Wetland vegetation is an important component of wetland ecosystems and plays a crucial role in the ecological functions of wetland environments. Accurate distribution mapping and dynamic change ...monitoring of vegetation are essential for wetland conservation and restoration. The development of unoccupied aerial vehicles (UAVs) provides an efficient and economic platform for wetland vegetation classification. In this study, we evaluated the feasibility of RGB imagery obtained from the DJI Mavic Pro for wetland vegetation classification at the species level, with a specific application to Honghu, which is listed as a wetland of international importance. A total of ten object-based image analysis (OBIA) scenarios were designed to assess the contribution of five machine learning algorithms to the classification accuracy, including Bayes, K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and random forest (RF), multi-feature combinations and feature selection implemented by the recursive feature elimination algorithm (RFE). The overall accuracy and kappa coefficient were compared to determine the optimal classification method. The main results are as follows: (1) RF showed the best performance among the five machine learning algorithms, with an overall accuracy of 89.76% and kappa coefficient of 0.88 when using 53 features (including spectral features (RGB bands), height information, vegetation indices, texture features, and geometric features) for wetland vegetation classification. (2) The RF model constructed by only spectral features showed poor classification results, with an overall accuracy of 73.66% and kappa coefficient of 0.70. By adding height information, VIs, texture features, and geometric features to construct the RF model layer by layer, the overall accuracy was improved by 8.78%, 3.41%, 2.93%, and 0.98%, respectively, demonstrating the importance of multi-feature combinations. (3) The contribution of different types of features to the RF model was not equal, and the height information was the most important for wetland vegetation classification, followed by the vegetation indices. (4) The RFE algorithm effectively reduced the number of original features from 53 to 36, generating an optimal feature subset for wetland vegetation classification. The RF based on the feature selection result of RFE (RF-RFE) had the best performance in ten scenarios, and provided an overall accuracy of 90.73%, which was 0.97% higher than the RF without feature selection. The results illustrate that the combination of UAV-based RGB imagery and the OBIA approach provides a straightforward, yet powerful, approach for high-precision wetland vegetation classification at the species level, in spite of limited spectral information. Compared with satellite data or UAVs equipped with other types of sensors, UAVs with RGB cameras are more cost efficient and convenient for wetland vegetation monitoring and mapping.
An overview of osteocalcin progress Li, Jinqiao; Zhang, Hongyu; Yang, Chao ...
Journal of Bone and Mineral Metabolism,
07/2016, Volume:
34, Issue:
4
Journal Article, Book Review
Peer reviewed
An increasing amount of data indicate that osteocalcin is an endocrine hormone which regulates energy metabolism, male fertility and brain development. However, the detailed functions and mechanism ...of osteocalcin are not well understood and conflicting results have been obtained from researchers worldwide. In the present review, we summarize the progress of osteocalcin studies over the past 40 years, focusing on the structure of carboxylated and undercarboxylated osteocalcin, new functions and putative receptors, the role of osteocalcin in bone remodeling, specific expression and regulation in osteoblasts, and new indices for clinical studies. The complexity of osteocalcin in completely, uncompletely and non-carboxylated forms may account for the discrepancies in its tertiary structure and clinical results. Moreover, the extensive expression of osteocalcin and its putative receptor GPRC6A imply that there are new physiological functions and mechanisms of action of osteocalcin to be explored. New discoveries related to osteocalcin function will assist its potential clinical application and physiological theory, but comprehensive investigations are required.
Exploring a method to fabricate robust and stable 3D conductive networks in polymers matrix is still the challenge in the research and development of electromagnetic interference (EMI) shielding ...materials. Here, a feasible approach is provided to produce high‐performance, silicone‐doped MXene EMI shielding composites. The trace amount of hydroxyethyl cellulose is deliberately applied as gels to construct the MXene aerogels with a stable and highly conductive network by the freeze‐drying method. For more desirable mechanical and waterproof properties, the silicone resin is introduced into the MXene aerogels on purpose. The best silicone‐doped MXene EMI shielding composites display a superior electrical conductivity of 3166.4 S m−1, and EMI shield effectiveness of 74.5 dB at the X‐band (8.2–12.4 GHz). It is worth noting that the introduction of silicone resins sharply improves the hydrophobicity of EMI shielding materials to a range of water contact angle of about 151.5°–155.0°. This is a promising method to make MXene‐based EMI shielding composites with self‐cleaning function.
Hydroxyethyl cellulose (HEC) is used as highly efficient gels to produce MXene/HEC mixed aerogels (MHA). The MHA is further doped with silicone resin to form MXene/HEC/silicone resin (MHSi) composites. The MHSi composites with double rough surface have hydrophobicity (water contact angle of about 150.5°–155.0°) and excellent electromagnetic shielding performance (21.0–74.5 dB).
An efficient method has been successfully developed to achieve the asymmetric C–H functionalization of indoles in the carbocyclic ring via organocatalysis, and a variety of tetrahydropyranoindoles ...were synthesized in good yields with excellent stereoselectivities. Further study on thermodynamic calculations indicated that the process was promoted by generating more thermodynamically stable products. This strategy, together with traditional C-3 functionalization of hydroxyindoles, could realize the switchable, regiodivergent asymmetric modification of indoles.
