Large-scale Deformation Research Articles

Determination of present-day crustal deformation along the Kenyan rift system using InSAR

The Kenyan rift system is prone to deformation due to various geological processes and human activities, such as overexploitation of groundwater and exploitation of geothermal energy. Crustal deformation monitoring is essential for understanding the geodynamics of the rift and assessing potential hazards as the rift passes through densely populated areas. However, retrieving InSAR displacement measurements along the Kenya rift system is challenging due to the high variability in tropospheric delay caused by its location in the tropics and the significant topographic variations along the rift. Here, we leverage Sentinel-1 data to analyze both local and large-scale deformation in the rift using multi-temporal InSAR processing with an improved tropospheric correction method, which we previously proposed with a modification on variance covariance modeling. We provide evidence for the previously reported ground displacement of 5-7.5 cm southwest of the Suswa volcano as being a misidentification of a tropospheric delay, that was dominant on March 27, 2018. Moreover, we observe episodic ground displacement at Suswa and Longonot, attributable to magma movement. Our results indicate that Suswa experienced a 9 cm movement towards the satellite line of sight between 2018-2020, while Longonot moved 4 cm towards satellite line of sight between 2016-2018 and also underwent 3 cm displacement away from the satellite line of sight between 2019-2021. In addition, we observe land subsidence associated with geothermal exploitation in the range of 1-3.6 cm/yr in Olkaria as well as in multiple locations in Nairobi at a rate of up to 6 cm/yr, primarily attributed to groundwater overexploitation. Moreover, we detect undocumented ground displacement at the Chalbi salt flat and along the Elgeiyo escarpment, at rates of up to 4.8 cm/yr and 5.7 cm/yr, respectively. Long-wavelength, low-amplitude deformation at the Turkana depression aligned well with the current kinematics of the Kenyan rift system. We find that localized deformations, caused by magmatism and human activity, dominate the south segment of the rift, whereas large-scale low-amplitude deformations dominate the north segment. In summary, our results emphasize the importance of tropospheric delay correction in retrieving InSAR-derived displacement measurements along the Kenyan rift system.Graphical

Micro-Scale Topography Triggers Dynamic 3D Nuclear Deformations.

Navigating complex extracellular environments requires extensive deformation of cells and their nuclei. Most in vitro systems used to study nuclear deformations impose whole-cell confinement that mimics the physical crowding experienced by cells during 3D migration through tissues. Such systems, however, do not reproduce the types of nuclear deformations expected to occur in cells that line tissues such as endothelial or epithelial cells whose physical confinement stems principally from the topography of their underlying basement membrane. Here, it is shown that endothelial cells and myoblasts cultured on microgroove substrates that mimic the anisotropic topography of the basement membrane exhibit large-scale 3D nuclear deformations, with partial to complete nuclear penetration into the microgrooves. These deformations do not lead to significant DNA damage and are dynamic with nuclei cyclically entering and exiting the microgrooves. Atomic force microscopy measurements show that these deformation cycles are accompanied by transient changes in perinuclear stiffness. Interestingly, nuclear penetration into the grooves is driven principally by cell-substrate adhesion stresses, with a limited need for cytoskeleton-associated forces. Finally, it is demonstrated that myoblasts from laminopathy patients exhibit abnormal nuclear deformations on microgrooves, raising the possibility of using microgroove substrates as a novel functional diagnostic platform for pathologies that involve abnormal nuclear mechanics.

Bayesian predictive system for assessing the damage intensity of residential masonry buildings under the impact of continuous ground deformation

