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  • MDPI AG  (17)
  • 1
    Online Resource
    Online Resource
    U-Net-STN: A Novel End-to-End Lake Boundary Prediction Model
    MDPI AG ; 2023
    In:  Land Vol. 12, No. 8 ( 2023-08-14), p. 1602-
    Details
    In: Land, MDPI AG, Vol. 12, No. 8 ( 2023-08-14), p. 1602-
    Abstract: Detecting changes in land cover is a critical task in remote sensing image interpretation, with particular significance placed on accurately determining the boundaries of lakes. Lake boundaries are closely tied to land resources, and any alterations can have substantial implications for the surrounding environment and ecosystem. This paper introduces an innovative end-to-end model that combines U-Net and spatial transformation network (STN) to predict changes in lake boundaries and investigate the evolution of the Lake Urmia boundary. The proposed approach involves pre-processing annual panoramic remote sensing images of Lake Urmia, obtained from 1996 to 2014 through Google Earth Pro Version 7.3 software, using image segmentation and grayscale filling techniques. The results of the experiments demonstrate the model’s ability to accurately forecast the evolution of lake boundaries in remote sensing images. Additionally, the model exhibits a high degree of adaptability, effectively learning and adjusting to changing patterns over time. The study also evaluates the influence of varying time series lengths on prediction accuracy and reveals that longer time series provide a larger number of samples, resulting in more precise predictions. The maximum achieved accuracy reaches 89.3%. The findings and methodologies presented in this study offer valuable insights into the utilization of deep learning techniques for investigating and managing lake boundary changes, thereby contributing to the effective management and conservation of this significant ecosystem.
    Type of Medium: Online Resource
    ISSN: 2073-445X
    URL: Article
    DOI: 10.3390/land12081602
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2682955-1
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  • 2
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    Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 15, No. 1 ( 2022-12-30), p. 217-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 1 ( 2022-12-30), p. 217-
    Abstract: To meet the demands of natural resource monitoring, land development supervision, and other applications for high-precision and high-frequency information extraction from constructed land change, this paper focused on automatic feature extraction and data processing optimization methods for newly constructed bare land based on remote sensing images. A generalized deep convolutional neural network change detection model framework integrating multi-scale information was developed for the automatic extraction of change information. To resolve the problems in the automatic extraction of new bare land parcels, such as mis-extractions and parcel fragmentation, a proximity evaluation model that integrates the confidence-based semantic distance and spatial distance between parcels and their overlapping area is proposed to perform parcel aggregation. Additionally, we propose a complete set of optimized processing techniques from pixel pre-processing to vector post-processing. The results demonstrated that the aggregation method developed in this study is more targeted and effective than ArcGIS for the automatically extracted land change parcels. Additionally, compared with the initial parcels, the total number of optimized parcels decreased by more than 50% and the false detection rate decreased by approximately 30%. These results indicate that this method can markedly reduce the overall data volume and false detection rate of automatically extracted parcels through post-processing under certain conditions of the model and samples and provide technical support for applying the results of automatic feature extraction in engineering practices.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15010217
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 3
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    Characterization of the WAK Gene Family Reveals Genes for FHB Resistance in Bread Wheat (Triticum aestivum L.)
    MDPI AG ; 2022
    In:  International Journal of Molecular Sciences Vol. 23, No. 13 ( 2022-06-28), p. 7157-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 23, No. 13 ( 2022-06-28), p. 7157-
    Abstract: Wall-associated kinases (WAKs) are important receptor-like proteins that play major roles in plant defense against pathogens. Fusarium head blight (FHB), one of the most widespread and devastating crop diseases, reduces wheat yield and leads to quality deterioration. Although WAK gene families have been studied in many plants, systematic research on bread wheat (Triticum aestivum) and its role in FHB resistance, in particular, is lacking. In this study, we identified and characterized 320 genes of the TaWAK family in wheat distributed across all chromosomes except 4B and divided them into three phylogenetic groups. Duplication and synteny analyses provided valuable information on the evolutionary characteristics of the TaWAK genes. The gene expression pattern analysis suggested that TaWAK genes play diverse roles in plant biological processes and that at least 30 genes may be involved in the response to Fusarium infection in wheat spikes, with most of the genes contributing to pectin- and chitin-induced defense pathways. Furthermore, 45 TaWAK genes were identified within 17 hcmQTLs that are related to wheat FHB resistance. Our findings provide potential candidate genes for improving FHB resistance and insights into the future functional analysis of TaWAK genes in wheat.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms23137157
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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  • 4
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    Effect of Weaning at 21 Days of Age on the Content of Bile Acids in Chyme of Cecum
    MDPI AG ; 2022
    In:  Animals Vol. 12, No. 16 ( 2022-08-20), p. 2138-
    Details
    In: Animals, MDPI AG, Vol. 12, No. 16 ( 2022-08-20), p. 2138-
