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  • MDPI AG  (7)
  • 1
    Online Resource
    Online Resource
    Meteorological-Data-Driven Rubber Tree Powdery Mildew Model and Its Application on Spatiotemporal Patterns: A Case Study of Hainan Island
    MDPI AG ; 2023
    In:  Sustainability Vol. 15, No. 16 ( 2023-08-08), p. 12119-
    Details
    In: Sustainability, MDPI AG, Vol. 15, No. 16 ( 2023-08-08), p. 12119-
    Abstract: Given that rubber is an important strategic material and the prevalence of rubber tree powdery mildew (RTPM) is a serious issue, the study of RTPM is becoming increasingly significant in aiding our understanding and managing rubber plantations. By enhancing our understanding, we may improve both the yield and quality of the rubber produced. Using meteorological station and reanalysis data, we employed factor expansion and three different feature-selection methods to screen for significant meteorological factors, ultimately constructing a data-driven RTPM disease index (RTPM-DI) model. This model was then used to analyze the spatiotemporal distribution of RTPM-DI in Hainan Island from 1980 to 2018, to reproduce and explore its patterns. The results show that (1) the RTPM-DI is dominantly negatively influenced by the average wind speed and positively affected by days with moderate rain; (2) the average wind speed and the days with moderate rain could explain 71% of the interannual variations in RTPM-DI, and a model established on the basis of these can simulate the changing RTPM-DI pattern very well (RMSE = 8.2511, MAE = 6.7765, MAPE = 0.2486, KGE = 0.9921, MSE = 68.081, RMSLE = 0.0953); (3) the model simulation revealed that during the period from 1980 to 2018, oscillating cold spots accounted for 72% of the whole area of Hainan Island, indicating a declining trend in RTPM-DI in the middle, western, southwestern, and northwestern regions. Conversely, new hot-spots and oscillating hot-spots accounted for 1% and 6% of the entire island, respectively, demonstrating an upward trend in the southeastern and northern regions. Additionally, no discernible pattern was observed for 21% of the island, encompassing the southern, eastern, and northeastern regions. It is evident that the whole island displayed significant spatial differences in the RTPM-DI pattern. The RTPM-DI model constructed in this study enhances our understanding of how climate change impacts RTPM, and it provides a useful tool for investigating the formation mechanism and control strategies of RTPM in greater depth.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su151612119
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2518383-7
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  • 2
    Online Resource
    Online Resource
    Rare Carbon-Bridged Citrinin Dimers from the Starfish-Derived Symbiotic Fungus Penicillium sp. GGF16-1-2
    MDPI AG ; 2022
    In:  Marine Drugs Vol. 20, No. 7 ( 2022-07-06), p. 443-
    Details
    In: Marine Drugs, MDPI AG, Vol. 20, No. 7 ( 2022-07-06), p. 443-
    Abstract: Four novel, rare carbon-bridged citrinin dimers, namely dicitrinones G–J (1–4), and five known analogs (5–9) were isolated from the starfish-derived fungus Penicillium sp. GGF 16-1-2. Their structures were elucidated by extensive spectroscopic analysis and quantum chemical calculations. Compounds 1–9 exhibited strong antifungal activities against Colletotrichum gloeosporioides with LD50 values from 0.61 μg/mL to 16.14 μg/mL. Meanwhile, all compounds were evaluated for their cytotoxic activities against human pancreatic cancer BXPC-3 and PANC-1 cell lines; as a result, compound 1 showed more significant cytotoxicities than the positive control against both cell lines. In addition, based on the analyses of the protein-protein interaction (PPI) network and Western blot, 1 could induce apoptosis by activating caspase 3 proteins (CASP3).
