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  • 1
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Sustainability Vol. 15, No. 10 ( 2023-05-22), p. 8392-
    In: Sustainability, MDPI AG, Vol. 15, No. 10 ( 2023-05-22), p. 8392-
    Abstract: Digital transformation is a crucial strategy for enterprises to achieve improvements in quality, efficiency, and dynamism. It represents the key direction for enterprise innovation and reform, and it is also of great significance for promoting high-quality economic development. While the previous research has shown that digital transformation can affect business efficiency, performance, and corporate governance, there is a lack of literature clearly linking digital transformation to internal control. To determine whether enterprise digital transformation impacts the constructions and implementation of internal control, we analyze data from Shanghai and Shenzhen A-share listed companies between 2012 and 2019. Our study found that the extent of digital transformation in enterprises has a positive impact on the establishment and effectiveness of internal control. Furthermore, we examine the role of market competition in the relationship between the digital transformation and internal control. We found that the impact of digital transformation on internal control is limited in enterprises with relatively mild market competition, while in more fiercely competitive markets, the positive impact of digital transformation on internal control is more pronounced.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2518383-7
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  • 2
    Online Resource
    Online Resource
    MDPI AG ; 2015
    In:  Nanomaterials Vol. 5, No. 3 ( 2015-08-28), p. 1442-1453
    In: Nanomaterials, MDPI AG, Vol. 5, No. 3 ( 2015-08-28), p. 1442-1453
    Type of Medium: Online Resource
    ISSN: 2079-4991
    Language: English
    Publisher: MDPI AG
    Publication Date: 2015
    detail.hit.zdb_id: 2662255-5
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  • 3
    In: Nanomaterials, MDPI AG, Vol. 13, No. 1 ( 2022-12-29), p. 157-
    Abstract: Hydrogels have attracted much attraction for promising flexible electronics due to the versatile tunability of the properties. However, there is still a big obstacle to balance between the multi-properties and performance of wearable electronics. Herein, we propose a salt-percolated nanocellulose composite hydrogel which was fabricated via radical polymerization with acrylic acid as polymer networks (NaCl-CNCs-PAA). CNCs were utilized as a reinforcing agent to enhance the mechanical properties of the hydrogel. Moreover, the abundant hydroxyl groups endow the hydrogel with noncovalent interactions, such as hydrogen bonding, and the robustness of the hydrogel was thus improved. NaCl incorporation induced the electrostatic interaction between CNCs and PAA polymer blocks, thus facilitating the improvement of the stretchability of the hydrogel. The as-obtained hydrogel exhibited excellent stretchability, ionic conductivity, mechanical robustness and anti-freezing properties, making it suitable for self-powered sensing applications. A single-mode triboelectric nanogenerator (C-TENG) was fabricated by utilizing the composite hydrogel as electrodes. This C-TENG could effectively convert biomechanical energy to electricity (89.2 V, 1.8 µA, 32.1 nC, and the max power density of 60.8 mW m−2 at 1.5 Hz.) Moreover, the composite hydrogel was applied for strain sensing to detect human motions. The nanocellulose composite hydrogel can achieve the application as a power supply in integrated sensing systems and as a strain sensor for human motion detection.
    Type of Medium: Online Resource
    ISSN: 2079-4991
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662255-5
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  • 4
    In: Remote Sensing, MDPI AG, Vol. 10, No. 9 ( 2018-09-14), p. 1468-
    Abstract: Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2513863-7
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  • 5
    Online Resource
    Online Resource
    MDPI AG ; 2019
    In:  Remote Sensing Vol. 11, No. 18 ( 2019-09-16), p. 2156-
    In: Remote Sensing, MDPI AG, Vol. 11, No. 18 ( 2019-09-16), p. 2156-
    Abstract: Hainan Island is the second-largest island in China and has the most species-diverse mangrove forests in the country. To date, the height and aboveground ground biomass (AGB) of the mangrove forests on Hainan Island are unknown, partly as a result of the challenges faced during extensive field sampling in mangrove habitats (intertidal mudflats inundated by periodic seawater). Therefore, this study used a low-cost UAV-LiDAR (light detection and ranging sensor mounted on an unmanned aerial vehicle) system as a sampling tool and Sentinel-2 imagery as auxiliary data to estimate and map the mangrove height and AGB on Hainan Island. Hainan Island has 3697.02 hectares of mangrove forests with an average patch area of approximately 1 ha. The results show that the mangroves on whole Hainan Island have an average height of 6.99 m, a total AGB of 474,199.31 Mg and an AGB density of 128.27 Mg ha−1. The AGB hot spots are located in Qinglan Harbor and the south of Dongzhai Harbor. The proposed height model LiDAR-S2 performed well with an R2 of 0.67 and an RMSE (root mean square error) of 1.90 m; the proposed AGB model G~LiDAR~S2 performed better (an R2 of 0.62 and an RMSE of 50.36 Mg ha−1) than the traditional AGB model G~S2 that directly related ground plots and Sentinel-2 data. The results also indicate that the LiDAR metrics describing the canopy’s thickness and its top and bottom characteristics are the most important variables for mangrove AGB estimation. For the Sentinel-2 indices, the red-edge and shortwave infrared features, especially the red-edge 1 and shortwave infrared Band 11 features, play the most important roles in estimating mangrove AGB and height. In conclusion, this paper presents the first mangrove height and AGB maps of Hainan Island and demonstrates the feasibility of using UAV-LiDAR as a sampling tool for mangrove forests.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2513863-7