    Abstract: This experiment was conducted to investigate the effects of weaning at 21 days of age on cecal chyme bile acids (BAs) in piglets. According to a 2 × 3 factorial design, the main factors were lactation and weaning, and the other factor was 22, 24, and 28 days of age, respectively. Piglets were randomly divided into two groups of eighteen piglets each and six piglets were selected for slaughter at 22, 24, and 28 days of age, respectively, to determine the content of different types of Bas in the intestinal lumen of the cecum. Results: (1) There was a significant interaction between weaning and age on intestinal primary Bas hyocholic acid (HCA) and chenodeoxycholic acid (CDCA) (p 〈 0.05), and weaning significantly increased the content of primary BAs in piglets’ intestines, which showed a trend of decreasing and then increasing with the increase in piglets’ age. (2) There was a significant interaction between weaning and age on intestinal secondary BAs deoxycholic acid (DCA), lithocholic acid (LCA), and ursodeoxycholic acid (UDCA) (p 〈 0.05). DCA and LCA in piglets’ intestines tended to decrease with increasing age, while UDCA showed a trend of decreasing and then increasing with increasing piglets’ age; weaning significantly increased the content of secondary BAs in piglets’ intestines. (3) There was a significant interaction between weaning and age on intestinal glycine chenodeoxycholic acid (GCDCA), taurochenodeoxycholic acid (TCDCA), and taurolithocholic acid (TLCA), but not on taurohyocholic acid (THCA), taurohyodeoxycholic acid (THDCA), and taurineursodeoxycholic acid (TUDCA) (p 〉 0.05). Weaning significantly increased the contents of GCDCA, TCDCA, TLCA, THDCA, and TUDCA in the intestinal tract (p 〈 0.05), while THCA content was not significant. In conclusion, weaning can increase the BAs content in the cecum of piglets, and there is an interaction between group and weaning age on BAs content.
    Type of Medium: Online Resource
    ISSN: 2076-2615
    URL: Article
    DOI: 10.3390/ani12162138
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2606558-7
    SSG: 23
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  • 5
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    Haze Grading Using the Convolutional Neural Networks
    MDPI AG ; 2022
    In:  Atmosphere Vol. 13, No. 4 ( 2022-03-25), p. 522-
    Details
    In: Atmosphere, MDPI AG, Vol. 13, No. 4 ( 2022-03-25), p. 522-
    Abstract: As an air pollution phenomenon, haze has become one of the focuses of social discussion. Research into the causes and concentration prediction of haze is significant, forming the basis of haze prevention. The inversion of Aerosol Optical Depth (AOD) based on remote sensing satellite imagery can provide a reference for the concentration of major pollutants in a haze, such as PM2.5 concentration and PM10 concentration. This paper used satellite imagery to study haze problems and chose PM2.5, one of the primary haze pollutants, as the research object. First, we used conventional methods to perform the inversion of AOD on remote sensing images, verifying the correlation between AOD and PM2.5. Subsequently, to simplify the parameter complexity of the traditional inversion method, we proposed using the convolutional neural network instead of the traditional inversion method and constructing a haze level prediction model. Compared with traditional aerosol depth inversion, we found that convolutional neural networks can provide a higher correlation between PM2.5 concentration and satellite imagery through a more simplified satellite image processing process. Thus, it offers the possibility of researching and managing haze problems based on neural networks.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    URL: Article
    DOI: 10.3390/atmos13040522
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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  • 6
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    Emerging Lignin-Based Materials in Electrochemical Energy Systems
    MDPI AG ; 2022
    In:  Energies Vol. 15, No. 24 ( 2022-12-13), p. 9450-
    Details
    In: Energies, MDPI AG, Vol. 15, No. 24 ( 2022-12-13), p. 9450-
    Abstract: Lignin is a promising material due to its excellent properties. It is commonly used in electrochemical energy systems (including electrolytes, electrodes, diaphragms, and binders) due to its low price, sustainability and rich functional groups. However, lignin’s applications in energy storage systems have not been systematically reviewed in the current research. In this article, recent advances in the preparation and design of lignin-derived energy storage materials were reviewed. Starting with a brief overview of the basic chemistry of lignin and the separation process, progress in the preparation of lignin-based materials for lithium-ion batteries, supercapacitors, fuel cells, and solar cells were described, respectively. This review provides the basis for the application of lignin in the field of electrochemical energy systems. Also, the current bottleneck problems and perspectives of lignin-derived materials in improved energy storage device performance were presented for future developments.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    URL: Article
    DOI: 10.3390/en15249450
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2437446-5
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  • 7
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    Evaluation of Empirical Atmospheric Models Using Swarm-C Satellite Data
    MDPI AG ; 2022
    In:  Atmosphere Vol. 13, No. 2 ( 2022-02-09), p. 294-
    Details
    In: Atmosphere, MDPI AG, Vol. 13, No. 2 ( 2022-02-09), p. 294-
    Abstract: Swarm-C satellite, a new instrument for atmospheric study, has been the focus of many studies to evaluate its usage and accuracy. This paper takes the Swarm-C satellite as a research object to verify the Swarm-C accelerometer’s inversion results. This paper uses the two-row orbital elements density inversion to verify the atmospheric density accuracy results of the Swarm-C satellite accelerometer. After the accuracy of the satellite data is verified, this paper conducts comparative verification and empirical atmospheric model evaluation experiments based on the Swarm-C accelerometer’s inversion results. After comparing with the inversion results of the Swarm-C semi-major axis attenuation method, it is found that the atmospheric density obtained by inversion using the Swarm-C accelerometer is more dynamic and real-time. It shows that with more available data, the Swarm-C satellite could be a new high-quality instrument for related studies along with the well-established satellites. After evaluating the performance of the JB2008 and NRLMSISE-00 empirical atmospheric models using the Swarm-C accelerometer inversion results, it is found that the accuracy and real-time performance of the JB2008 model at the altitude where the Swarm-C satellite is located are better than the NRLMSISE-00 model.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    URL: Article