The paper introduces a method for predicting damage intensity in masonry residential buildings situated in mining areas, focusing on the impact of large-scale continuous ground deformation. The research utilizes in situ data collected in a database, encompassing structural and material features, as well as information on maintenance quality and building durability. In addition to this information, the database collected data on the intensity of continuous deformation of the mining area at the location of the building, as well as the range and intensity of damage identified in buildings. The information included in the database was the result of many years of observations of buildings during the disclosure of impacts from mining exploitation and was based on: the results of in-situ building inventory, analysis of available building documentation and information provided by mining companies. The archived data were categorized variables labeled. The transformation of the data to a labeled value was dictated directly by the assumptions of the GOBNILP algorithm. Ultimately, a predictive model, represented by an optimal Bayesian network structure, is established. The optimisation of the network structure is achieved through the adaptation of the GOBNILP Bayesian network learning algorithm from data. This optimisation process is executed through the Gurobi Optimizer. It is worth noting that this interdisciplinary approach represents one of the first applications of such a methodology in the field of civil and environmental engineering. The results obtained can therefore be of significant value given the fact that the methodology of detecting the structure of Bayesian networks from data is still developing intensively in other scientific fields. In the course of the analyses, metric scores are examined, and various network structures are assessed based on their complexity. Great values of classification accuracies over 91% were obtained. This meticulous evaluation allows for the selection of the optimal Bayesian network that best generalises the knowledge acquired during the learning process. The paper also demonstrates the potential application of the obtained model in diagnosing damage causes and predicting future occurrences, highlighting the versatility of the proposed approach for addressing issues in the field.

Asymmetrical calcium ions induced stress and remodeling in lipid bilayer membranes.

Ca2+ ions play crucial roles in regulating many chemical and biological processes, but their impact on lipid bilayer membranes remains elusive, especially when the impacts on the two leaflets are asymmetrical. Using a recently developed multisite Ca2+ model, we performed molecular dynamics simulations to study the impact of Ca2+ on the properties of membranes composed of POPC and POPS and observed that both the structure and fluidity of the membranes were significantly affected. In particular, we examined the influence of asymmetrically distributed Ca2+ on asymmetric lipid bilayers and found that imbalanced stress in the two leaflets was generated, with the negatively charged leaflet on the Ca2+-rich side becoming more condensed, which in turn induced membrane curvature that bent the membrane away from the Ca2+-rich side. We employed continuum mechanics to study the large-scale deformations of a spherical vesicle and found that the vesicle can go through vesiculation to form a multi-spherical shape in which a number of spheres are connected with infinitesimal necks, depending on the specific Ca2+ distributions. These results provide new insights into the underlying mechanisms of many biological phenomena involving Ca2+-membrane interactions and may lead to new methods for manipulating the membrane curvature of vesicles in chemical, biological, and nanosystems.

Cross-diffusion waves by cellular automata modeling: Pattern formation in porous media.

Porous earth materials exhibit large-scale deformation patterns, such as deformation bands, which emerge from complex small-scale interactions. This paper introduces a cross-diffusion framework designed to capture these multiscale, multiphysics phenomena, inspired by the study of multi-species chemical systems. A microphysics-enriched continuum approach is developed to accurately predict the formation and evolution of these patterns. Utilizing a cellular automata algorithm for discretizing the porous network structure, the proposed framework achieves substantial computational efficiency in simulating the pattern formation process. This study focuses particularly on a dynamic regime termed "cross-diffusion wave," an instability in porous media where cross-diffusion plays a significant role-a phenomenon experimentally observed in materials like dry snow. The findings demonstrate that external thermodynamic forces can initiate pattern formation in cross-coupled dynamic systems, with the propagation speed of deformation bands primarily governed by cross-diffusion and a specific cross-reaction coefficient. Owing to the universality of thermodynamic force-flux relationships, this study sheds light on a generic framework for pattern formation in cross-coupled multi-constituent reactive systems.

Monitoring surface deformation of the Qinghai–Tibet permafrost region using an improved MT-InSAR method based on spatial–temporal constraints

ABSTRACT Monitoring ongoing permafrost degradation on the Qinghai–Tibet Plateau is challenging because of its underground characteristics. Interferometric synthetic aperture radar (InSAR) technology, which can detect large-scale and high-precision surface deformation, has become an effective tool for indirect permafrost monitoring. Traditional InSAR methods usually solve permafrost deformation independently and on a point-by-point basis with high coherence, while existing permafrost deformation models ignore the interannual variation in deformation. This paper proposes an improved multi-temporal InSAR (MT-InSAR) method based on spatial–temporal constraints for monitoring permafrost deformation. The method establishes a segmented permafrost deformation model considering the interannual variation in deformation over time. The spatial similarity constraint is subsequently introduced to establish the relationship between deformations at neighboring points, improving the results of permafrost monitoring. Both simulated and real experiments confirmed the ability of the proposed method to capture interannual variations in permafrost deformation with higher accuracy and point density. A real experiment with Sentinel-1 data revealed significant interannual variations in surface deformation within the Wudaoliang-Tuotuohe permafrost region from 2018–2021, characterized by decreased linear deformation and slight seasonal deformation changes. Compared with the traditional methods and in-situ data, the deformation accuracy and point density can be improved by approximately 15.6% and 19.4%, respectively.

