MOFs-Derived Zn-Based Catalysts in Acetylene Acetoxylation

Metal–organic frameworks (MOFs)-derived materials with a large specific surface area and rich pore structures are favorable for catalytic performance. In this work, MOFs are successfully prepared. Through pyrolysis of MOFs under nitrogen gas, zinc-based catalysts with different active sites for acetylene acetoxylation are obtained. The influence of the oxygen atom, nitrogen atom, and coexistence of oxygen and nitrogen atoms on the structure and catalytic performance of MOFs-derived catalysts was investigated. According to the results, the catalysts with different catalytic activity are Zn-O-C (33%), Zn-O/N-C (27%), and Zn-N-C (12%). From the measurements of X-ray photoelectron spectroscopy (XPS), it can be confirmed that the formation of different active sites affects the electron cloud density of zinc. The electron cloud density of zinc affects the ability to attract CH3COOH, which makes catalysts different in terms of catalytic activity.

Performance Study of Zn-Co-Ni/AC Catalyst in Acetylene Acetylation

Zinc acetate (Zn(OAc)2) loaded on activated carbon (AC) is the most commonly used catalyst for the industrial synthesis of vinyl acetate (VAc) using the acetylene method. The aim of this study is to optimize the Zn(OAc)2/AC catalyst by adding co-catalysts to improve its activity and stability. Ternary catalysts were synthesized by adding Co and Ni to the Zn(OAc)2/AC catalyst (Zn-Co-Ni/AC). Due to the strong synergistic effect among promoter Co, Ni, and the active component of Zn(OAc)2, the resulting catalyst is capable to absorb more acetic acid and less acetylene. The stability and activity of Zn-Co-Ni/AC catalyst have been improved through electron transfer to alter the electron cloud density around the Zn element. Under the same reaction conditions, the activity of Zn-Co-Ni/AC catalyst was enhanced by 83% compared to that of Zn(OAc)2/AC, and the activity was still as high as 30.1% after 120 h of testing.

EXPLORING CROSS-CITY SEMANTIC SEGMENTATION OF ALS POINT CLOUDS

Deep learning models achieve excellent semantic segmentation results for airborne laser scanning (ALS) point clouds, if sufficient training data are provided. Increasing amounts of annotated data are becoming publicly available thanks to contributors from all over the world. However, models trained on a specific dataset typically exhibit poor performance on other datasets. I.e., there are significant domain shifts, as data captured in different environments or by distinct sensors have different distributions. In this work, we study this domain shift and potential strategies to mitigate it, using two popular ALS datasets: the ISPRS Vaihingen benchmark from Germany and the LASDU benchmark from China. We compare different training strategies for cross-city ALS point cloud semantic segmentation. In our experiments, we analyse three factors that may lead to domain shift and affect the learning: point cloud density, LiDAR intensity, and the role of data augmentation. Moreover, we evaluate a well-known standard method of domain adaptation, deep CORAL (Sun and Saenko, 2016). In our experiments, adapting the point cloud density and appropriate data augmentation both help to reduce the domain gap and improve segmentation accuracy. On the contrary, intensity features can bring an improvement within a dataset, but deteriorate the generalisation across datasets. Deep CORAL does not further improve the accuracy over the simple adaptation of density and data augmentation, although it can mitigate the impact of improperly chosen point density, intensity features, and further dataset biases like lack of diversity.

Changes in subsidence and uplift and the nighttime land surface temperature anomaly related the distance to the earthquake epicenter and the faults using Sentinel and MODIS imageries

