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  • 1
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    Online Resource
    Feeding Fungal-Pretreated Corn Straw Improves Health and Meat Quality of Lambs Infected with Gastrointestinal Nematodes
    MDPI AG ; 2020
    In:  Animals Vol. 10, No. 9 ( 2020-09-16), p. 1659-
    Details
    In: Animals, MDPI AG, Vol. 10, No. 9 ( 2020-09-16), p. 1659-
    Abstract: Infections with gastrointestinal nematodes (GIN) adversely affect meat color in lambs. Although white-rot fungi (WRF) pretreatment increases nutritional value and fiber digestion of corn straw for lambs, whether it can improve meat quality of lambs infected with GINs is unknown. The objective of this experiment was to study effects of feeding WRF-pretreated corn straw on the health and meat quality of lambs infected with GINs. Sixteen healthy Ujumqin lambs were orally drenched with 3rd-stage GINs larvae and randomly divided into two dietary treatments of control (CON) and WRF diets for 70 days of feeding. Results showed that feeding WRF-pretreated corn straw decreased L* and b* values (p 〈 0.05) and increased a* value (p 〈 0.01) of both longissimus thoracis et lumborum (LTL) and semimembranosus (SM) muscles of lambs infected with GINs. Feeding WRF-pretreatment corn straw decreased fecal egg count (p = 0.014) and increased packed cell volume (p = 0.013) of lambs from 28 d of feeding and increased plasma iron content (p = 0.008) of lambs from 56 d of the feeding. Feeding WRF-pretreatment corn straw decreased myosin heavy-chain (MyHC)-I (p = 0.032) and MyHC-IIα (p = 0.025) content in LTL muscle and MyHC-I (p = 0.022) and MyHC-IIβ (p = 0.048) in SM muscle of lambs. In conclusion, although there were no significant changes in the content of most amino acids or increased intensity of better flavor compounds, meat quality and health of lambs infected with GINs was significantly improved by feeding WRF-pretreated corn straw due to increased PCV and meat color and tenderness.
    Type of Medium: Online Resource
    ISSN: 2076-2615
    URL: Article
    DOI: 10.3390/ani10091659
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2606558-7
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  • 2
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    Chemical Composition in Kernels of Ten Grafted Pecan (Carya illinoensis) Varieties in Southeastern China
    MDPI AG ; 2022
    In:  Sci Vol. 4, No. 2 ( 2022-06-10), p. 25-
    Details
    In: Sci, MDPI AG, Vol. 4, No. 2 ( 2022-06-10), p. 25-
    Abstract: As woody oil crop, pecan [Carya illinoinensis (Wangenh.) K. Koch] may be a solution to the shortage of edible oil in the future. In this study, fruit traits, kernel nutrition and fatty acid composition of 10 pecan varieties were determined to assess the pote ntial of pecans for exploitation as edible oil, as well as to further screen varieties that could be used as edible oil resources and to understand their development prospects for cultivation in mountainous hills. The study showed that all the fruit trait indicators measured, including green-fruit weight (mean 28.47 g), nut weight (10.33 g), kernel weight (5.25 g), nut percentage (36.83%) and kernel percentage (50.50%), showed highly significant differences among the 10 varieties. Among the main nutritional indicators of the kernels, the crude fat content was stable (mean 70.01%) with non-significant differences, while protein (67.50 mg·g−1), soluble sugar (10.7 mg·g−1) and tannin (6.07 mg·g−1) showed highly significant differences between varieties. The oil percentage of nuts (kernel percentage * crude fat) averaged 35.36%, with highly significant differences between varieties. The fatty acid composition was dominated by unsaturated fatty acids (mean 91.82%), with unsaturated fatty acids being 11.24 times more abundant than saturated fatty acids. Among the monounsaturated fatty acids, oleic acid was the highest (mean 70.02%), with highly significant differences between varieties, followed by cis-11-eicosanoic acid (0.25%), with non-significant differences between varieties; among the polyunsaturated fatty acids, linoleic acid was the highest (19.58%), followed by linolenic acid (0.97%), both of which showed highly significant differences between varieties; monounsaturated fatty acids were 2.42 times more abundant than polyunsaturated fatty acids. Compared to other oilseed crops, pecan has the potential to produce “nutritious, healthy and stable” edible oil, while its wide habitat and good productivity benefits offer broad prospects for development in the hills and mountains of subtropical China.