Impact of Temperature on the Hygroscopic Behavior and Mechanical Properties of Expansive Mudstone

Aiming at the large-scale nonlinear deformation of the roadway in Liangjia Coal Mine, Shandong Province, the mudstone of the 1602 working face is taken as the research object. A high-precision mudstone weathering test system integrating monitoring and control was developed, and the weathering tests of expanded mudstone were carried out at 10 °C, 20 °C, 30 °C and 40 °C. The results show that the hygroscopic curves of expanded mudstone demonstrate a nonlinear growth trend at different temperatures, and the influence of temperature on the hygroscopic curves is less than 20%. From the overall law, it can be roughly divided into three stages: the strong hygroscopic stage, the hygroscopic deceleration stage and the stable hygroscopic stage. The maximum expansion rates of the samples were 4.1%, 5.3%, 6.2% and 6.8%, respectively, and the water content and expansion rates corresponding to different ambient temperature and humidity were generally “concave”. The mechanical tests show that the mechanical properties of mudstone decrease as the ambient temperature increases, and the corresponding compressive strength decreases by 16~50%. The linear degradation is obvious, and the gradual expansion of peak strain indicates that the plasticity of the rock increases and the elastic modulus decreases linearly.

A reconfigurable compliant robotic gripper based on flexible scissors mechanism

Abstract Traditional robotic grippers are constrained by their fixed structures and limited grasping range, which poses difficulties when handling objects of various shapes and sizes. In contrast, reconfigurable grippers can flexibly adjust their grasping mode and configuration based on object properties, enabling them to accommodate a wider range of geometries. However, existing reconfigurable grippers face challenges in achieving structural consistency, large-scale continuous deformation, and ease of manufacturing and operation. To overcome these limitations, this study proposes a reconfigurable compliant robotic gripper (RCRG) based on a flexible scissor mechanism finger (FSMF). The FSMF incorporates an anisotropic scissor unit design that decouples extension and bending motions, achieving up to 4.5 times continuous finger length adjustment. Multiple FSMF modules can be mounted on a rigid scissor mechanism base to form either a two-fingered or three-fingered RCRG, depending on task requirements. With this configuration, the RCRGs can transition from a compact, retracted form to a significantly expanded state by deforming both the base and the FSMFs, thereby reconfiguring the grasping space to accommodate objects of various sizes, shapes, and orientations for diverse tasks. Theoretical modeling and experimental validation were conducted to analyze the mechanical performance of the FSMF under different extension ratios. Various grasping tests results demonstrate that the proposed RCRGs design efficiently and stably handles various objects, with a grasping envelope variation of up to 64 times, showcasing high adaptability and flexibility.

3D human model guided pose transfer via progressive flow prediction network

Human pose transfer is to transfer a conditional person image to a new target pose. The difficulty lies in modeling the large-scale spatial deformation from the conditional pose to the target one. However, the commonly used 2D data representations and one-step flow prediction scheme lead to unreliable deformation prediction because of the lack of 3D information guidance and the great changes in the pose transfer. Therefore, to bring the original 3D motion information into human pose transfer, we propose to simulate the generation process of real person image. We drive the 3D human model reconstructed from the conditional person image with the target pose and project it to the 2D plane. The 2D projection thereby inherits the 3D information of the poses which can guide the flow prediction. Furthermore, we propose a progressive flow prediction network consisting of two streams. One stream is to predict the flow by decomposing the complex pose transformation into multiple sub-transformations. The other is to generate the features of the target image according to the predicted flow. Besides, to enhance the reliability of the generated invisible regions, we use the target pose information which contains structural information from the flow prediction stream as the supplementary information to the feature generation. The synthesized images with accurate depth information and sharp details demonstrate the effectiveness of the proposed method.

