Fabrication of Organic Solvent Nanofiltration Membrane through Interfacial Polymerization Using N-Phenylthioure as Monomer for Dimethyl Sulfoxide Recovery

To recover dimethyl sulfoxide, an organic solvent nanofiltration membrane is prepared via the interfacial polymerization method. N-Phenylthiourea (NP)is applied as a water-soluble monomer, reacted with trimesoyl chloride (TMC) on the polyetherimide substrate crosslinked by ethylenediamine. The results of attenuated total reflectance-fourier transform infrared spectroscopy and X-ray electron spectroscopy confirm that N-Phenylthiourea reacts with TMC. The membrane morphology is investigated through atomic force microscopy and scanning electronic microscopy, respectively. The resultant optimized TFC membranes NF-1NP exhibited stable permeance of about 4.3 L m−2 h−1 bar-1 and rejection of 97% for crystal violet (407.98 g mol−1) during a 36 h continuous separation operation. It was also found that the NF-1NP membrane has the highest rejection rate in dimethyl sulfoxide (DMSO), and the rejection rates in methanol, acetone, tetrahydrofuran, ethyl acetate and dimethylacetamide(DMAc) are 51%, 84%, 94%, 96% and 92% respectively. The maximum flux in the methanol system is 11 L m−2 h−1 bar−1, while that in acetone, tetrahydrofuran, ethyl acetate and DMAc is 4.3 L m−2 h−1 bar−1, 6.3 L m−2 h−1 bar−1, 3.2 L m−2 h−1 bar−1, 4.9 L m−2 h−1 bar−1 and 2.1 L m−2 h−1 bar−1, respectively. It was also found that the membrane prepared by N-Phenylthiourea containing aromatic groups has lower mobility and stronger solvent resistance than that of by thiosemicarbazide.</jats:p

A Novel Weakly Supervised Remote Sensing Landslide Semantic Segmentation Method: Combining CAM and cycleGAN Algorithms

With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation. The landslide semantic segmentation of a remote sensing image based on deep learning mainly uses supervised learning, the accuracy of which depends on a large number of training data and high-quality data annotation. At this stage, high-quality data annotation often requires the investment of significant human effort. Therefore, the high cost of remote sensing landslide image data annotation greatly restricts the development of a landslide semantic segmentation algorithm. Aiming to resolve the problem of the high labeling cost of landslide semantic segmentation with a supervised learning method, we proposed a remote sensing landslide semantic segmentation with weakly supervised learning method combing class activation maps (CAMs) and cycle generative adversarial network (cycleGAN). In this method, we used the image level annotation data to replace pixel level annotation data as the training data. Firstly, the CAM method was used to determine the approximate position of the landslide area. Then, the cycleGAN method was used to generate the fake image without a landslide, and to make the difference with the real image to obtain the accurate segmentation of the landslide area. Finally, the pixel-level segmentation of the landslide area on remote sensing image was realized. We used mean intersection-over-union (mIOU) to evaluate the proposed method, and compared it with the method based on CAM, whose mIOU was 0.157, and we obtain better result with mIOU 0.237 on the same test dataset. Furthermore, we made a comparative experiment using the supervised learning method of a u-net network, and the mIOU result was 0.408. The experimental results show that it is feasible to realize landslide semantic segmentation in a remote sensing image by using weakly supervised learning. This method can greatly reduce the workload of data annotation

Cognitive Radar Waveform Selection for Low-Altitude Maneuvering-Target Tracking: A Robust Information-Aided Fusion Method

In this paper, we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform, aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model, combining linear and nonlinear states, is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework, our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm, using the IMCS method for adaptive parameter selection, exhibits high accuracy and robustness in tracking a low-altitude maneuvering target, resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms

Mercury Exposure in Children of the Wanshan Mercury Mining Area, Guizhou, China

To evaluate the mercury (Hg) exposure level of children located in a Hg mining area, total Hg concentrations and speciation were determined in hair and urine samples of children in the Wanshan Hg mining area, Guizhou Province, China. Rice samples consumed by these same children were also collected for total mercury (THg) and methyl-mercury (MeHg) analysis. The geometric mean concentrations of THg and MeHg in the hair samples were 1.4 (range 0.50–6.0) μg/g and 1.1 (range 0.35–4.2) μg/g, respectively, while the geometric mean concentration of urine Hg (UHg) was 1.4 (range 0.09–26) μg/g Creatinine (Cr). The average of the probable daily intake (PDI) of MeHg via rice consumption was 0.052 (0.0033–0.39) µg/kg/day, which significantly correlated with the hair MeHg concentrations (r = 0.55, p &lt; 0.01), indicating that ingestion of rice is the main pathway of MeHg exposure for children in this area. Furthermore, 18% (26/141) of the PDIs of MeHg exceeded the USEPA Reference Dose (RfD) of 0.10 µg/kg/day, indicating that children in this area are at a high MeHg exposure level. This paper for the first time evaluates the co-exposure levels of IHg and MeHg of children living in Wanshan mining area, and revealed the difference in exposure patterns between children and adults in this area