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With the escalation of global climate change and human activity, geohazards become increasingly frequent which cause severe casualties and property losses to local communities. To alleviate this situation and provide scientific guidance for risk reduction, it is imperative to address some of the basic questions related to geohazards, including: i) how to detect active geohazards (AGs) rapidly and automatically over a wide area; ii) how to determine the region with a high level of hazard activity; iii) what are the primary conditioning factors (CFs) of AGs; and iv) do factors operate independently or are they interconnected. To tackle these issues, we propose a universally applicable framework for wide area automated detection of AGs. The framework is based on multi-source Earth observations which capture surface deformation ranging from millimeters to meters. Our study has focused on the Hexi Corridor (HXC) in Gansu Province, China, covering an area of 210,000 km2 with a length of 1100 km. First, we construct an AGs database for the HXC with high automatic and rapid update capabilities, including a total of 4492 AGs (3652 active landslides and 840 land subsidence areas). Second, using the Geographic Detectors method, we determine the primary CFs including elevation, land surface temperature, and precipitation. We find that faults exert greater control over very slow-moving landslides, but are less effective over slow-moving landslides. Third, we analyzed the interactive effects of dual CFs on geohazard actives. Any interaction effect of dual CFs contributes to the bivariate enhancement of geohazard activity. This study significantly enhances the capabilities of the wide area automated detection of AGs, and provides a crucial dataset for hazard prediction and mitigation along the HXC.

The mountainous areas of Southwest China are crucial regions for hydropower resource development. The unique geological environment and numerous constructed hydropower stations make landslides the primary geological hazard for the region. Therefore, the deformation monitoring and mechanism analysis of reservoir landslides have attracted extensive attention from the academic community. The Maoergai Reservoir (MEGR) is located in the middle reaches of the Heishui River in Sichuan Province, China. Following impoundment, numerous active slopes have arisen in the reservoir area, with the Xierguazi-Mawo landslide complex (XMLC) potentially being the most threatening. In this paper, we first acquired the surface displacement of the MEGR area, before and after impoundment, using time series Synthetic Aperture Radar Interferometry (InSAR) analysis of the ALOS-1/PALSAR-1, ALOS-2/PALSAR-2 and Sentinel-1 datasets. Then, the three-dimensional (3D) deformation field was inverted by combining multi-track InSAR observations and a topography-constrained model. Results show the presence of 47 active landslides within the 675 km2 reservoir area, a significant increase compared to the seven landslides identified prior to impoundment. Based on the derived 3D velocity vectors, we found that the motion of the XMLC exhibits heterogeneity in terms of spatial and temporal patterns. Further, the displacement time series of all detected active landslides suggests that irrigation and water level fluctuations are two major factors influencing the kinematic behaviors of landslides. Irrigation accelerates landslide movement and thus permanently alters the deformation trend, while water level fluctuations cause seasonal oscillations in the displacement time series with a time lag of approximately 100 days. The above results can contribute to landslide management and prevention in the MEGR area and provide a representation for numerous water reservoirs in southwest China.

Emergency response after earthquakes, especially rapid access to building damage information, is of great significance to ensure timely rescue and reduce casualties. However, manual field surveys of building damages are inefficient and dangerous, and optical satellite data are more susceptible to cloud interference after earthquakes. Synthetic aperture radar (SAR) is now widely used in disaster response efforts due to its full-time and all-weather capability. According to the change in coherence between SAR images before and after earthquakes, it is possible to identify damaged buildings that cause coherence loss. However, the accuracy of traditional coherence-based damage detection methods is relatively low due to biases in coherence estimation and inconsistency in spatio-temporal baselines. In this study, we propose a new method to produce a postearthquake building damage proxy map (BDPM) based on multitemporal Sentinel-1 coherence, which incorporates homogeneous SAR pixel coherence estimation and histogram matching techniques. The former is used to reduce estimation biases and the latter to reduce the effect of baseline inconsistencies in adjacent coherence maps. We successfully applied this method to the 2022 Mw 6.2 Afghanistan earthquake, the 2023 strong earthquake sequence in Turkey, and the 2023 Ms 6.2 Jishishan, China earthquake. We also verified its accuracy (over 80%) by comparing the BDPM with results from the United Nations Institute for Training and Research and analyzed various factors affecting the distribution of damaged buildings. These analyses confirm the effectiveness of our method for generating BDPM using free medium-resolution Sentinel-1 data, which will greatly assist in earthquake emergency response.