When an earthquake occurs, the faults of the region usually heat the rocks and soil of the region due to their movements. The purpose of this study was to analyze the uplift, subsidence, cloud cover and changes in nighttime land surface temperature (nLST) anomalies around faults and the earthquake epicenter in Kermanshah, Iran (date and time of earthquake 12 November, 2017 at 18:18 Coordinated Universal Time (UTC) and at 21:48 Iranian time(. Heat changes were investigated by considering the effect of other cooling factors such as vegetation (EVI), land altitude and soil moisture, rainfall and water areas. Using the MODIS sensor product, the amount of cloud cover and cooling factors were obtained. Using sentinel 1A the amount of earth uplift and subsidence were calculated. The results showed that using statistical analysis, a significant difference was observed in the nighttime land surface temperature around the faults and around the uplift and subsidence on the night of the accident, before and after night of earthquake. However, there was no significant difference between nighttime temperature and changes in the rate of spatial variation of cooling factors. It was found that the earthquake caused an increase in temperature at the fault and earthquake epicenter location. It also causes changes in height such as uplift and subsidence. Cloud cover situation showed before the earthquake, the cloud density was high and after the earthquake, the cloud density decreased. Crises managers can consider these results for monitoring metropolices for more readiness before earthquake accordance.

A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

Tree Height Measurements in Degraded Tropical Forests Based on UAV-LiDAR Data of Different Point Cloud Densities: A Case Study on Dacrydium pierrei in China

Tropical forest degradation is a major contributor to greenhouse gas emissions. Tree height can be used as an important predictor of forest growth, and yield models can provide basic data for forest degradation assessments. As an important parameter of unmanned aerial vehicle-light detection and ranging (UAV-LiDAR), it is not clear how the point cloud density affects the extraction accuracy of tree height in degraded tropical rain forests. To solve this problem, we collected UAV-LiDAR data at a flight altitude of 150 m, and then resampled the UAV-LiDAR data obtained according to the point cloud density percentage resampling method and obtained UAV-LiDAR data for five different point cloud densities, namely, 12, 17, 28, 64, and 108 points/m2. On the basis of the resampled LiDAR data, we generated a canopy height model (CHM) to extract the height of Dacrydium pierrei (D. pierrei). The results show that (1) With the increase in the point cloud density, the accuracy of tree height extraction gradually increased, with a maximum accuracy at 108 points/m2 (root mean squared error (RMSE)% = 22.78%, bias% = 14.86%). The accuracy (RMSE%) increased by 6.92% as the point cloud density increased from 12 points/m2 to 17 points/m2, but only increased by 0.99% as the point cloud density increased from 17 points/m2 to 108 points/m2, indicating that 17 points/m2 is a critical point for tree height extraction of D. pierrei. (2) Compared with the results from broad-leaved forests, the accuracy of D. pierrei height extraction from coniferous forest was higher. With the increase in point cloud density, the difference in the accuracy of D. pierrei height between two stands gradually increased. When the point cloud density was 108 points/m2, the differences in RMSE% and bas% were 3.55% and 6.22%, respectively. When the point cloud density was 12 points/m2, the differences in RMSE% and bias% were 2.71% and 4.69%, respectively. Our research identified the lowest LiDAR data point cloud density required to ensure a certain accuracy in tree height extraction, which will help scholars formulate UAV-LiDAR forest resource survey plans.

3D Change Detection Using Adaptive Thresholds Based on Local Point Cloud Density

In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clouds, an approach for 3D change detection using point-based comparison is presented in this paper. To avoid density variation in point clouds, adaptive thresholds are calculated through the k-neighboring average distance and the local point cloud density. A series of experiments for quantitative evaluation is performed. In the experiments, the influencing factors including threshold, registration error, and neighboring number of 3D change detection are discussed and analyzed. The results of the experiments demonstrate that the approach using adaptive thresholds based on local point cloud density are effective and suitable.

Progress in preparation and characterization of silylene iron, cobalt and nickel complexes

The synthesis and characterization of Fe, Co and Ni complexes supported by silylene ligands in recent ten years are summarized. Due to the decrease of electron cloud density on Si...

Synthesis of butadiene/isoprene–styrene di-block copolymer with high cis-1,4 unit content based on a neodymium phosphate ester

A novel synthesis strategy of butadiene/isoprene–styrene di-block copolymer with high cis-1,4 content was explored based on a neodymium phosphate catalyst, by adjusting the electron cloud density of the catalytic active center via triphenyl phosphine.