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  • 6
    In: Remote Sensing, MDPI AG, Vol. 10, No. 2 ( 2018-02-14), p. 294-
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2513863-7
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  • 7
    In: Polymers, MDPI AG, Vol. 11, No. 7 ( 2019-07-11), p. 1171-
    Abstract: Fluorescence/temperature-sensitive hydrogels, thanks to their properties in fluorescence and temperature sensitivity, have shown a promising outlook in the fields of drug delivery, cell imaging, etc., and thus become the focus of present research. This paper reports the preparation of green-fluorescence/temperature-sensitive hydrogels through one-step radical polymerization with green fluorescence-emissioned carbon dots as fluorescence probes and N-isopropylacrylamide as a monomer. UV-vis spectra, fluorescence spectra, and fluorescence microscope imaging have been used to characterize the prepared hydrogel, and to study their optical and temperature-sensitive properties. It was discovered that the emission of prepared hydrogel is excitation wavelength-dependent, pH responding, and excellent temperature-sensitive, as well as having good biocompatibility. The prepared hydrogel can also be applied as fluorescence ink in the fields of anti-counterfeit identification and appraisal.
    Type of Medium: Online Resource
    ISSN: 2073-4360
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2527146-5
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  • 8
    Online Resource
    Online Resource
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 7 ( 2019-07-23), p. 315-
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 7 ( 2019-07-23), p. 315-
    Abstract: Imbalanced learning is a methodological challenge in remote sensing communities, especially in complex areas where the spectral similarity exists between land covers. Obtaining high-confidence classification results for imbalanced class issues is highly important in practice. In this paper, extreme gradient boosting (XGB), a novel tree-based ensemble system, is employed to classify the land cover types in Very-high resolution (VHR) images with imbalanced training data. We introduce an extended margin criterion and disagreement performance to evaluate the efficiency of XGB in imbalanced learning situations and examine the effect of minority class spectral separability on model performance. The results suggest that the uncertainty of XGB associated with correct classification is stable. The average probability-based margin of correct classification provided by XGB is 0.82, which is about 46.30% higher than that by random forest (RF) method (0.56). Moreover, the performance uncertainty of XGB is insensitive to spectral separability after the sample imbalance reached a certain level (minority:majority 〉 10:100). The impact of sample imbalance on the minority class is also related to its spectral separability, and XGB performs better than RF in terms of user accuracy for the minority class with imperfect separability. The disagreement components of XGB are better and more stable than RF with imbalanced samples, especially for complex areas with more types. In addition, appropriate sample imbalance helps to improve the trade-off between the recognition accuracy of XGB and the sample cost. According to our analysis, this margin-based uncertainty assessment and disagreement performance can help users identify the confidence level and error component in similar classification performance (overall, producer, and user accuracies).
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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  • 9
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  Sensors Vol. 20, No. 22 ( 2020-11-23), p. 6699-
    In: Sensors, MDPI AG, Vol. 20, No. 22 ( 2020-11-23), p. 6699-
    Abstract: Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2052857-7
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  • 10
    In: Polymers, MDPI AG, Vol. 13, No. 9 ( 2021-04-21), p. 1350-
    Abstract: Thermosetting organic resins are widely applied as insulating coatings for soft magnetic powder cores (SMPCs) because of their high electrical resistivity. However, their poor thermal stability and thermal decomposition lead to a decrease in electrical resistivity, thus limiting the annealing temperature of SMPCs. The large amount of internal stress generated by soft magnetic composites during pressing must be mitigated at high temperatures; therefore, it is especially important to find organic resins with excellent thermal stabilities. In this study, we prepared SMPCs using poly-silicon-containing arylacetylene resin, an organic resin resistant to high temperatures, as an insulating layer. With 2 wt % PSA as an insulating layer and annealed at 700 °C for 1 h, the FeSiAl SMPCs achieved the best magnetic properties, including the lowest core loss of 184 mW/cm3 (measured at 0.1 T and 50 kHz) and highest permeability of 96.
    Type of Medium: Online Resource
    ISSN: 2073-4360
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527146-5
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