    Type of Medium: Online Resource
    ISSN: 2413-4155
    URL: Article
    DOI: 10.3390/sci4020025
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2963087-3
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  • 3
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    Research Progress in the Synthesis of Carbon Dots and Their Application in Food Analysis
    MDPI AG ; 2022
    In:  Biosensors Vol. 12, No. 12 ( 2022-12-12), p. 1158-
    Details
    In: Biosensors, MDPI AG, Vol. 12, No. 12 ( 2022-12-12), p. 1158-
    Abstract: Food safety is connected to public health, making it crucial to protecting people’s health. Food analysis and detection can assure food quality and effectively reduce the entry of harmful foods into the market. Carbon dots (CDs) are an excellent choice for food analysis and detection attributable to their advantages of good optical properties, water solubility, high chemical stability, easy functionalization, excellent bleaching resistance, low toxicity, and good biocompatibility. This paper focuses on the optical properties, synthesis methods, and applications of CDs in food analysis and detection, including the recent advances in food nutritional composition analysis and food quality detection, such as food additives, heavy metal ions, foodborne pathogens, harmful organic pollutants, and pH value. Moreover, this review also discusses the potentially toxic effects, current challenges, and prospects of CDs in basic research and applications. We hope that this review can provide valuable information to lay a foundation for subsequent research on CDs and promote the exploration of CDs-based sensing for future food detection.
    Type of Medium: Online Resource
    ISSN: 2079-6374
    URL: Article
    DOI: 10.3390/bios12121158
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662125-3
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  • 4
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    A Novel Electro-Thermal Model of Lithium-Ion Batteries Using Power as the Input
    MDPI AG ; 2021
    In:  Electronics Vol. 10, No. 22 ( 2021-11-11), p. 2753-
    Details
    In: Electronics, MDPI AG, Vol. 10, No. 22 ( 2021-11-11), p. 2753-
    Abstract: Considering that use of measured current as input of a battery model may cause distortion of the model due to low accuracy of the on-board current sensor and that power can be used to indicate energy transmission in an electric vehicle model, the power input internal resistance model is widely used in simulation of whole electric vehicles. However, since no consideration is given to battery polarization and electro-thermal coupling characteristics, the foregoing model cannot be used to describe the internal temperature change of batteries under working conditions. Three contributions are made in the present study: (1) ternary lithium-ion batteries were taken as the research objects and a second-order RC equivalent circuit model with power as the input was established in the present study; (2) A dynamic heat generation rate model suitable for RC equivalent circuits was built based on coupled electrical and thermal characteristics of lithium-ion batteries; (3) An electric model and a two-state equivalent thermal network model were further built and combined by using the heat generation rate model to form a power input electro-thermal model. Parameters of the model so formed were identified offline, and the battery model was verified with respect to accuracy under seven working conditions. The results show that the maximum root mean square error in voltage estimation, current estimation, and surface temperature estimation is 19.38 mV, 9.51 mA, and 0.19 °C respectively, which indicates that the power input electro-thermal model can describe the electrical and thermal dynamic behavior of batteries more accurately and comprehensively than the traditional power input internal resistance model.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics10222753
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662127-7
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  • 5
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    Self-Paced Convolutional Neural Network for PolSAR Images Classification
    MDPI AG ; 2019
    In:  Remote Sensing Vol. 11, No. 4 ( 2019-02-19), p. 424-
    Details
    In: Remote Sensing, MDPI AG, Vol. 11, No. 4 ( 2019-02-19), p. 424-
    Abstract: Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification. Convolutional neural network can be used to extract the channel-spatial features of PolSAR images. Self-paced learning has been demonstrated to be instrumental in enhancing the learning robustness of convolutional neural network. In this paper, a novel classification method for PolSAR images using self-paced convolutional neural network (SPCNN) is proposed. In our method, each pixel is denoted by a 3-dimensional tensor block formed by its scattering intensity values on four channels, Pauli’s RGB values and its neighborhood information. Then, we train SPCNN to extract the channel-spatial features and obtain the classification results. Inspired by self-paced learning, SPCNN learns the easier samples first and gradually involves more difficult samples into the training process. This learning mechanism can make network converge to better values. The proposed method achieved state-of-the-art performances on four real PolSAR dataset.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs11040424