    Type of Medium: Online Resource
    ISSN: 1660-3397
    URL: Article
    DOI: 10.3390/md20070443
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2175190-0
    SSG: 15,3
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  • 3
    Online Resource
    Online Resource
    Microstructure and Impact Toughness of Local-Dry Keyhole Tungsten Inert Gas Welded Joints
    MDPI AG ; 2019
    In:  Materials Vol. 12, No. 10 ( 2019-05-20), p. 1638-
    Details
    In: Materials, MDPI AG, Vol. 12, No. 10 ( 2019-05-20), p. 1638-
    Abstract: In this paper, the microstructure and impact toughness of a S32101 duplex stainless steel underwater local-dry keyhole tungsten inert gas welded joint were studied. The impact toughness value of the underwater weld metal reached 78% of the onshore weld metal, which is in accordance with the underwater welding standards. The proportion of austenite in the underwater weld metal was 0.9% lower than that of the onshore weld metal. The proportion of the Σ3 coincidence site lattice boundaries and random phase boundaries in the underwater weld metal, which significantly influence the impact toughness of the weld metal, were smaller than that of the onshore weld metal.
    Type of Medium: Online Resource
    ISSN: 1996-1944
    URL: Article
    DOI: 10.3390/ma12101638
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2487261-1
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  • 4
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    Online Resource
    CGR-Block: Correlated Feature Extractor and Geometric Feature Fusion for Point Cloud Analysis
    MDPI AG ; 2022
    In:  Sensors Vol. 22, No. 13 ( 2022-06-28), p. 4878-
    Details
    In: Sensors, MDPI AG, Vol. 22, No. 13 ( 2022-06-28), p. 4878-
    Abstract: Point cloud processing based on deep learning is developing rapidly. However, previous networks failed to simultaneously extract inter-feature interaction and geometric information. In this paper, we propose a novel point cloud analysis module, CGR-block, which mainly uses two units to learn point cloud features: correlated feature extractor and geometric feature fusion. CGR-block provides an efficient method for extracting geometric pattern tokens and deep information interaction of point features on disordered 3D point clouds. In addition, we also introduce a residual mapping branch inside each CGR-block module for the further improvement of the network performance. We construct our classification and segmentation network with CGR-block as the basic module to extract features hierarchically from the original point cloud. The overall accuracy of our network on the ModelNet40 and ScanObjectNN benchmarks achieves 94.1% and 83.5%, respectively, and the instance mIoU on the ShapeNet-Part benchmark also achieves 85.5%, proving the superiority of our method.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s22134878
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2052857-7
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  • 5
    Online Resource
    Online Resource
    Racemic Norlignans as Diastereoisomers from Ferula sinkiangensis Resins with Antitumor and Wound-Healing Promotion Activities
    MDPI AG ; 2022
    In:  Molecules Vol. 27, No. 12 ( 2022-06-17), p. 3907-
    Details
    In: Molecules, MDPI AG, Vol. 27, No. 12 ( 2022-06-17), p. 3907-
    Abstract: Ferulasinkins A–D (1–4), four new norlignans, were isolated from the resins of Ferula sinkiangensis, a medicinal plant of the Apiaceae family. All of them were obtained as racemic mixtures, chiral HPLC was used to produce their (+)- and (−)-antipodes. The structures of these new compounds, including their absolute configurations, were elucidated by spectroscopic and computational methods. This isolation provides new insight into the chemical profiling of F. sinkiangensis resins beyond the well-investigated structure types such as sesquiterpene coumarins and disulfides. Compounds 2a and 3a were found to significantly inhibit the invasion and migration of triple-negative breast cancer (TNBC) cell lines via CCK-8 assay. On the other hand, the wound-healing assay also demonstrated that compounds 4a and 4b could promote the proliferation of human umbilical vein endothelial cells (HUVECs). Notably, the promoting effects of 4a and 4b were observed as more significant versus a positive control using basic fibroblast growth factor (bFGF).
