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  • 1
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    Online Resource
    A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 12 ( 2022-06-17), p. 2902-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 12 ( 2022-06-17), p. 2902-
    Abstract: The coarse scale of passive microwave surface soil moisture (SSM) is not suitable for regional agricultural and hydrological applications such as drought monitoring and irrigation management. The optical/thermal infrared (OTI) data-based passive microwave SSM downscaling method can effectively improve its spatial resolution to fine scale for regional applications. However, the estimation capability of SSM with long time series is limited by OTI data, which are heavily polluted by clouds. To reduce the dependence of the method on OTI data, an SSM retrieval and spatio-temporal fusion model (SMRFM) is proposed in the study. Specifically, a model coupling in situ data, MODerate-resolution Imaging Spectro-radiometer (MODIS) OTI data, and topographic information is developed to retrieve MODIS SSM (1 km) using the least squares method. Then the retrieved MODIS SSM and the spatio-temporal fusion model are employed to downscale the passive microwave SSM from coarse scale to 1 km. The proposed SMRFM is implemented in a grassland dominated area over Naqu, central Tibet Plateau, for Advanced Microwave Scanning Radiometer—Earth Observing System sensor (AMSR-E) SSM downscaling in unfrozen period. The in situ SSM and Noah land surface model 0.01° SSM are used to validate the estimated MODIS SSM with long time series. The evaluations show that the estimated MODIS SSM has the same temporal resolution with AMSR-E and obtains significantly improved detailed spatial information. Moreover, the temporal accuracy of estimated MODIS SSM against in situ data (r = 0.673, μbRMSE = 0.070 m3/m3) is better than the AMSR-E (r = 0.661, μbRMSE = 0.111 m3/m3). In addition, the temporal r of estimated MODIS SSM is obviously higher than that of Noah data. Therefore, this suggests that the SMRFM can be used to estimate MODIS SSM with long time series by AMSR-E SSM downscaling in the study. Overall, the study can provide help for the development and application of microwave SSM-related scientific research at the regional scale.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14122902
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 2
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    Deep Learning Model for Soil Environment Quality Classification of Pu-erh Tea
    MDPI AG ; 2022
    In:  Forests Vol. 13, No. 11 ( 2022-10-27), p. 1778-
    Details
    In: Forests, MDPI AG, Vol. 13, No. 11 ( 2022-10-27), p. 1778-
    Abstract: Pu-erh tea, Camellia sinensis is a traditional Chinese tea, one of the black teas, originally produced in China’s Yunnan Province, named after its origin and distribution center in Pu-erh, Yunnan. Yunnan Pu-erh tea is protected by geographical Indication and has unique quality characteristics. It is made from Yunnan large-leaf sun-green tea with specific processing techniques. The quality formation of Pu-erh tea is closely related to the soil’s environmental conditions. In this paper, time-by-time data of the soil environment of tea plantations during the autumn tea harvesting period in Menghai County, Xishuangbanna, Yunnan Province, China, in 2021 were analyzed. Spearman’s correlation analysis was conducted between the inner components of Pu’er tea and the soil environmental factor. The analysis showed that three soil environmental indicators, soil temperature, soil moisture, and soil pH, were highly significantly correlated. The soil environmental quality evaluation method was proposed based on the selected soil environmental characteristics. Meanwhile, a deep learning model of Long Short Term Memory (LSTM) Network for the soil environmental quality of tea plantation was established according to the proposed method, and the soil environmental quality of tea was classified into four classes. In addition, the paper also compares the constructed models based on BP neural network and random forest to evaluate the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the indicators for comparative analysis. This paper innovatively proposes to introduce the main inclusions of Pu’er tea into the classification and discrimination model of the soil environment in tea plantations, while using machine learning-related algorithms to classify and predict the categories of soil environmental quality, instead of relying solely on statistical data for analysis. This research work makes it possible to quickly and accurately determines the physiological status of tea leaves based on the establishment of a soil environment quality prediction model, which provides effective data for the intelligent management of tea plantations and has the advantage of rapid and low-cost assessment compared with the need to measure the intrinsic quality of Pu-erh tea after harvesting is completed.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    URL: Article
    DOI: 10.3390/f13111778
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527081-3
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  • 3
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    Utilizing Protein–Peptide Hybrid Microarray for Time-Resolved Diagnosis and Prognosis of COVID-19
    MDPI AG ; 2023
    In:  Microorganisms Vol. 11, No. 10 ( 2023-09-28), p. 2436-
    Details
    In: Microorganisms, MDPI AG, Vol. 11, No. 10 ( 2023-09-28), p. 2436-
    Abstract: The COVID-19 pandemic has highlighted the urgent need for accurate, rapid, and cost-effective diagnostic methods to identify and track the disease. Traditional diagnostic methods, such as PCR and serological assays, have limitations in terms of sensitivity, specificity, and timeliness. To investigate the potential of using protein–peptide hybrid microarray (PPHM) technology to track the dynamic changes of antibodies in the serum of COVID-19 patients and evaluate the prognosis of patients over time. A discovery cohort of 20 patients with COVID-19 was assembled, and PPHM technology was used to track the dynamic changes of antibodies in the serum of these patients. The results were analyzed to classify the patients into different disease severity groups, and to predict the disease progression and prognosis of the patients. PPHM technology was found to be highly effective in detecting the dynamic changes of antibodies in the serum of COVID-19 patients. Four polypeptide antibodies were found to be particularly useful for reflecting the actual status of the patient’s recovery process and for accurately predicting the disease progression and prognosis of the patients. The findings of this study emphasize the multi-dimensional space of peptides to analyze the high-volume signals in the serum samples of COVID-19 patients and monitor the prognosis of patients over time. PPHM technology has the potential to be a powerful tool for tracking the dynamic changes of antibodies in the serum of COVID-19 patients and for improving the diagnosis and prognosis of the disease.
