Karst landslides detection and monitoring with multiple SAR data and multi-dimensional SBAS technique in Shuicheng, Guizhou, China

Shuicheng District is a karst mountain area, located in Guizhou Province, China. Its fragile stratum and frequent underground mining activities makes it prone to landslides. Owning to its wide coverage and frequent revisit, the InSAR technology has advantages in potential landslide identification and deformation monitor. However, affected by dense vegetation and atmospheric delay, it is much difficult to get sufficient effective targets to derive the deformation in this area. Besides, deformation derived from single orbit SAR data can result in the missing identification of some potential landslides and the misinterpreting of the real kinematics process of landslides. In this study, the multi-source SAR data, atmospheric error correction by quadratic tree image segmentation method, and phase-stacking method were selected to derive the surface deformation of this area. Besides, DS-InSAR and MSBAS method were combined to derive the deformation of Pingdi landslide. First, the potential landslides in this area were identified, surface deformation result, optical remote sensing images and geomorphological features were jointly considered. Then, the landslide distribution characteristics was analyzed in terms of slope, elevation and stratum. After that, the deformation along the LOS direction was acquired using the DS-InSAR method. The MSBAS method was used to retrieve the two-dimensional deformation of Pingdi landslide. Finally, the comprehensive analysis of triggering factors and failure process were conducted according to the spatial-temporal deformation characteristics and field investigation. The results indicated that landslides in Shuicheng district were mostly located in the junction of T1 and P3 stratum and mining related. Mining activity was the main cause of the Pingdi landslide deformation, the precipitation was the driving factor of the landslide instability. The research provides an insight into the explore the unstable slope distribution characteristic and the failure process of the landslides

Parallel Multistage Wide Neural Network

Deep learning networks have achieved great success in many areas such as in large scale image processing. They usually need large computing resources and time, and process easy and hard samples inefficiently in the same way. Another undesirable problem is that the network generally needs to be retrained to learn new incoming data. Efforts have been made to reduce the computing resources and realize incremental learning by adjusting architectures, such as scalable effort classifiers, multi-grained cascade forest (gc forest), conditional deep learning (CDL), tree CNN, decision tree structure with knowledge transfer (ERDK), forest of decision trees with RBF networks and knowledge transfer (FDRK). In this paper, a parallel multistage wide neural network (PMWNN) is presented. It is composed of multiple stages to classify different parts of data. First, a wide radial basis function (WRBF) network is designed to learn features efficiently in the wide direction. It can work on both vector and image instances, and be trained fast in one epoch using subsampling and least squares (LS). Secondly, successive stages of WRBF networks are combined to make up the PMWNN. Each stage focuses on the misclassified samples of the previous stage. It can stop growing at an early stage, and a stage can be added incrementally when new training data is acquired. Finally, the stages of the PMWNN can be tested in parallel, thus speeding up the testing process. To sum up, the proposed PMWNN network has the advantages of (1) fast training, (2) optimized computing resources, (3) incremental learning, and (4) parallel testing with stages. The experimental results with the MNIST, a number of large hyperspectral remote sensing data, CVL single digits, SVHN datasets, and audio signal datasets show that the WRBF and PMWNN have the competitive accuracy compared to learning models such as stacked auto encoders, deep belief nets, SVM, MLP, LeNet-5, RBF network, recently proposed CDL, broad learning, gc forest etc. In fact, the PMWNN has often the best classification performance

Pathologically Activated Neuroprotection via Uncompetitive Blockade of \u3cem\u3eN\u3c/em\u3e-Methyl-d-aspartate Receptors with Fast Off-rate by Novel Multifunctional Dimer Bis(propyl)-cognitin