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
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  • 6
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    Source Model of the 2023 Turkey Earthquake Sequence Imaged by Sentinel-1 and GPS Measurements: Implications for Heterogeneous Fault Behavior along the East Anatolian Fault Zone
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 10 ( 2023-05-18), p. 2618-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 10 ( 2023-05-18), p. 2618-
    Abstract: On 6 February 2023, a devastating doublet of earthquakes with magnitudes of Mw 7.8 and Mw 7.6 successively struck southeastern Turkey near the border of Syria. The earthquake sequence represents the strongest earthquakes in Turkey during the past 80 years and caused an extensive loss of life and property. In this study, we processed Sentinel-1 and GPS data to derive the complete surface displacement caused by the earthquake sequence. The surface displacements were adopted to invert for the fault geometry and coseismic slip distribution on the seismogenic faults of the earthquake sequence. The results indicate that the coseismic rupture of the Turkey earthquake sequence was dominated by left-lateral strike slips with a maximum slip of ~10 m on the East Anatolian Fault Zone (EAFZ) and the Sürgü fault (SF). Significant surface ruptures are recognized based on the geodetic inversion, which is consistent with the analysis of post-earthquake satellite images. The cumulative released moment of the two earthquakes reached 9.62 × 1020 Nm, which corresponds to an event of Mw 7.95. Additionally, the interseismic fault slip rates and locking depths along the central and western segments of the EAFZ were estimated using the high-resolution long-term velocity field. The results reveal significant lateral variations of fault slip rates and locking depths along the central and western segments of the EAFZ. Generally, the estimated fault locking zone showed good spatial consistency with the coseismic fault rupture of the Mw 7.8 shock on the EAFZ. The static coulomb failure stress (CFS) change due to the Mw 7.8 earthquakes suggests that the subsequent Mw 7.6 event was certainly promoted by the Mw 7.8 shock. The stress transfers from the fault EAFZ to the fault SF were realized by unclamping the interface of the fault SF, which significantly reduces the effective normal stress on the fault plane. Large CFS increases in the western Puturge segment of the EAFZ, which was not ruptured in the 2020 Mw 6.8 and the 2023 Mw 7.8 earthquakes, highlight the future earthquake risk in this fault segment.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15102618
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 7
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    Adaptation to Climate Change and its Impacts on Wheat Yield: Perspective of Farmers in Henan of China
    MDPI AG ; 2019
    In:  Sustainability Vol. 11, No. 7 ( 2019-04-01), p. 1928-
    Details
    In: Sustainability, MDPI AG, Vol. 11, No. 7 ( 2019-04-01), p. 1928-
    Abstract: This paper explored farm households’ autonomous climate change adaptation strategies and corresponding impacts on wheat yield. Based on a survey of 314 wheat farmers in rural China, results show that Chinese wheat farmers have a high rate of climate change awareness and adoption of climate change adaptation measures. Farmers’ cultivated area, cognition level and information accessibility on climate change significantly affect their adaptation decisions. However, these farmers are given limited adaptation strategies, mainly including increasing irrigation, and using more chemical fertilizer and pesticides. Through employing a simultaneous equations model with endogenous switching, we find farmers’ adaptation to climate change is maladaptive with negative effects on wheat yield. This study, therefore, suggests policymakers be mindful of farmers’ maladaptive responses to climate change and provide effective adaptation measures, to help farmers cope with the risks of climate change and ensure farmer’s livelihood security and sustainable agriculture development.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su11071928
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
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  • 8
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    Deep USRNet Reconstruction Method Based on Combined Attention Mechanism
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 21 ( 2022-10-30), p. 14151-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 21 ( 2022-10-30), p. 14151-