    Type of Medium: Online Resource
    ISSN: 2076-2607
    URL: Article
    DOI: 10.3390/microorganisms11102436
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720891-6
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  • 4
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    Remaining Useful Life Prediction Based on Deep Learning: A Survey
    MDPI AG ; 2024
    In:  Sensors Vol. 24, No. 11 ( 2024-05-27), p. 3454-
    Details
    In: Sensors, MDPI AG, Vol. 24, No. 11 ( 2024-05-27), p. 3454-
    Abstract: Remaining useful life (RUL) is a metric of health state for essential equipment. It plays a significant role in health management. However, RUL is often random and unknown. One type of physics-based method builds a mathematical model for RUL using prior principles, but this is a tough task in real-world applications. Another type of method estimates RUL from available information through condition and health monitoring; this is known as the data-driven method. Traditional data-driven methods require significant human effort in designing health features to represent performance degradation, yet the prediction accuracy is limited. With breakthroughs in various application scenarios in recent years, deep learning techniques provide new insights into this problem. Over the past few years, deep-learning-based RUL prediction has attracted increasing attention from the academic community. Therefore, it is necessary to conduct a survey on deep-learning-based RUL prediction. To ensure a comprehensive survey, the literature is reviewed from three dimensions. Firstly, a unified framework is proposed for deep-learning-based RUL prediction and the models and approaches in the literature are reviewed under this framework. Secondly, detailed estimation processes are compared from the perspective of different deep learning models. Thirdly, the literature is examined from the perspective of specific problems, such as scenarios where the collected data consist of limited labeled data. Finally, the main challenges and future directions are summarized.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s24113454
    Language: English
    Publisher: MDPI AG
    Publication Date: 2024
    detail.hit.zdb_id: 2052857-7
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  • 5
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    Optical Fiber Biosensors for Protein Detection: A Review
    MDPI AG ; 2022
    In:  Photonics Vol. 9, No. 12 ( 2022-12-15), p. 987-
    Details
    In: Photonics, MDPI AG, Vol. 9, No. 12 ( 2022-12-15), p. 987-
    Abstract: Proteins play an important role in organisms. The fast and high-accuracy detection of proteins is demanded in various fields, such as healthcare, food safty, and biosecurity, especially in the background of the globally raging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Optical fiber sensors have great potential for protein detection due to the excellent characteristics of high sensitivity, miniaturization, and capability for remote monitoring. Over the past decades, a large number of structures have been investigated and proposed. This paper begins with an overview of different fiber sensing structures for protein detection according to the fundamental sensing mechanisms. The overview is classified into four sections, including intensity-modulation, phase-modulation, scattering, and fluorescence. In each section, we reviewed the recent advances of fiber protein sensors and compared their performance, such as sensitivity and limit of detection. And then we analyzed the advantages and disadvantages of the four kinds of biosensors. Finally, the paper concludes with the challenges faced and possible future development of optical fiber protein biosensors for further study.