Subsidence caused by underground coal mining activities seriously threatens the safety of surface buildings, and interferometric synthetic aperture radar has proven to be one effective tool for subsidence monitoring in mining areas. However, the environmental characteristics of mining areas and the deformation behavior of mining subsidence lead to low coherence of interferogram. In this case, traditional phase unwrapping methods have problems, such as low accuracy, and often fail to obtain correct deformation information. Therefore, a novel phase unwrapping method is proposed using a channel-attention-based fully convolutional neural network (FCNet-CA) for low coherence mining areas, which integrates multiscale feature extraction block, bottleneck block, and can better extract interferometric phase features from the noise. In addition, based on the mining subsidence prediction model and transfer learning method, a new sample generation strategy is proposed, making the training dataset feature information more diverse and closer to the actual scene. Simulation experiment results demonstrate that FCNet-CA can restore the deformation pattern and magnitude in scenarios with high noise and fringe density (even if the phase gradient exceeds π). FCNet-CA was also applied to the Shilawusu coal mining area in Inner Mongolia Autonomous Region, China. The experimental results show that, compared with the root mean square error (RMSE) of phase unwrapping network and minimum cost flow, the RMSE of FCNet-CA in the strike direction is reduced by 67.9% and 29.5%, respectively, and by 72.4% and 50.9% in the dip direction, respectively. The actual experimental results further verify the feasibility and effectiveness of FCNet-CA.

Located on the southeastern periphery of the Tibetan Plateau, the Xianshuihe fault (XSHF) is an active left-lateral strike-slip fault renowned for its frequent and intensive seismic activities. This highlights the necessity of employing advanced geodetic methodologies to precisely evaluate the fault kinematics and seismic hazard potential along this fault. Among these techniques, interferometric synthetic aperture radar (InSAR) stands out for its high spatial resolution and regular revisit intervals, enabling accurate mapping of interseismic deformation associated with fault motion. However, the precision of InSAR in measuring deformation encounters several challenges, particularly artifacts stemming from phase unwrapping errors and atmospheric phase delays. In this study, we utilize ascending and descending Sentinel-1 InSAR images spanning from January 2017 to January 2023 to drive the line-of-sight (LOS) mean crustal velocities associated with the XSHF with emphasis on phase unwrapping errors and atmospheric delay corrections. Then, the reliability of the derived LOS velocities is assessed using independent observations from the Global Navigation Satellite System (GNSS). The inferred fault slip rate along the XSHF shows significant along-strike variations, gradually decreasing from ~11.1 mm/yr at the Luhuo section to ~6.6 mm/yr at the Kangding section and then sharply increasing to ~13.0 mm/yr towards its eastern terminus at the Moxi section. The fault locking depth shows similar along-strike variations, decreasing from ~19.5 km in the northwestern part to ~4.8 km at the Kangding section, before increasing to 19.6 km at the Moxi segment. Notably, apparent surface fault creeping, characterized by a slip rate of ~2.7 mm/yr, is observed at the Kangding segment, likely resulting from postseismic slip following the 2014 Mw 6.3 Kangding earthquake.

Abstract The reversible transformation of a nanohelix is one of the most exquisite and important phenomena in nature. However, nanomaterials usually fail to twist into helical crystals. Considering the irreversibility of the previously studied twisting forces, the reverse process (untwisting) is more difficult to achieve, let alone the retwisting of the untwisted crystalline nanohelices. Herein, we report a new reciprocal effect between molecular geometry and crystal structure which triggers a twisting-untwisting-retwisting cycle for tri-cobalt salicylate hydroxide hexahydrate. The twisting force stems from competition between the condensation reaction and stacking process, different from the previously reported twisting mechanisms. The resulting distinct nanohelices give rise to unusual structure elasticity, as reflected in the reversible change of crystal lattice parameters and the mutual transformation between the nanowires and nanohelices. This study proposes a fresh concept for designing reversible processes and brings a new perspective in crystallography.

Unmanned aerial vehicle (UAV) technology has witnessed widespread utilization in target surveillance activities. However, cooperative multiple UAVs for the identification of multiple targets poses a significant challenge due to the susceptibility of individual UAVs to false positive (FP) and false negative (FN) target detections. Specifically, the primary challenge addressed in this study stems from the weak discriminability of features in Synthetic Aperture Radar (SAR) imaging targets, leading to a high false alarm rate in SAR target detection. Additionally, the uncontrollable false alarm rate during electro-optical proximity detection results in an elevated false alarm rate as well. Consequently, a cumulative error propagation problem arises when SAR and electro-optical observations of the same target from different perspectives occur at different times. This paper delves into the target association problem within the realm of collaborative detection involving multiple unmanned aerial vehicles. We first propose an improved triplet loss function to effectively assess the similarity of targets detected by multiple UAVs, mitigating false positives and negatives. Then, a consistent discrimination algorithm is described for targets in multi-perspective scenarios using distributed computing. We established a multi-UAV multi-target detection database to alleviate training and validation issues for algorithms in this complex scenario. Our proposed method demonstrates a superior correlation performance compared to state-of-the-art networks.