Numerical study on vibration heat transfer enhancement of multi-configured blunt-headed cylinder with fin device

The large-scale flow-induced deformation is an effective heat transfer enhancement technology. Then, a blunt-headed cylinder with fin (BHC-F) was proposed to evaluate the heat transfer enhancement performance using flow-induced vibration with different configuration (incident angle Φ). The two-way fluid structure interaction method was utilized to explore the vibration response, flow field and heat transfer performance of BHC-F at Reynolds numbers Re = 500–1500. Vibration results found that the vibration displacement of BHC-F increased with the increase of Φ (0°–180°), which leaded to the acceleration of flow boundary layer separation. When Φ = 180°, the peak displacement of BHC-F reached 3.12 mm, which was 61.66 % and 34.48 % higher than that of Φ = 0° and 90°, respectively. At this time, heat transfer enhancement was obtained because the thermal boundary layer was reduced due to the interaction between the vortex and the wall. In addition, the PEC under all configurations were all greater than 1, indicating that BHC-F achieved the purpose of enhanced heat transfer. The increase of Re has a positive effect on the heat transfer enhancement of BHC-F. When Re = 1500, the maximum heat transfer enhancement of BHC-F-90° reached 13.8 %, which was 62.3 and 26.6 times higher than that of BHC-F-0° and BHC-F-180°. This study establishes a theoretical foundation for employing flow-induced vibration in heat transfer enhancement applications and offers technical support for enhancing the overall performance of heat exchangers.

Self-organized spatial targeting of contractile actomyosin rings for synthetic cell division

A key challenge for bottom-up synthetic biology is engineering a minimal module for self-division of synthetic cells. Actin-based cytokinetic rings are considered a promising structure to produce the forces required for the controlled excision of cell-like compartments such as giant unilamellar vesicles (GUVs). Despite prior demonstrations of actin ring targeting to GUV membranes and myosin-induced constriction, large-scale vesicle deformation has been precluded due to the lacking spatial control of these contractile structures. Here we show the combined reconstitution of actomyosin rings and the bacterial MinDE protein system within GUVs. Incorporating this spatial positioning tool, able to induce active transport of membrane-attached diffusible molecules, yields self-organized equatorial assembly of actomyosin rings in vesicles. Remarkably, the synergistic effect of Min oscillations and the contractility of actomyosin bundles induces mid-vesicle deformations and vesicle blebbing. Our system showcases how functional machineries from various organisms may be combined in vitro, leading to the emergence of functionalities towards a synthetic division system.

High Performance Conductive Composites and E-skins with Large-scale Manufacturing for Wearable Electronic Sensor Systems

Objective: High performance wearable sensors for biological and bio-technological recognition of human epidermal movements and muscle tissue deformations are attracting widespread attention and demonstrate fascinating potential for future wearable electronics. Methods: This work demonstrates conductive networks and film cracks-based stretchable strain sensors using carbonic sensing layers / mold star silicone elastomer nanocomposites. Results: The as-fabricated stretchable strain sensors demonstrate captivating superiority, including simplicity in the fabrication steps and ultra-high strain sensitivity far exceeding recently reported state-of-the-art strain sensors. Prominently, the stretchable strain sensors exhibit dazzling piezoresistivity with a high gauge factor of 2,185 and a wide sensing range of 30% strain. The working mechanism depends on the electrical resistance variations, which is strikingly altered by a percolating network crack surface microstructure due to strain concentration during tensile deformations. The ultra-sensitive sensing performance in conjunction with a wide sensing range, prominent linearity (R2>0.99 in the strain range of 15-30%), excellent reversibility characteristics and remarkable durability (more than 1,300 stretch-release cycles under a large-scale strain of 30%) for large-scale tensile deformations. Conclusion: The stretchable strain sensors to be employed as wearable strain monitoring devices for the diverse-range of practical applications, including but not confined to multi-scale monitoring, electronic skins, smart robotics, human-machine / computer interfaces, as well as sports performance monitoring.