    Type of Medium: Online Resource
    ISSN: 2304-6732
    URL: Article
    DOI: 10.3390/photonics9120987
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2770002-1
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  • 6
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    Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 23 ( 2020-11-25), p. 3860-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 23 ( 2020-11-25), p. 3860-
    Abstract: Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12233860
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 7
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    Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 8 ( 2021-07-30), p. 515-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 8 ( 2021-07-30), p. 515-
    Abstract: Global climate change and human activities have resulted in immense changes in the Earth’s ecosystem, and the interaction between the land surface and the atmosphere is one of the most important processes. Wind is a reference for studying atmospheric dynamics and climate change, analyzing the wind speed change characteristics in historical periods, and studying the influence of wind on the Earth-atmosphere interaction; additionally, studying the wind, contributes to analyzing and alleviating a series of problems, such as the energy crisis, environmental pollution, and ecological deterioration facing human beings. In this study, data from 697 meteorological stations in China from 2000 to 2019 were used to study the distribution and trend of wind speed over the past two decades. The relationships between wind speed and climate factors were explored using statistical methods; furthermore, combined with terrain, climate change, and human activities, we quantified the contribution of environmental factors to wind speed. The results show that a downward trend was recorded before 2011, but overall, there was an increasing trend that was not significant; moreover, the wind speed changes showed obvious seasonality and were more complicated on the monthly scale. The wind speed trend mainly increased in the western region, decreased in the eastern region, was higher in the northeastern, northwestern, and coastal areas, and was lower in the central area. Temperature, bright sunshine duration, evaporation, and precipitation had a strong influence, in which wind speed showed a significant negative correlation with temperature and precipitation and vice versa for sunshine and evapotranspiration. The influence of environmental factors is diverse, and these results could help to develop environmental management strategies across ecologically fragile areas and improve the design of wind power plants to make better use of wind energy.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10080515
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 8
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    Solution Growth of Two-Dimensional Bi2Se3 Nanosheets for Two-Color All-Optical Switching
    MDPI AG ; 2017
    In:  Materials Vol. 10, No. 11 ( 2017-11-21), p. 1332-
    Details
    In: Materials, MDPI AG, Vol. 10, No. 11 ( 2017-11-21), p. 1332-
    Abstract: Two-dimensional Bi2Se3 nanosheets with hexagonal shape are synthesized by a solution synthetic route. The Bi2Se3 nanosheets are 120 nm in edge width and 7 nm in thickness. The size of the Bi2Se3 nanosheets can be controlled by choosing different kinds of reducing agents including hydroxylamine and ethylenediamine. Subsequently, we demonstrate a configuration of two-color all-optical switching based on plasma channels effect using the as-synthesized Bi2Se3 nanosheets as an optical media. The signal light can be modulated as two states including dot and ring shape by changing the intensity of control light. The modulated signal light exhibits excellent spatial propagation properties. As a type of interesting optical material, ultrathin two-dimensional Bi2Se3 nanosheets might provide an effective option for photoelectric applications.
    Type of Medium: Online Resource
    ISSN: 1996-1944
    URL: Article
    DOI: 10.3390/ma10111332
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2487261-1
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  • 9
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    A Novel Obstacle Localization Method for an Underwater Robot Based on the Flow Field
    MDPI AG ; 2019
    In:  Journal of Marine Science and Engineering Vol. 7, No. 12 ( 2019-11-29), p. 437-
    Details
    In: Journal of Marine Science and Engineering, MDPI AG, Vol. 7, No. 12 ( 2019-11-29), p. 437-
    Abstract: Because the underwater environment is complex, autonomous underwater vehicles (AUVs) have difficulty locating their surroundings autonomously. In order to improve the adaptive ability of AUVs, this paper presents a novel obstacle localization strategy based on the flow features. Like fish, the strategy uses the flow field information directly to locate the object obstacles. Two different localization methods are provided and compared. The first method, which is named the Method of Spatial Distribution (MSD), is based on the spatial distribution of the flow field. The second method, which is named the Method of Amplitude Variation (MAV), is provided by the amplitude variation of the flow field. The flow field around spherical targets is obtained by a numerical method, and both methods use the parallel velocity component on the virtual lateral line. During the study, different target numbers, detective ratios, spacing ratios, and flow velocities are taken into account. It is demonstrated that both methods are able to locate object obstacles. However, the prediction accuracy of MAV is higher than that of MSD. That implies that MAV is more robust than MSD. These new findings indicate that the object obstacles can be directly located based on the flow field information and robust flow sensing is perhaps not based on the spatial distribution of the flow field but rather, on its fluctuation range.
    Type of Medium: Online Resource
    ISSN: 2077-1312
    URL: Article
    DOI: 10.3390/jmse7120437
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2738390-8
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  • 10
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    Can the Digital Economy Empower Low-Carbon Transition Development? New Evidence from Chinese Resource-Based Cities
    MDPI AG ; 2024
    In:  Sustainability Vol. 16, No. 14 ( 2024-07-12), p. 5966-
    Details
    In: Sustainability, MDPI AG, Vol. 16, No. 14 ( 2024-07-12), p. 5966-
    Abstract: Existing research lacks a systematic and comprehensive analysis of the digital economy (DE)’s impact on the low-carbon transformation of resource-based cities. This study utilizes panel data from 114 of these cities in China from 2006 to 2019 to construct a DE measurement system. Based on the global SBM directional distance function and the Malmquist–Luenberger index (SBM-DDF-GML), we calculated the total factor carbon productivity (TFCP), decomposed the carbon inefficiency value (CIV), and examined DE’s impact, mechanism, and heterogeneity on low-carbon transition development (LCTD) during distinct growth phases of resource-based cities. Based on this examination, we found the following: (1) The DE effectively reduced carbon intensity and inefficiency and improved the total factor carbon productivity in resource-based cities. These findings remained robust after a series of robustness tests. (2) The DE empowered LCTD by improving energy efficiency, upgrading industrial structure, and optimizing innovation factor allocation. Finally, (3) this effect varied across the different city stages, being most significant in mature cities and weakest in declining ones. The research findings provide empirical evidence for the LCTD of resource-based cities.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su16145966
    Language: English
    Publisher: MDPI AG
    Publication Date: 2024
    detail.hit.zdb_id: 2518383-7
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