negative role of local Mo depletion in mitigation of irradiation damage and consequent swelling behavior. [ABSTRACT FROM AUTHOR]

The sea ice cover is changing rapidly in polar regions, and sea ice products with high temporal and spatial resolution are of great importance in studying global climate change and navigation. In this paper, an ice map generation model based on Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance bands is constructed to obtain sea ice data with a high temporal and spatial resolution. By constructing a training sample library and using a multi-feature fusion machine learning algorithm for model classification, the high-accuracy recognition of ice and cloud regions is achieved. The first product provided by this algorithm is a near real-time single-scene sea ice presence map. Compared with the photo-interpreted ground truth, the verification shows that the algorithm can obtain a higher recognition accuracy for ice, clouds, and water, and the accuracy exceeds 98%. The second product is a daily and weekly clear sky map, which provides synthetic ice presence maps for one day or seven consecutive days. A filtering method based on cloud motion is used to make the product more accurate. The third product is a weekly fusion of clear sky optical images. In a comparison with the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration products performed in August 2019 and September 2020, these composite images showed spatial consistency over time, suggesting that they can be used in many scientific and practical applications in the future. [ABSTRACT FROM AUTHOR]

Rotor blades play an important role in unmanned helicopters, and it is of great significance to study the erosion of rotor blades. In this study, titanium alloy (Ti-4Al-1.5Mn) was used as the helicopter rotor blades' surface material. The commercial software Ansys-Fluent 18.0 was mainly used to study the erosion of solid particles on the helicopter rotor blades. The moving mesh method and the discrete phase method (DPM) were used to construct an erosion model of the blades at different speeds (500, 1000, or 2000 rpm), and at different particle mass flow rates (0.5, 1, or 1.5 kg/s). The results show that the erosion of helicopter blades is mainly observed at the leading edge and at the tip of the blades. At different particle mass flow rates, greater particle mass flow rates lead to greater DPM erosion rates. As the blade speed increases, the maximum DPM erosion rate decreases, but the severely eroded area increases. Finally, the values of the severely eroded area of the helicopter rotor blades and the ratios of the severely eroded area growth are obtained through the image processing method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel kinematic and inverse dynamic model for the flybar-less (FBL) swashplate mechanism of a small-scale unmanned helicopter. The swashplate mechanism is an essential configuration of helicopter flight control systems. It is a complex, multi-loop chain mechanism that controls the main rotor. In recent years, the demand for compact swashplate designs has increased owing to the development of small-scale helicopters. The swashplate mechanism proposed in this paper is the latest architectures used for hingeless rotors without a Bell-Hiller mixer. Firstly, the kinematic analysis is derived from the parallel manipulators concepts. Then, based on the principle of virtual work, a methodology for deriving a closed-form dynamic equation of the FBL swashplate mechanism is developed. Finally, the correctness and efficiency of the presented analytical model are demonstrated by numerical examples and the influence factors of the loads acted on actuators are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes neutrosophic vague N-soft sets which is composed of neutrosophic vague sets and N-soft sets for the first time. The new hybrid model includes a pair of asymmetric functions: truth-membership and false-membership, and an indeterminacy-membership function. Some useful operations and propositions are given and illustrated by examples. Moreover, a method of priority relation ranking based on neutrosophic vague N-soft sets is presented. The validity of the method is verified by comparison. It is more flexible and reasonable to use this method in our daily life. Finally, a potential application of multi-attribute decision making is presented. [ABSTRACT FROM AUTHOR]

Polynyas are an important factor in the Antarctic and Arctic climate, and their changes are related to the ecosystems in the polar regions. The phenomenon of polynyas is influenced by the combination of inherent persistence and dynamic factors. The dynamics of polynyas are greatly affected by temporal dynamical factors, and it is difficult to objectively reflect the internal characteristics of their formation. Separating the two factors effectively is necessary in order to explore their essence. The Special Sensor Microwave/Imager (SSM/I) passive microwave sensor has been making observations of Antarctica for more than 20 years, but it is difficult for existing current sea ice concentration (SIC) products to objectively reflect how the inherent persistence factors affect the formation of polynyas. In this paper, we proposed a long-term multiple spatial smoothing method to remove the influence of dynamic factors and obtain stable annual SIC products. A halo located on the border of areas of low and high ice concentration around the Antarctic coast, which has a strong similarity with the local seabed in outline, was found using the spatially smoothed SIC products and seabed. The relationship of the polynya location to the wind and topography is a long-understood relationship; here, we quantify that where there is an abrupt slope and wind transitions, new polynyas are best generated. A combination of image expansion and threshold segmentation was used to extract the extent of sea ice and coastal polynyas. The adjusted record of changes in the extent of coastal polynyas and sea ice in the Southern Ocean indicate that there is a negative correlation between them. [ABSTRACT FROM AUTHOR]