Landslides have attracted extensive attention and research worldwide because of their tremendous destructiveness, and many catastrophic landslides have occurred along river corridors and reservoir bank slopes. The detection and detailed monitoring of potential landslides are prerequisites for landslide disaster prevention, with recent advance in spaceborne Interferometric Synthetic Aperture Radar (InSAR) having effectively addressed this challenge. In this article, a wide-area landslide detection and monitoring framework, combining multiple InSAR techniques, is proposed to investigate active landslides along the upper reaches of the Yellow River from Longyang Gorge to Liujia Gorge (UYRLL), north-west China. A total of 597 active landslides have been mapped in this region for the first time. Our analyses suggest that a large percentage of the detected landslides are distributed at an elevation of 2000–3000 m with slope angles of 5–30° and their spatial distribution density is correlated with regional tectonic activity. Multitemporal InSAR techniques have also been adopted to analyze the surface motion characteristics of two typical landslides (i.e., the Lijia landslide and the Xijitan landslide), showing that these two landslides are dominated by linear motion. The landslide movements also contain slight nonlinear oscillations, inferred from wavelet analysis to be associated with reservoir water area changes and seasonal rainfall. The proposed framework herein can be extended to the detection and monitoring of active landslides in other areas with similar geomorphic features, and the above findings on landslide characteristics can also contribute to landslide prevention and mitigation in the upper Yellow River area.

This study uses satellite radar observations to investigate the triggering and recovery mechanisms of landslides that are accelerated by earthquakes without immediate failures but showing a prolonged response.

In March 2021, a seismic sequence including three Mw>5.5 events struck northern Thessaly, Greece. Owing to the high temporal resolution of Sentinel-1 images which were sampled every 6 days and recorded the three events separately, we are able to map individually the coseismic deformation fields of the three events. Based on their respective coseismic displacements, we determined the geometry of the fault plane for each earthquake with the method of multipeak particle swarm optimization and inverted the best-fitting slip distribution by linear least squares inversion. Modelling results show that the three events occurred successively on 3, 4 and 12 March 2021 were all dominated by normal-slip motions on previously unknown faults within the top 15km of the Earth’s crust. The 3 March 2021 Mw 6.3 earthquake ruptured a northeast-dipping fault with a strike angle of 301° (clockwise from the North) and a dip angle of 46°, producing the maximum slip of about 2.2m. The slip motion of the 4 March 2021 Mw 5.9 aftershock shows a similar fault geometry (striking 297° and dipping 42°) to the 3 March mainshock, but with a considerably smaller dip-slip component (~0.8m). The 12 March 2021 Mw 5.6 aftershock occurred on a southwest-dipping fault (striking 100° and dipping 40°) with a normal fault slip of up to 0.5m. Static Coulomb stress changes triggered by the earthquake sequence imply a promotion relationship between the first 3 March event and the two subsequent events. Due to the coseismic stress perturbation, more than 70% of aftershocks were distributed in areas with increased Coulomb stress and the northwest segment of the Larissa fault close to the seismic sequence was exposed to a relatively high seismic risk.

The Pamir, situated in central Asia, is a result of the ongoing northward advance of the Indian continent, leading to compression of the Asian landmass. While geodetic and seismic data typically indicate that the most significant deformation in Pamir is along its northern boundary, an Mw 7.2 earthquake on 7 December 2015 and an Mw 6.8 earthquake on 23 February 2023 have occurred in the remote interior of Pamir. These two Mw ≥ 6.5 earthquakes, with good observations of satellite synthetic aperture radar data, provide a rare opportunity to gain insights into rupture mechanics and deformation patterns in this challenging-to-reach region. Here, we utilize spaceborne synthetic aperture radar data to determine the seismogenic faults and finite slip models for these two earthquakes. Our results reveal that the 2015 earthquake ruptured a ~88 km long, left-lateral strike-slip fault that dips to northwest. The rupture of the 2015 earthquake extended to the ground surface over a length of ~50 km with a maximum slip of ~3.5 m. In contrast, the 2023 earthquake did not rupture the ground surface, with a maximum slip of ~2.2 m estimated at a depth of ~9 km. Notably, the seismogenic fault of the 2015 earthquake does not align with the primary strand of the Sarez–Karakul fault system (SKFS), and the 2023 earthquake occurred on a previously unmapped fault. The well-determined seismogenic faults for the 2015 and 2023 earthquakes, along with the SKFS and other distributed faults in the region, suggest the existence of a wide shear zone extending from south to north within the central Pamir.