Uncompetitive N-methyl-d-aspartate (NMDA) receptor antagonists with fast off-rate (UFO) may represent promising drug candidates for various neurodegenerative disorders. In this study, we report that bis(propyl)-cognitin, a novel dimeric acetylcholinesterase inhibitor and γ-aminobutyric acid subtype A receptor antagonist, is such an antagonist of NMDA receptors. In cultured rat hippocampal neurons, we demonstrated that bis(propyl)-cognitin voltage-dependently, selectively, and moderately inhibited NMDA-activated currents. The inhibitory effects of bis(propyl)-cognitin increased with the rise in NMDA and glycine concentrations. Kinetics analysis showed that the inhibition was of fast onset and offset with an off-rate time constant of 1.9 s. Molecular docking simulations showed moderate hydrophobic interaction between bis(propyl)-cognitin and the MK-801 binding region in the ion channel pore of the NMDA receptor. Bis(propyl)-cognitin was further found to compete with [3H]MK-801 with a Ki value of 0.27 μm, and the mutation of NR1(N616R) significantly reduced its inhibitory potency. Under glutamate-mediated pathological conditions, bis(propyl)-cognitin, in contrast to bis(heptyl)-cognitin, prevented excitotoxicity with increasing effectiveness against escalating levels of glutamate and much more effectively protected against middle cerebral artery occlusion-induced brain damage than did memantine. More interestingly, under NMDA receptor-mediated physiological conditions, bis(propyl)-cognitin enhanced long-term potentiation in hippocampal slices, whereas MK-801 reduced and memantine did not alter this process. These results suggest that bis(propyl)-cognitin is a UFO antagonist of NMDA receptors with moderate affinity, which may provide a pathologically activated therapy for various neurodegenerative disorders associated with NMDA receptor dysregulation

Long-Term Continuously Updated Deformation Time Series From Multisensor InSAR in Xi'an, China From 2007 to 2021

According to historical records, land subsidence has been occurring in Xi'an, China, since the 1960s, characterized by complex land subsidence patterns. This subsidence has the potential to cause serious societal and economic problems during the process of urbanization. Long-term, large-scale monitoring and dynamic high precision tracking of the evolution of surface deformation associated with geohazards is a prerequisite for effective prevention or advance warning of geological disasters. Synthetic aperture radar interferometry (InSAR), a satellite remote sensing technology, can facilitate such monitoring. Both currently operating and planned SAR satellites would provide extensive amounts of SAR data. In this article, we describe an approach for continuously updating long-term multisensor InSAR deformation time series using robust sequential least squares. It is successfully applied to near-real-time monitoring of the long-term evolution of surface deformation in Xi'an, China, from January 3, 2007 to February 12, 2021, using four SAR satellites: ALOS/PALSAR-1, TerraSAR-X, ALOS/PALSAR-2, and Sentinel-1A. In order to analyze deformation evolution, temporal independent component analysis was used to interpret deformation patterns. We found that land subsidence in Xi'an has slowed and even halted in some areas. However, large areas are uplifting, which presents a potential for geohazards. We conclude that the proposed approach using continuously updated deformation time series from multisensor InSAR can provide near-real-time deformation measurements, which are necessary for an early warning system

Near Real-Time InSAR Deformation Time Series Estimation With Modified Kalman Filter and Sequential Least Squares

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Entering the Era of Earth Observation-Based Landslide Warning Systems: A novel and exciting framework

Landslide early warning remains a grand challenge due to the high human cost of catastrophic landslides globally and the difficulty of identifying a diverse range of landslide triggering factors. There have been only a very limited number of success stories to date. However, recent advances in earth observation (EO) from ground, aircraft and space have dramatically improved our ability to detect and monitor active landslides and a growing body of geotechnical theory suggests that prefailure behavior can provide clues to the location and timing of impending catastrophic failures. In this paper, we use two recent landslides in China as case studies, to demonstrate that (i) satellite radar observations can be used to detect deformation precursors to catastrophic landslide occurrence, and (ii) early warning can be achieved with real-time in-situ observations. A novel and exciting framework is then proposed to employ EO technologies to build an operational landslide early warning system.This work was supported by the National Natural Science Foundation of China under grants 41801391, 41874005, and 41929001; the National Science Fund for Outstanding Young Scholars of China under grant 41622206; the Fund for International Cooperation under grant NSFCRCUK_NERC; Resilience to Earthquake-Induced Landslide Risk in China under grant 41661134010; the open fund of State Key Laboratory of Geodesy and Earth’s Dynamics (SKLGED2018-5-3-E); Sichuan Science and Technology Plan Project under grant 2019YJ0404; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project under grant SKLGP2018Z019; the Spanish Ministry of Economy and Competitiveness, the State Agency of Research, and the European Funds for Regional Development under projects TEC2017-85244-C2-1-P and TIN2014-55413-C2-2-P; and the Spanish Ministry of Education, Culture, and Sport under project PRX17/00439. This work was also partially supported by the U.K. Natural Environment Research Council through the Center for the Observation and Modeling of Earthquakes, Volcanoes, and Tectonics under come30001 and the Looking Inside the Continents From Space and Community Earthquake Disaster Risk Reduction in China projects under NE/K010794/1 and NE/N012151/1, respectively, and by the European Space Agency through the ESA-MOST DRAGON-4 project (32244 [4]). Roland Bürgmann acknowledges support by the NASA Earth Surface and Interior focus area

Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems.This work was supported by the ESA-MOST China DRAGON-5 project with ref. 59339, by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI), and the European Funds for Regional Development under grant [grant number PID2020-117303GB-C22], by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital in the framework of the project CIAICO/2021/335, by the Natural Science Foundation of China [grant numbers 41874005 and 41929001], the Fundamental Research Funds for the Central University [grant numbers 300102269712 and 300102269303], and China Geological Survey Project [grant numbers DD20190637 and DD20190647]. Xiaojie Liu and Liuru Hu have been funded by Chinese Scholarship Council Grants Ref. [grant number 202006560031] and [grant number 202004180062], respectively

Functionalisation of MWCNTs with poly(lauryl acrylate) polymerised by Cu(0)-mediated and RAFT methods

Poly(lauryl acrylate) P[LA] of various molar masses were prepared via reversible addition–fragmentation chain transfer (RAFT) polymerisation and Cu(0)-mediated radical polymerisation, for the purpose of improving the dispersion and interfacial adhesion of MWCNTs with polymers such as isotactic poly(propylene) (iPP). Lauryl acrylate (LA) was polymerised via RAFT to high conversion (95%), furnished polymers in good agreement with theoretical Mn with dispersity increasing with increasing Mn. LA polymerised via the Cu(0)-mediated method to full conversion (>98%), gave polymers in good agreement with theoretical Mn and low dispersity (Đ ≈ 1.2) for lower molar mass polymers. Low molar mass tailing was also observed for P[LA] via Cu(0)-mediated polymerisation for higher molar mass polymers. Thermogravimetric analysis (TGA) of P[LA] via RAFT showed an onset of degradation occurred at ≈340–350 °C, however, this decreased to ≈250–260 °C for lower molar mass polymers. TGA of the RAFT agent revealed an onset of degradation of ≈200–250 °C. Free radicals generated from thermal degradation of end groups did not influence the thermal stability of the P[LA] backbone and ‘unzipping’ commonly seen with methacrylates was not observed. TGA analysis of P[LA] via the Cu(0)-mediated method revealed a similar degradation profile to that of P[LA] via RAFT. The thermal stability of P[LA] is sufficient to allow for melt processing with iPP. P[LA] via RAFT mixed with MWCNTs showed an adsorption of ≈10–25 wt% P[LA] on to the MWCNTs. The onset of thermal degradation of the P[LA] remained unchanged after adsorption on to the MWCNTs. P[LA] via the Cu(0)-mediated method adsorbed up to 85 wt% and an increase in thermal stability of ≈50 °C was recorded. Increasing P[LA] and MWCNT concentration independently also resulted in an increase in the level of adsorption, possibility due to increased CH–π interaction. The difference in thermal stability could possibly be due to heat transfer from the P[LA] to the MWCNTs, resulting in delayed pyrolysis of P[LA]. Size exclusion chromatography (SEC) and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) of P[LA] after heating to 200 °C for 30 min in air showed loss of end groups but, the P[LA] backbone remained preserved for both polymer types. Evidence from transmission electron micrographs (TEM) shows the P[LA] adsorbing onto the MWCNT surface. Melt processing composites of P[LA] via Cu(0)-mediated with MWCNTs and iPP was possible as the P[LA] was thermally stable during the both extrusion and in the TGA when studied post melt mixing