W1-Net: a highly scalable ptychography convolutional neural network

X-ray ptychography is a coherent diffraction imaging technique that allows for the quantitative retrieval of both the amplitude and phase information of a sample in diffraction-limited resolution. However, traditional reconstruction algorithms require a large number of iterations to obtain phase and amplitude images exactly, and the expensive computation precludes real-time imaging. To solve the inverse problem of ptychography data, PtychoNN uses deep convolutional neural networks for real-time imaging. However, its model is relatively simple, and its accuracy is limited by the size of the training dataset, resulting in lower robustness. To address this problem, a series of W-Net neural network models have been proposed which can robustly reconstruct the object phase information from the raw data. Numerical experiments demonstrate that our neural network exhibits better robustness, superior reconstruction capabilities and shorter training time with high-precision ptychography imaging

Transmission of sodium chloride in PDMS membrane during Pervaporation based on polymer relaxation

Polydimethylsiloxane (PDMS) composite membrane is used for treating pharmaceutical wastewater containing NaCl and solvent. In this study, the influence of feed concentrations of NaCl and isobutanol, process temperature and membrane microstructures on salt rejection are evaluated. Microstructures of PDMS membrane before and after separation are characterized by nuclear magnetic resonance (NMR), energy dispersive X-ray spectroscopy (EDS), scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and Positron annihilation life-time spectroscopy (PALS). The PV results show that NaCl will not spontaneously enter PDMS membrane without isobutanol. However, while NaCl feed concentration is 13 wt%, salt rejection of PDMS membrane drops from 100% to 99.09% with increasing feed concentration of isobutanol (up to 7 wt%). On the contrary, a higher temperature increases salt rejection of PDMS membrane and NaCl permeation through PDMS membrane is not through a vapor permeate process. Due to the relaxation of PDMS polymer chain, when PDMS cross-linking ratio is 0.1, the salt rejection increases from 99.87% to 100% with its thickness increasing from 10 ?m to 17.5 ?m. While the cross-linking ratio rises to 0.2, the salt rejection is 100% with the PDMS layer thickness of 10 ?m. The relationship between relaxation of polymer chains and transport of NaCl in PDMS membrane is an excellent guidance and will be beneficial for the treatment of saline organic wastewater

A new method for the extraction of tailing ponds from very high-resolution remotely sensed images: PSVED

Automatic extraction of tailing ponds from Very High-Resolution (VHR) remotely sensed images is vital for mineral resource management. This study proposes a Pseudo-Siamese Visual Geometry Group Encoder-Decoder network (PSVED) to achieve high accuracy tailing ponds extraction from VHR images. First, handcrafted feature (HCF) images are calculated from VHR images based on the index calculation algorithm, highlighting the tailing ponds’ signals. Second, considering the information gap between VHR images and HCF images, the Pseudo-Siamese Visual Geometry Group (Pseudo-Siamese VGG) is utilized to extract independent and representative deep semantic features from VHR images and HCF images, respectively. Third, the deep supervision mechanism is attached to handle the optimization problem of gradients vanishing or exploding. A self-made tailing ponds extraction dataset (TPSet) produced with the Gaofen-6 images of part of Hebei province, China, was employed to conduct experiments. The results show that the proposed method achieves the best visual performance and accuracy for tailing ponds extraction in all the tested methods, whereas the running time of the proposed method maintains at the same level as other methods. This study has practical significance in automatically extracting tailing ponds from VHR images which is beneficial to tailing ponds management and monitoring

Multi-Level Difference Network for Change Detection from Very High-Resolution Remote Sensing Images: A Case Study in Open-Pit Mines

Automatic change detection based on remote sensing is playing an increasingly important role in the national economy construction. To address the problem of limited change detection accuracy in existing single-level difference networks, this study proposes the Multi-level Difference Network (MDNet) for automatic change detection of ground targets from very high-resolution (VHR) remote sensing images. An early-difference network and a late-difference network are combined by MDNet to extract multi-level change features. The early-difference network can focus on change information throughout to reduce the spurious changes in the change detection results, and the late-difference network can provide deep features of a single image for reducing rough boundaries and scattered holes in the change detection results, thus improving the accuracy. However, not all high-level features extracted by MDNet contribute to the recognition of image differences, and the multi-level change features suffer from cross-channel heterogeneity. Stacking them directly on channels does not make effective use of change information, thus limiting the performance of MDNet. Therefore, the Multi-level Change Features Fusion Module (MCFFM) is proposed in this study for the effective fusion of multi-level change features. In the experiments, the publicly available open-pit mine change detection (OMCD) dataset was used first to achieve a change detection of open-pit mines over a large area, with an F1-score of 89.2%, increasing by 1.3% to 5.9% compared to the benchmark methods. Then, a self-made OMCD dataset was used to achieve an F1-score of 92.8% for the localized and fine-scale change detection in open-pit mines, which is an improvement of 0.7% to 5.4% compared to the benchmark methods. Finally, the Season-varying Change Detection Dataset is used to verify that the MDNet proposed can detect changes in other scenarios very well. The experimental results show that the proposed MDNet has significantly improved the performance of change detection on the three datasets compared with six advanced deep learning models, which will contribute to the development of change detection with VHR remote sensing images

Evaluating the Impact of Human Activities on Vegetation Restoration in Mining Areas Based on the GTWR

The clarification of the impact of human activities on vegetation in mining areas contributes to the harmonization of mining and environmental protection. This study utilized Geographically and Temporally Weighted Regression (GTWR) to establish a quantitative relationship among the Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and Digital Elevation Model (DEM). Furthermore, residual analysis was performed to remove the impact of natural factors and separately assess the impact of human activities on vegetation restoration. The experiment was carried out in Shangwan Mine, China, and following results were obtained: (1) During the period of 2000 to 2020, intensified huan activities corresponded to positive vegetation changes (NDVI-HA) that exhibited an upward trend over time. (2) The spatial heterogeneity of vegetation restoration was attributed to the DEM. It is negatively correlated with NDVI in natural conditions, while under the environment of mining activities, there is a positive correlation between NDVI-HA and DEM. (3) The contribution of human activities to vegetation restoration in mining areas has been steadily increasing, surpassing the influences of temperature and precipitation since 2010. The results of this study can provide important references for the assessment of vegetation restoration to some extent in mining areas

Transmission of butanol isomers in pervaporation based on series resistance model

Pervaporation (PV) has shown great potential in the separation of butanol aqueous solutions due to their economic and environmental benefits. This work applies polydimethylsiloxane (PDMS) composite membrane to separate four butanol isomers (n-butanol, isobutanol, sec-butanol and tert-butanol) in aqueous solution. Based on physical and chemical properties of butanol isomers, such as solubility, polarity and interaction parameter, we systematically study the transmission difference in the pervaporation process. The influence of feed concentration, temperature and permeate pressure on membrane performance of PDMS composite membrane are investigated. The results show that the contact angles of butanol isomers on the PDMS layer are 51°, 42.7°, 37.7°, 29.1° and the fluxes at 40 °C are 237.6 g m-2 h-1, 245.4 g m-2 h-1, 224.1 g m-2 h-1, 169.4 g m-2 h-1 for n-butanol, isobutanol, sec-butanol, and tert-butanol, respectively. Moreover, the similar compatibility principle is introduced to the series resistance model so the process simulation matches well with the antagonistic effect of water molecules on mass transfer of butanol isomers. The permeation activation energy is negative, indicating that the dissolution dominates the dissolution and diffusion process. In addition, low vacuum is not conducive to the separation of n-butanol from water. The research on isomers separation through pervaporation may pave a way to separate other solvents of similar properties