On 21 May 2021 (UTC), an MW 7.4 earthquake jolted the east Bayan Har block in the Tibetan Plateau. The earthquake received widespread attention as it is the largest event in the Tibetan Plateau and its surroundings since the 2008 Wenchuan earthquake, and especially in proximity to the seismic gaps on the east Kunlun fault. Here we use satellite interferometric synthetic aperture radar data and subpixel offset observations along the range directions to characterize the coseismic deformation of the earthquake. Range offset displacements depict clear surface ruptures with a total length of ~170 km involving two possible activated fault segments in the earthquake. Coseismic modeling results indicate that the earthquake was dominated by left-lateral strike-slip motions of up to 7 m within the top 12 km of the crust. The well-resolved slip variations are characterized by five major slip patches along strike and 64% of shallow slip deficit, suggesting a young seismogenic structure. Spatial–temporal changes of the postseismic deformation are mapped from early 6-day and 24-day InSAR observations, and are well explained by time-dependent afterslip models. Analysis of Global Navigation Satellite System (GNSS) velocity profiles and strain rates suggests that the eastward extrusion of plateau is diffusely distributed across the east Bayan Har block, but exhibits significant lateral heterogeneities, as evidenced by magnetotelluric observations. The block-wide distributed deformation of the east Bayan Har block along with the significant co- and post-seismic stress loadings from the Madoi earthquake imply high seismic risks along regional faults, especially the Tuosuo Lake and Maqên–Maqu segments of the Kunlun fault that are known as seismic gaps.

The presence of common mode error (CME) in the coordinate displacement time series of the Global Navigation Satellite System (GNSS) affects geophysical studies using GNSS observations. In order to investigate the effect of CME on the time series in GNSS networks in Shanxi, this paper proposes an improved superposition filtering method by introducing single-day solution accuracy, correlation coefficient, and spherical distance between stations as weights. The filtering effect is evaluated using the GNSS data in Shanxi. By using the improved stacking filtering method, the root mean square (RMS) values for N, E, U are reduced by approximately 27.8 %, 29.0 %, and 46.0 %, respectively. And compared to the traditional stacking filter, our improved method can achieve better results with CME extraction. We investigate the CME spatial-temporal characteristics and its relationship with environmental loading. The results show that the CME between stations decreases as the distance between stations increases. In addition, we analyze the effect of CME on the noise component and velocity estimates. Results show that removing the CME refines the velocity and leads to a significant reduction in the magnitude of noise, indicating that the CME is dominated by the flicker noise in Shanxi Province. [ABSTRACT FROM AUTHOR]

COVID-19 is threatening human health worldwide but no effective treatment currently exists for this disease. Current therapeutic strategies focus on the inhibition of viral replication or using anti-inflammatory/immunomodulatory compounds to improve host immunity, but not both. Traditional Chinese medicine (TCM) compounds could be promising candidates due to their safety and minimal toxicity. In this study, we have developed a novel in silico bioinformatics workflow that integrates multiple databases to predict the use of honeysuckle ( Lonicera japonica ) and Huangqi ( Astragalus membranaceus ) as potential anti-SARS-CoV-2 agents. Using extracts from honeysuckle and Huangqi, these two herbs upregulated a group of microRNAs including let-7a , miR-148b , and miR-146a , which are critical to reduce the pathogenesis of SARS-CoV-2. Moreover, these herbs suppressed pro-inflammatory cytokines including IL-6 or TNF-α, which were both identified in the cytokine storm of acute respiratory distress syndrome, a major cause of COVID-19 death. Furthermore, both herbs partially inhibited the fusion of SARS-CoV-2 spike protein-transfected BHK-21 cells with the human lung cancer cell line Calu-3 that was expressing ACE2 receptors. These herbs inhibited SARS-CoV-2 M pro activity, thereby alleviating viral entry as well as replication. In conclusion, our findings demonstrate that honeysuckle and Huangqi have the potential to be used as an inhibitor of SARS-CoV-2 virus entry that warrants further in vivo analysis and functional assessment of miRNAs to confirm their clinical importance. This fast-screening platform can also be applied to other drug discovery studies for other infectious diseases., Competing Interests: Author C-TW was employed by the company Phalanx Biotech Group. Author S-TC was employed by Chuang Song Zong Pharmaceutical Co., Ltd. Ligang Plant. Author Z-YH received a scholarship from ASUS Intelligent Cloud Services. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yeh, Doan, Huang, Chu, Shi, Lee, Wu, Lin, Chiang, Liu, Chuang, Ping, Liu and Huang.)