Real-time Large-scale Deformation of Gaussian Splatting

Neural implicit representations, including Neural Distance Fields and Neural Radiance Fields, have demonstrated significant capabilities for reconstructing surfaces with complicated geometry and topology, and generating novel views of a scene. Nevertheless, it is challenging for users to directly deform or manipulate these implicit representations with large deformations in a real-time fashion. Gaussian Splatting (GS) has recently become a promising method with explicit geometry for representing static scenes and facilitating high-quality and real-time synthesis of novel views. However, it cannot be easily deformed due to the use of discrete Gaussians and the lack of explicit topology. To address this, we develop a novel GS-based method (GaussianMesh) that enables interactive deformation. Our key idea is to design an innovative mesh-based GS representation, which is integrated into Gaussian learning and manipulation. 3D Gaussians are defined over an explicit mesh, and they are bound with each other: the rendering of 3D Gaussians guides the mesh face split for adaptive refinement, and the mesh face split directs the splitting of 3D Gaussians. Moreover, the explicit mesh constraints help regularize the Gaussian distribution, suppressing poor-quality Gaussians ( e.g. , misaligned Gaussians, long-narrow shaped Gaussians), thus enhancing visual quality and reducing artifacts during deformation. Based on this representation, we further introduce a large-scale Gaussian deformation technique to enable deformable GS, which alters the parameters of 3D Gaussians according to the manipulation of the associated mesh. Our method benefits from existing mesh deformation datasets for more realistic data-driven Gaussian deformation. Extensive experiments show that our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate (65 FPS on average on a single commodity GPU).

In-Plane Cyclic Mechanical Properties of CF/PEEK/TPU Flexible Composite with Zero Poisson Ratio.

This paper presents the in-plane deformation and cyclic mechanical properties of CF/PEEK (Carbon Fiber-Reinforced Polyetheretherketone)-reinforced TPU (thermoplastic polyurethanes) flexible composites with a zero Poisson ratio. A novel CF/PEEK honeycomb reinforcement with a zero Poisson ratio was fabricated by using 3D-printing technology. Then, TPU was bonded in the two sides of the CF/PEEK honeycomb reinforcement. The in-plane deformation ability and cyclic mechanical properties were evaluated. The results show that the zero Poisson ratio flexible composite can achieve a large in-plane plastic deformation of more than 50% and can better maintain the zero Poisson ratio superstructure. By collecting and comparing the mechanical characteristic values of the CF/PEEK flexible composite under a cyclic load, the CF/PEEK flexible composite MH22-t0.6-CT has the best structural stability. The length of the structure was increased by about 12.53%. By studying the deformation mechanism and failure mechanisms of the flexible composites, the in-plane recyclability of the flexible composites was evaluated, which provides the basic research basis for large-scale in-plane deformation composites.

Unexpected far-field deformation of the 2023 Kahramanmaraş earthquakes revealed by space geodesy.

The spatiotemporal pattern of surface displacements from large earthquakes provides crucial insights about the deformation of Earth's crust at various scales and the interactions among tectonic plates. However, the lack of extensive and large-scale geodetic networks near such seismic events hinders our thorough understanding of the large-scale crustal deformation resulting from earthquakes. Using Türkiye's extensive and continuous global navigation satellite system (GNSS) network during the moment magnitude 7.8 and 7.6 Kahramanmaraş earthquakes on 6 February 2023, we show that large earthquakes can induce far-field crustal deformations (>700 kilometers), exceeding current predictions from elastic dislocation models. They can lead to the mobilization of tectonic plates and the triggering of far-field earthquakes, which carries profound implications for seismic hazard assessments and necessitates a new perspective on crustal deformation and earthquake mechanics.