Ice storms greatly affect the structure, dynamics, and functioning of forest ecosystems. Studies on the impact of such disasters, as well as the post-disaster recovery of forests, are important contents in forest biology, ecology, and geography. Remote-sensing technology provides data and methods that can support the study of disasters at the large-to-medium scale and over long time periods. This study took Chebaling National Nature Reserve in Guangdong Province, China, as the study area. First, field-survey data and remote-sensing data were comprehensively analyzed to demonstrate the feasibility of replacing the forest stock volume with the mean annual value of the Enhanced Vegetation Index (EVI), to study forest growth and change. We then used the EVI from 2007 to 2017, together with a variety of other remote-sensing and forest sub-compartment data, to analyze the impact of the 2008 ice storm and the subsequent post-disaster recovery of the forest. Finally, we drew the following conclusions: (1) Topography had a considerable effect on disaster impact and forest recovery in Chebaling. The forest at high altitudes (700–1000 m) and on steep slopes (25–40°) was seriously affected by this disaster but had a stronger post-disaster recovery ability. Meanwhile, the hardest-hit area for coniferous forest was higher and steeper than that for broad-leaved forest. (2) In the same terrain conditions, coniferous forests were less affected by the disaster than broad-leaved forests and showed less variation during the post-disaster recovery process. Nevertheless, broad-leaved forests had faster recovery rates and higher recovery degrees; (3) Under the influence of human activities, the recovery and fluctuation degree for planted forest in the post-disaster recovery process was significantly higher than that for natural forest. The study suggests that forest has high disaster resistance and self-recovery ability after the ice storm, and this ability has a strong correlation with the type of forest and the topographic factors such as elevation and slope. At the same time, human intervention can speed up the recovery of forests after disasters. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhea (PED) re-emerged in China in 2010 and is now widespread. Evidence indicates that highly virulent porcine epidemic diarrhea virus (PEDV) strains belonging to genotype G2 caused a large-scale outbreak of diarrhea. Currently, vaccines derived from PEDV classical strains do not effectively prevent infection by virulent PEDV strains, and no specific drug is available to treat the disease. RNA interference (RNAi) is a novel and effective way to cure a wide range of viruses. We constructed three short hairpin RNA (shRNA)-expressing plasmids (shR-N307, shR-N463, and shR-N1071) directed against nucleocapsid (N) and determined their antiviral activities in intestine epithelial cells infected with a classical CV777 strain and LNCT2. We verified that shR-N307, shR-N463, and shR-N1071 effectively inhibited the expression of the transfected N gene in vitro, comparable to the control shRNA. We further demonstrated the shRNAs markedly reduced PEDV CV777 and LNCT2 replication upon downregulation of N production. Therefore, this study provides a new strategy for the design of antiviral methods against coronaviruses by targeting their processivity factors. [ABSTRACT FROM AUTHOR]

The heavy industry in India has witnessed rapid development in the past decades. This has increased the pressures and load on the Indian environment, and has also had a great impact on the world economy. In this study, the Preparatory Project Visible Infrared Imaging Radiometer (NPP VIIRS) 375-m active fire product (VNP14IMG) and night-time light (NTL) data were used to study the spatiotemporal patterns of heavy industrial development in India. We employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-term VNP14IMG data and artificial heat-source objects. Next, the initial heavy industry heat sources were distinguished from normal heat sources using a threshold recognition model. Finally, the maximum night-time light data were used to delineate the final heavy industry heat sources. The results suggest, that this modified method is a much more accurate and effective way of monitoring heavy industrial heat sources, and the accuracy of this detection model was higher than 92.7%. The number of main findings were concluded from the study: (1) the heavy industry heat sources are mainly concentrated in the north-east Assam state, east-central Jharkhand state, north Chhattisgarh and Odisha states, and the coastal areas of Gujarat and Maharashtra. Many heavy industrial heat sources were also found around a line from Kolkata on the Eastern Indian Ocean to Mumbai on the Western Indian Ocean. (2) The number of working heavy industry heat sources (NWH) and, particularly, the total number of fire hotspots for each working heavy industry heat source area (NFHWH) are continuing to increase in India. These trends mirror those for the Gross Domestic Product (GDP) and total population of India between 2012 and 2017. (3) The largest values of NWH and NFHWH were in Jharkhand, Chhattisgarh, and Odisha whereas the smallest negative values, the S l o p e _ N W H in Jharkhand and Chhattisgarh were also the two largest values in the whole country. The smallest negative values of S l o p e _ N W H and S l o p e _ N F H W H were in Haryana. The S l o p e _ N F H W H in the mainland Gujarat had the second most negative value, while the value of the S l o p e _ N W H was the third-highest positive value. [ABSTRACT FROM AUTHOR]