The mechanism of photocatalytic conversion of CO2 and H2O over copper oxide promoted titania, Cu(I)/TiO2, was investigated by means of in situ DRIFT spectroscopy in combination with isotopically ...labeled 13CO2. In addition to small amounts of 13CO, 12CO was demonstrated to be the primary product of the reaction by the 2115 cm−1 Cu(I)−CO signature, indicating that carbon residues on the catalyst surface are involved in reactions with predominantly photocatalytically activated surface adsorbed water. This was confirmed by prolonged exposure of the catalyst to light and water vapor, which significantly reduced the amount of CO formed in a subsequent experiment in the DRIFT cell. In addition, formation of carboxylates and (bi)carbonates was observed by exposure of the Cu(I)/TiO2 surface to CO2 in the dark. These carboxylates and (bi)carbonates decompose upon light irradiation, yielding predominantly CO2. At the same time a novel carbonate species is produced (having a main absorption at ∼1395 cm−1) by adsorption of photocatalytically produced CO on the Cu(I)/TiO2 surface, most likely through a reverse Boudouard reaction of photocatalytically activated CO2 with carbon residues. The finding that carbon residues are involved in photocatalytic water activation and CO2 reduction might have important implications for the rates of artificial photosynthesis reported in many studies in the literature, in particular those using photoactive materials synthesized with carbon containing precursors.
Source rock samples (i.e., drill cuttings) of the Wenchang, Enping, and Zhuhai formations from the Baiyun sag in the deep-water area of the Pearl River Mouth Basin (PRMB) in the South China Sea were ...collected and subjected to organic geochemical analysis. The three sets of potential source rocks were further divided into six types, based on biomarkers and carbon isotopic compositions: shallow lacustrine and semideep–deep lacustrine source rocks of Wenchang Formation, shallow lacustrine and marine transgression-related source rocks of Enping Formation, and littoral and neritic source rocks of Zhuhai Formation. The Wenchang Formation semideep–deep lacustrine source rocks developed in the Baiyun Sag are characterized by low ratio of C30 4-methylsteranes to C29 steranes (4-Me/C29 < 0.25) and relatively 13C-depleted isotopic compositions (δ13Ckerogen < −27.5‰), which are obviously different from those from shallow-water area in the PRMB. Rock–Eval analysis shows the Wenchang Formation develops good source rocks, which are better than those in the Enping Formation. The Zhuhai Formation does not contain effective source rocks because of its low maturity. According to the distribution of bicadinanes and oleanane, it is speculated that shallow lacustrine source rocks of Wenchang Formation in the northern Baiyun Sag have similar high bicadinanes to C30 hopane ((W + T)/C30H) and low oleanane to C30 hopane (OL/C30H) ratios to those of the Enping Formation, indicating their possible contribution to the discovered hydrocarbon accumulations. In summary, hydrocarbons generated by the Wenchang Formation source rocks should become the next key exploration target in the Baiyun Sag.
•Geochemical data obtained from the extracted source rocks are more reliable.•Wenchang semideep–deep lacustrine source rock in Baiyun Sag is newly identified.•Sediment supply direction controls distributions of (W + T) and OL in source rocks.•Hydrocarbons derived from Wenchang Formation are the next key exploration target.
The restoration control of the modern alternating current–direct current (AC–DC) hybrid power grid after a major blackout is difficult and complex. Taking into account the interaction between the ...line‐commutated converter high‐voltage direct current (LCC‐HVDC) and the AC power grid, this paper proposes a novel optimization method of restoration path to reconfigure the skeleton network for the blackout power grid. Based on the system strength, the supporting capability of the AC power grid for the LCC‐HVDC is first analysed from the aspects of start‐up and operation of LCC‐HVDCs. Subsequently, the quantitative relationship between the restoration path and the restoration characteristic of LCC‐HVDC is derived in detail based on the system strength indices of the short‐circuit capacity and the frequency regulation capability. Then, an optimization model of restoration path considering non‐tree paths is formulated and a feasible optimization algorithm is proposed to achieve the optimal path restoration scheme. A modified IEEE 39‐bus system and a partial power grid of Southwest China are simulated to show that the proposed method is suitable for the restoration of AC–DC power grids and can improve restoration efficiency. This research can be an important guidance for operators to rapidly restore the AC–DC power grid.
This paper proposes an optimization method of restoration path to reconfigure the skeleton network for the blackout alternating current–direct current hybrid power grid. In this method, the influence of the restoration path on the system strength is derived in detail, the relationship between restoration path and line‐commutated converter high‐voltage direct current restoration characteristic is quantified, and non‐tree paths are considered to establish an optimization model of restoration path.
This paper implements deep learning methods of recurrent neural networks and short-term memory models. Two kinds of time-series data were used: air pollutant factors, such as O3, SO2, and CO2 from ...2017 to 2019, and meteorological factors such as temperature, humidity, wind direction, and wind speed. A trained model was used to predict air pollution within an eight-hour period. Correlation analysis was applied using Pearson and Spearman correlation coefficients. The KNN method was used to fill in the missing values to improve the generated model’s accuracy. The average absolute error percentage value was used in the experiments to evaluate the model’s performance. LSTM had the lowest RMSE value at 1.9 than the other models from the experiments. CNN had a significant RMSE value at 3.5, followed by Bi-LSTM at 2.5 and Bi-GRU at 2.7. In comparison, the RNN was slightly higher than LSTM at a 2.4 value.
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