Synergistic effects of dual reactive compatibilizers for high-performance fully biodegradable polylactic acid/poly (butyleneadipate-co-terephthalate) composites

The PLA-g-(GMA-co-St) graft copolymer (PGS) was prepared using melt-free radical grafting technology, PGS and ESO were simultaneously employed as compatibilizers for the poly (lactic acid)/poly (butylene adipate-co-terephthalate) (PLA/PBAT) blends. The effects of the type and amount of compatibilizers on the properties of the blends were studied. The results reveal that the epoxy groups in PGS and ESO can interact with the terminal groups of PLA and PBAT. The incorporation of either PGS or ESO separately can improve the compatibility between PLA and PBAT to a considerable degree. However, the introduction of both compatibilizers into the PLA/PBAT blends results in a notable shift of the Tg of the two phases, reduces the size of PBAT particles, and makes their dispersion more uniform. This reveals that the dual reactive compatibilizers can achieve a synergistic effect, significantly reduced the interfacial tension between the two phases and facilitated inter-phase dispersion, ultimately forming a more uniform microstructure. Simultaneously, as the amount of ESO added to the blends gradually increase, the vicat softening temperature and complete decomposition temperature of the blends continue to increase, the notched impact strength and elongation at the break of the blends gradually increase and then decrease. When the ESO amount reaches 4 wt%, the performance of each property increases to 88.9 °C, 447.66 °C, 335.16 %, and 23,359.30 J/m2, respectively. At this point, the fracture surface of the blend samples is accompanied by a large-scale plastic deformation. In conclusion, this work represents the first attempt to accomplish synergistic effects via dual reactive compatibilizers. Under the best formulation and processing circumstances, the PLA/PBAT blends greatly increase their overall performance while maintaining biodegradability, hence broadening their application prospects in packaging, agriculture, and disposable tableware.

Study on the Susceptibility of Steel Arches with Letting Pressure Nodes Based on Incremental Dynamic Analysis

When soft rock tunnels pass through fractured fault zones, they are particularly susceptible to extrusion and large-scale deformations, especially during seismic events. To address these challenges, this study introduces an innovative yield-support steel arch design featuring a circumferential letting pressure node at its core. This design delivers incremental support resistance within the deformation zone and a susceptibility curve is applied to evaluate the damage probability of the steel arch with a letting pressure node under seismic loading conditions. Measurements of the surrounding rock pressure and structural forces on the steel arch with the letting pressure node were conducted at the Baoshan Jewel Mountain Tunnel in China. The field experiment results revealed a 23% reduction in the surrounding rock pressure and an 11% decrease in the internal forces of the support structure. These findings demonstrate the successful application of the letting pressure node-supported steel arch in mitigating large deformations in soft rock environments. Additionally, using finite element software ANSYS 2022, a seismic time-history analysis was conducted, employing the relative deformation rate of the letting pressure node steel arch as the damage index and the peak ground acceleration (PGA) as the strength parameter to generate the incremental dynamic analysis (IDA) curve. According to the susceptibility curve derived from the incremental dynamic analysis, at the design ground motion level of 8 degrees, the letting pressure node steel arch has a 94% probability of exceeding its normal service life limit and experiencing damage. The findings of this study offer a novel approach to addressing large deformations in soft rock tunnels. The proposed susceptibility curves for steel arches with letting pressure nodes provide a robust foundation for predicting the damage probability of yielding support structures under seismic conditions.

Multilevel network for large deformation image registration based on feature consistency and flow normalization.