    DOI: 10.3390/atmos13020294
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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  • 8
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    Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
    MDPI AG ; 2023
    In:  Applied Sciences Vol. 13, No. 6 ( 2023-03-11), p. 3610-
    Details
    In: Applied Sciences, MDPI AG, Vol. 13, No. 6 ( 2023-03-11), p. 3610-
    Abstract: We used the Swarm-C accelerometer data to invert the orbital atmospheric density in this study. First, the Swarm-C satellite mission data were obtained from the ESA’s public data platform, and preliminary data error correction was performed. This paper refers to the calibration method of GRACE-A satellite accelerometer data. It adds linear temperature correction on the original basis. Moreover, this study’s accelerometer data correction results were compared with the data correction results published by the ESA. In order to explore the influence of light radiation on the accelerometer, we established a geometric model of Swarm-C to simulate the physical shape of the satellite surface. The light radiation pressure model and the shadow area judgment model were established, the change in the light radiation acceleration during the transition process of the satellite from the umbra area to the penumbra area and then to the shadowless area was studied, and the state transition during the transition process was analyzed. Finally, the atmospheric drag coefficient was calculated based on the Sentman model. Atmospheric density inversion calculations were performed using the above data. We show the spatial distribution of atmospheric density at a fixed latitude, testing our results during geomagnetic storms. We compared the density results with existing research data, demonstrating the effectiveness of our approach.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app13063610
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704225-X
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  • 9
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    U-Net-LSTM: Time Series-Enhanced Lake Boundary Prediction Model
    MDPI AG ; 2023
    In:  Land Vol. 12, No. 10 ( 2023-09-29), p. 1859-
    Details
    In: Land, MDPI AG, Vol. 12, No. 10 ( 2023-09-29), p. 1859-
    Abstract: Change detection of natural lake boundaries is one of the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons in each layer can only be propagated to the upper layer, and the processing of samples is independent at each moment. However, for time-series data with transferability, the learned change information needs to be recorded and utilized. To solve the above problems, we propose a lake boundary change prediction model combining U-Net and LSTM. The ensemble of LSTMs helps to improve the overall accuracy and robustness of the model by capturing the spatial and temporal nuances in the data, resulting in more precise predictions. This study selected Lake Urmia as the research area and used the annual panoramic remote sensing images from 1996 to 2014 (Lat: 37°00′ N to 38°15′ N, Lon: 46°10′ E to 44°50′ E) obtained by Google Earth Professional Edition 7.3 software as the research data set. This model uses the U-Net network to extract multi-level change features and analyze the change trend of lake boundaries. The LSTM module is introduced after U-Net to optimize the predictive model using historical data storage and forgetting as well as current input data. This method enables the model to automatically fit the trend of time series data and mine the deep information of lake boundary changes. Through experimental verification, the model’s prediction accuracy for lake boundary changes after training can reach 89.43%. Comparative experiments with the existing U-Net-STN model show that the U-Net-LSTM model used in this study has higher prediction accuracy and lower mean square error.
    Type of Medium: Online Resource
    ISSN: 2073-445X
    URL: Article
    DOI: 10.3390/land12101859
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2682955-1
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  • 10
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    Adsorption of Different Pollutants by Using Microplastic with Different Influencing Factors and Mechanisms in Wastewater: A Review
    MDPI AG ; 2022
    In:  Nanomaterials Vol. 12, No. 13 ( 2022-06-30), p. 2256-
    Details
    In: Nanomaterials, MDPI AG, Vol. 12, No. 13 ( 2022-06-30), p. 2256-
    Abstract: The studies on microplastics are significant in the world. According to the literature, microplastics have greatly specific surface areas, indicating high adsorption capacities for highly toxic pollutants in aquatic and soil environments, and these could be used as adsorbents. The influencing factors of microplastic adsorption, classification of microplastics, and adsorption mechanisms using microplastics for adsorbing organic, inorganic, and mixed pollutants are summarized in the paper. Furthermore, the influence of pH, temperature, functional groups, aging, and other factors related to the adsorption performances of plastics are discussed in detail. We found that microplastics have greater advantages in efficient adsorption performance and cost-effectiveness. In this paper, the adsorptions of pollutants by microplastics and their performance is proposed, which provides significant guidance for future research in this field.
    Type of Medium: Online Resource
    ISSN: 2079-4991
    URL: Article
    DOI: 10.3390/nano12132256
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662255-5
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