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules27123907
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2008644-1
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  • 6
    Online Resource
    Online Resource
    A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs
    MDPI AG ; 2023
    In:  Sensors Vol. 23, No. 8 ( 2023-04-10), p. 3847-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 8 ( 2023-04-10), p. 3847-
    Abstract: Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future goal of intelligent roads. Drones have emerged as a promising application area of intelligent technology in this field. They can help achieve fast and precise detection, classification, and localization of infrastructure along highways, which can significantly enhance efficiency and ease the burden on road management staff. As the infrastructure along the road is exposed to the outdoors for a long time, it is easily damaged and obscured by objects such as sand and rocks; on the other hand, based on the high resolution of the images taken by Unmanned Aerial Vehicles (UAVs), the variable shooting angles, complex backgrounds, and high percentage of small targets mean the direct use of existing target detection models cannot meet the requirements of practical applications in industry. In addition, there is a lack of large and comprehensive image datasets of infrastructure along highways from UAVs. Based on this, a multi-classification infrastructure detection model combining multi-scale feature fusion and an attention mechanism is proposed. In this paper, the backbone network of the CenterNet model is replaced with ResNet50, and the improved feature fusion part enables the model to generate fine-grained features to improve the detection of small targets; furthermore, the attention mechanism is added to make the network focus more on valuable regions with higher attention weights. As there is no publicly available dataset of infrastructure along highways captured by UAVs, we filter and manually annotate the laboratory-captured highway dataset to generate a highway infrastructure dataset. The experimental results show that the model has a mean Average Precision (mAP) of 86.7%, an improvement of 3.1 percentage points over the baseline model, and the new model performs significantly better than other detection models overall.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23083847
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2052857-7
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  • 7
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    Online Resource
    The Influence of Stand Structure on Understory Herbaceous Plants Species Diversity of Platycladus orientalis Plantations in Beijing, China
    MDPI AG ; 2022
    In:  Forests Vol. 13, No. 11 ( 2022-11-15), p. 1921-
    Details
    In: Forests, MDPI AG, Vol. 13, No. 11 ( 2022-11-15), p. 1921-
    Abstract: Species diversity is a crucial index used to evaluate the stability and complexity of forest ecosystems. Studying the relationship between stand structure and understory herbaceous plants species diversity is useful for managers to formulate the best forest structure optimization method with the goal of improving herbaceous species diversity. In this research, Platycladus orientalis plantations in Beijing were taken as the research object. Pearson’s correlation analysis was used to explore the single-factor correlation between stand structure and understory herbaceous plants species diversity; furthermore, a typical correlation analysis and multiple linear regression were used to explore the multi-factor correlation and analyze the dominant stand structure parameters affecting understory herbaceous plants species diversity. In the range of stand structures studied, the results showed that canopy density was negatively correlated with the Shannon–Wiener index and Simpson index (p 〈 0.01), and tree density was negatively correlated with the Shannon–Wiener index (p 〈 0.05). In terms of stand spatial structure, the mingling degree was positively correlated with the Shannon–Wiener index, Simpson index, Margalef richness index and Pielou evenness index (p 〈 0.05), while the uniform angle was negatively correlated with the Pielou evenness index (p 〈 0.05). The correlation coefficient of the first group of typical variables in the typical correlation analysis was 0.90 (p 〈 0.05); from this group of typical variables, it can be concluded that canopy density is the most influential indicator affecting the comprehensive index of understory herbaceous plants species diversity, with a load of −0.690, and the Shannon–Wiener index and Simpson index are the most responsive indicators of changes in the comprehensive index of stand structure, with loads of 0.871 and 0.801, respectively. In the process of the management of Platycladus orientalis plantations under a low altitude, south slope, thin soil layer and hard soil parent material, in order to improve the herbaceous species diversity, the canopy density of the overstory and tree density should be appropriately reduced. Additionally, it is necessary to regulate the horizontal spatial structure of stands. When the trees are randomly distributed and the mingling degree is high, the species diversity of herbs can be increased.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    URL: Article
    DOI: 10.3390/f13111921
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527081-3
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