Deformable image registration is an essential technique of medical image analysis, which plays important roles in several clinical applications. Existing deep learning-based registration methods have already achieved promising performance for the registrations with small deformations, while it is still challenging to deal with the large deformation registration due to the limits of the image intensity-similarity-based objectivefunction. To achieve the image registration with large-scale deformations, we proposed a multilevel network architecture FCNet to gradually refine the registration results based on semantic feature consistency constraint and flow normalization (FN)strategy. At each level of FCNet, the architecture is mainly composed to a FeaExtractor, a FN module, and a spatial transformation module. FeaExtractor consists of three parallel streams which are used to extract the individual features of fixed and moving images, as well as their joint features, respectively. Using these features, the initial deformation field is estimated, which passes through a FN module to refine the deformation field based on the difference map of deformation filed between two adjacent levels. This allows the FCNet to progressively improve the registration performance. Finally, a spatial transformation module is used to get the warped image based on the deformation field. Moreover, in addition to the image intensity-similarity-based objective function, a semantic-feature consistency constraint is also introduced, which can further promote the alignments by imposing the similarity between the fixed and warped image features. To validate the effectiveness of the proposed method, we compared our method with the state-of-the-art methods on three different datasets. In EMPIRE10 dataset, 20, 3, and 7 fixed and moving 3D computer tomography (CT) image pairs were used for training, validation, and testing respectively; in IXI dataset, atlas to individual image registration task was performed, with 3D MR images of 408, 58, and 115 individuals were used for training, validation, and testing respectively; in the in-house dataset, patient to atlas registration task was implemented, with the 3D MR images of 94, 3, and 15 individuals being training, validation, and testing sets,respectively. The qualitative and quantitative comparison results demonstrated that the proposed method is beneficial for handling large deformation image registration problems, with the DSC and ASSD improved by at least 1.0% and 25.9% on EMPIRE10 dataset. The ablation experiments also verified the effectiveness of the proposed feature combination strategy, feature consistency constraint, and FNmodule. Our proposed FCNet enables multiscale registration from coarse to fine, surpassing existing SOTA registration methods and effectively handling long-range spatial relationships.

Identification of potential landslide in Jianzha county based on InSAR and deep learning

Landslide disasters have characteristics of frequent occurrence, widespread impact, and high destructiveness, posing serious threats to human lives, property, and the ecological environment. Timely and accurate early identification of landslides remains an urgent issue within the disaster prevention field. This study focuses on Jianzha County, Qinghai Province, integrating PS-InSAR, SBAS-InSAR, and optical remote-sensing techniques to delineate potential landslide-prone areas. Utilizing Google Earth imagery and existing landslide datasets, potential landslide points were identified through a deep learning model. Results indicate the following: (1) In Jianzha County, the variation trend of the average surface velocity monitored by PS-InSAR and SBAS-InSAR technology is consistent, and the deformation monitoring results are reliable. (2) Utilizing the deep learning model, 56 potential landslide points were identified, comprising 39 high-risk points and 17 medium-risk points. By integrating the spatial distribution data of historical geological disaster points, 10 out of 13 previously occurred landslide disaster points were found to be located at the identified high-risk landslide points, achieving a detection accuracy of 76.92%. (3) The spatial distribution of landslide points exhibits clustering, with slopes ranging from 10° to 40°, elevations between 15 and 30 m, and slope orientations predominantly toward the northeast. (4) Landslide formation is correlated with seasonal precipitation concentrations and temperature fluctuations. This method can provide a crucial basis for large-scale surface deformation monitoring and early identification of landslide risks.

Multifunctional chitosan-based composite hydrogels engineered for sensing applications

Chitosan-based hydrogels, as natural high-molecular-weight flexible materials, are widely utilized due to their outstanding properties. In this research, we developed a one-pot method for synthesizing a novel PVA/CS@PPy-PDAx% conductive hydrogel and explored the internal bonding patterns through molecular dynamics simulations. By adding PPy-PDA nanoparticles into a hydrogel matrix, an interpenetrating conductive network established successfully. The uniform distribution of PPy-PDA nanoparticles endowed the hydrogel with good electrical conductivity (0.171 S/m), significantly enhanced mechanical properties, and strain sensing (S = 5.04), as well as near-infrared photothermal responsiveness (temperature increase of 41.9 °C within 30 s). Additionally, due to the hydrogel's significant photothermal conversion efficiency under near-infrared radiation, it exhibits rapid elimination of Escherichia coli with an antibacterial efficiency exceeding 90 %. The unique hydrogen-bonded crosslinked structure provides the hydrogel with excellent re-healing properties, allowing for restoration through a freeze-thaw process after damage. The conductivity remains nearly unchanged after re-healing, maintaining the material's integrity and functionality. The flexible sensor based on this hydrogel has a response time of 100 ms and can sensitively detect large-scale deformations (e.g., joint bending at various angles), different gravitational forces, and recognize human handwriting. These characteristics make this hydrogel a promising candidate for advancing intelligent wearable technologies and human-machine interaction systems.