Class imbalance is a key issue for the application of deep learning for remote sensing image classification because a model generated by imbalanced samples training has low classification accuracy for minority classes. In this study, an accurate classification approach using the multistage sampling method and deep neural networks was proposed to classify imbalanced data. We first balance samples by multistage sampling to obtain the training sets. Then, a state-of-the-art model is adopted by combining the advantages of atrous spatial pyramid pooling (ASPP) and Encoder-Decoder for pixel-wise classification, which are two different types of fully convolutional networks (FCNs) that can obtain contextual information of multiple levels in the Encoder stage. The details and spatial dimensions of targets are restored using such information during the Decoder stage. We employ four deep learning-based classification algorithms (basic FCN, FCN-8S, ASPP, and Encoder-Decoder with ASPP of our approach) on multistage training sets (original, MUS1, and MUS2) of WorldView-3 images in southeastern Qinghai-Tibet Plateau and GF-2 images in northeastern Beijing for comparison. The experiments show that, compared with existing sets (original, MUS1, and identical) and existing method (cost weighting), the MUS2 training set of multistage sampling significantly enhance the classification performance for minority classes. Our approach shows distinct advantages for imbalanced data. [ABSTRACT FROM AUTHOR]

Ab initio calculations were conducted to assist the construction of the n-body potential of the Ti-Nb-Al ternary metal system. Applying the constructed Ti-Nb-Al interatomic potential, molecular dynamics and Monte Carlo simulations were performed to predict a quadrilateral composition region, within which metallic glass was energetically favored to be formed. In addition, the amorphous driving force of those predicted possible glassy alloys was derived and an optimized composition around Ti15Nb45Al40 was pinpointed, implying that this alloy was easier to be obtained. The atomic structure of Ti-Nb-Al metallic glasses was identified by short-, medium-, and extended-range analysis/calculations, and their hierarchical structures were responsible to the formation ability and unique properties in many aspects. [ABSTRACT FROM AUTHOR]

Rapid urbanization and economic development have led to the development of heavy industry and structural re-equalization in mainland China. This has resulted in scattered and disorderly layouts becoming prominent in the region. Furthermore, economic development has exacerbated pressures on regional resources and the environment and has threatened sustainable and coordinated development in the region. The NASA Land Science Investigator Processing System (Land-SIPS) Visible Infrared Imaging Radiometer (VIIRS) 375-m active fire product (VNP14IMG) was selected from the Fire Information for Resource Management System (FIRMS) to study the spatiotemporal patterns of heavy industry development. Furthermore, we employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-order VNP14IMG and constructed heat source objects. Lastly, we used a threshold recognition model to identify heavy industry objects from normal heat source objects. Results suggest that the method is an accurate and effective way to monitor heat sources generated from heavy industry. Moreover, some conclusions about heavy industrial heat source distribution in mainland China at different scales were obtained. Those can be beneficial for policy-makers and heavy industry regulation. [ABSTRACT FROM AUTHOR]

Quarry sites result from human activity, which includes the removal of original vegetation and the overlying soil to dig out stones for building use. Therefore, the dynamics of the quarry area provide a unique view of human mining activities. Actually, the topographic changes caused by mining activities are also a result of the development of the local economy. Thus, monitoring the quarry area can provide information about the policies of the economy and environmental protection. In this paper, we developed a combined method of machine learning classification and quarry region analysis to estimate the quarry area in a quarry region near Beijing. A temporal smoothing based on the classification results of all years was applied in post-processing to remove outliers and obtain gently changing sequences along the monitoring term. The method was applied to Landsat images to derive a quarry distribution map and quarry area time series from 1984 to 2017, revealing significant inter-annual variability. The time series revealed a five-stage development of the quarry area with different growth patterns. As the study region lies on two jurisdictions—Tianjin and Hebei—a comparison of the quarry area changes in the two jurisdictions was applied, which revealed that the different policies in the two regions could impose different impacts on the development of a quarry area. An analysis concerning the relationship between quarry area and gross regional product (GRP) was performed to explore the potential application on socioeconomic studies, and we found a strong positive correlation between quarry area and GRP in Langfang City, Hebei Province. These results demonstrate the potential benefit of annual monitoring over the long-term for socioeconomic studies, which can be used for mining decision making. [ABSTRACT FROM AUTHOR]

An interatomic potential is constructed for the ternary Al-Mg-Zn system under a proposed modified tight-binding scheme, and it is verified to be realistic. Applying this ternary potential, atomistic simulations predict an intrinsic glass formation region in the composition triangle, within which the glassy alloys are more energetically favored in comparison with their solid solution counterparts. Kinetically, the amorphization driving force of each disordered state is derived to correlate the readiness of its glass-forming ability in practice; thus, an optimal stoichiometry region is pinpointed around Al 35 Mg 35 Zn 30 . Furthermore, by monitoring the structural evolution for various (Al 50 Mg 50 ) 1-x Zn x (x = 30, 50, and 70 at.%) compositions, the optimized-glass-former Al 35 Mg 35 Zn 30 is characterized by both the highest degree of icosahedral ordering and the highest phase stability among the investigated compositions. In addition, the icosahedral network in Al 35 Mg 35 Zn 30 exhibits a much higher cross-linking degree than that in Al 25 Mg 25 Zn 50 . This suggests that there is a certain correlation between the icosahedral ordering and the larger glass-forming ability of Al 35 Mg 35 Zn 30 . Our results have significant implications in clarifying glass formation and hierarchical atomic structures, and in designing new ternary Al-Mg-Zn glassy alloys with high GFA.

With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability., Competing Interests: The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.