    Abstract: Single image super-resolution (SISR) based on deep learning is a key research problem in the field of computer vision. However, existing super-resolution reconstruction algorithms often improve the quality of image reconstruction through a single network depth, ignoring the problems of reconstructing image texture structure and easy overfitting of network training. Therefore, this paper proposes a deep unfolding super-resolution network (USRNet) reconstruction method under the integrating channel attention mechanism, which is expected to improve the image resolution and restore the high-frequency information of the image. Thus, the image appears sharper. First, by assigning different weights to features, focusing on more important features and suppressing unimportant features, the details such as image edges and textures are better recovered, and the generalization ability is improved to cope with more complex scenes. Then, the CA (Channel Attention) module is added to USRNet, and the network depth is increased to better express high-frequency features; multi-channel mapping is introduced to extract richer features and enhance the super-resolution reconstruction effect of the model. The experimental results show that the USRNet with integrating channel attention has a faster convergence rate, is not prone to overfitting, and can be converged after 10,000 iterations; the average peak signal-to-noise ratios on the Set5 and Set12 datasets after the side length enlarged by two times are, respectively, 32.23 dB and 29.72 dB, and are dramatically improved compared with SRCNN, SRMD, PAN, and RCAN. The algorithm can generate high-resolution images with clear outlines, and the super-resolution effect is better.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su142114151
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 9
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    Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
    MDPI AG ; 2021
    In:  Remote Sensing Vol. 13, No. 22 ( 2021-11-14), p. 4577-
    Details
    In: Remote Sensing, MDPI AG, Vol. 13, No. 22 ( 2021-11-14), p. 4577-
    Abstract: The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs13224577
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
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  • 10
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    A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 12 ( 2022-06-20), p. 7508-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 12 ( 2022-06-20), p. 7508-
    Abstract: As regional interaction increases in an open economy, a region’s green total factor productivity in agriculture must be considered alongside relationships with other regions. In this study, the slack-based model (SBM) global Malmquist–Luenberger (GML) index is used to measure the green total factor productivity of agriculture in each province of China, and the social network analysis (SNA) and vector autoregressive model (VAR) impulse response function (IRF) are used to examine the spatial network structure and regional interactivity. The research confirms that the absolute value and concentration of agricultural green total factor productivity are generally higher in the south than in the north of China, but the peak is lower in the south than in the north. The network density of agricultural green total factor productivity in China from 2008 to 2019 shows an increase, with the cut-off values of mean, 10, 50, and 100 treated as 4.97%, 2.57%, 3.30%, and 2.43%, respectively. From 2008 to 2019, the central potentials of network entry and network exit of green total factor productivity in China’s agriculture show a “V”-shaped and inverted “V”-shaped evolution path, respectively, with the density of cohesive subgroups growing, which demonstrates that the spatial structure of green total factor productivity in Chinese agriculture has experienced an evolutionary path from polycentric to monocentric to polycentric conditions. The spatial interaction of different cohesive subgroups is intensifying and has a certain degree of self-stability. In terms of regional interaction, the siphon effect of the east on the green development of agriculture in the central and western regions is significant, but the trickle-down effect is not obvious, and the interaction between the central and western regions has a catalytic effect on the efficiency of the green economy of agriculture in both regions. It is recommended that targeted policies be introduced to support the flow of agricultural factors and industrial division of labour between the central and western regions and the south and north, taking into account the actual situation. The novelty of this paper is that it focuses on the green total factor productivity of Chinese agriculture and combines the innovative use of the social network analysis paradigm to analyse the green development of agriculture in a country from a spatial dynamic evolutionary perspective. A limitation of the research methodology in this paper is its poor applicability to closed economy analysis.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su14127508
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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