This study assesses the stability of the Bei'an-Hei'he Highway (BHH), located near the southern limit of latitudinal permafrost in the Xiao Xing'anling Mountains, Northeast China, where permafrost degradation is intensifying under combined climatic and anthropogenic influences. Freeze-thaw-induced ground deformation and related periglacial hazards remain poorly quantified, limiting regional infrastructure resilience. We developed an integrated framework that fuses multi-source InSAR (ALOS, Sentinel-1, ALOS-2), unmanned aerial vehicle (UAV) photogrammetry, electrical resistivity tomography (ERT), and theoretical modeling to characterize cumulative deformation, evaluate present stability, and project future dynamics. Results reveal long-term deformation rates from -35 to +40 mm/yr within a 1-km buffer on each side of the BHH, with seasonal amplitudes up to 11 mm. Sentinel-1, with its 12-day revisit cycle, demonstrated superior capability for monitoring the Xing'an permafrost. Deformation patterns were primarily controlled by air temperature, while precipitation and the topographic wetness index enhanced spatial heterogeneity through thermo-hydrological coupling. Wavelet analysis identified a 334-day deformation cycle, lagging climate forcing by similar to 107 days due to the insulating effects of peat. Early-warning analysis classified 4.99 % of the highway length as high-risk (subsidence 10.91 mm/yr). The InSAR-based landslide prediction model achieved high accuracy (Area Under the Receiver Operating Characteristic (ROC) Curve, or AUC = 0.9486), validated through field surveys of subsidence, cracking, and slow-moving failures. The proposed 'past-present-future' framework demonstrates the potential of multi-sensor integration for permafrost monitoring and provides a transferable approach for assessing infrastructure stability in cold regions.

Canopy reflectance (CR) models describe the transfer and interaction of radiation from the soil background to the canopy layer and play a vital role in the retrieval of biophysical variables. However, few efforts have focused on estimating soil background scattering operators, resulting in uncertainties in CR modelling, especially over sloping terrain. This study developed a canopy reflectance model for simulating CR over sloping terrain, which combines the general spectral vector (GSV) model, the PROSPECT model, and 4SAIL model coupled with topography (GSV-PROSAILT). The canopy reflectance simulated by GSV-PROSAILT was validated against two datasets: discrete anisotropic radiative transfer (DART) simulations and remote sensing observations. A comparison with DART simulations under various conditions revealed that the GSV-PROSAILT model captures terrain-induced CR distortion with high accuracy (red band: coefficient of determination $\lpar {\rm R 2} \rpar = 0.731$(R2)=0.731, root-mean-square error (RMSE) = 0.007; near infrared (NIR) band: $\rm R2 = 0.8319$R2=0.8319, RMSE = 0.0098). The results of remote sensing observation verification revealed that the GSV-PROSAILT model can be successfully used in CR modelling. These validations confirmed the performance of GSV-PROSAILT in soil and canopy reflectance modelling over sloping terrain, indicating that it can provide a potential tool for biophysical variable retrieval over mountainous areas.

Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

Aeolian sand, as a primary medium of desertification, changes surface energy budgets and consequently affects both ecological systems and infrastructure stability on the Tibetan Plateau. Accurate interpretation of upper boundary conditions is critical for assessing aeolian sand's effects on subsurface hydrothermal processes. Nevertheless, current numerical simulations typically rely on empirical thermal boundaries and neglect surface radiation components and latent heat exchange, causing, significant deviations between simulations and field observations. This study establishes a thermal boundary model to calculate net surface energy (Q) based on energy balance theory and 13 sets of reflectance experiments. Using meteorological data from 2003 to 2019, soil temperature evolution was simulated under three aeolian sand coverage conditions: dry, 5 % moisture, and 10 % moisture. Results indicated that the simulated outputs exhibit strong correlations with observed data in terms of trend direction, phase timing of peaks and troughs, and temperature amplitude (R > 0.93, p < 0.0001). At the sand-atmosphere interface (-0.05 m), the annual mean temperature under dry aeolian sand cover reached 5.300 degrees C, which is 4.823 degrees C higher than that of the exposed surface (0.477 degrees C) during 2005-2006. When including moisture, the latent heat-driven cooling effect became evident, and the annual mean temperature at the sand surface dropped significantly to 0.930 degrees C (5 % moisture) and 1.461 degrees C (10 % moisture). More importantly, moisture cooling effects in shallow layers (-0.05 to-0.4 m) exhibit non-monotonic behavior: 10 % moisture yields higher annual mean temperatures than 5 % moisture (e.g., 1.370 degrees C vs. 0.858 degrees C at-0.2 m), suggesting aeolian sand's thermal impact on underlying permafrost involves critical moisture thresholds.

Permafrost is undergoing widespread degradation affected by climate change and anthropogenic factors, leading to seasonal freezing and thawing exhibiting interannual, and fluctuating differences, thereby impacting the stability of local hydrological processes, ecosystems, and infrastructure. To capture this seasonal deformation, scholars have proposed various InSAR permafrost deformation models. However, due to spatial-temporal filtering smoothing high-frequency deformation and the presence of approximate assumptions in permafrost models, such differences are often difficult to accurately capture. Therefore, this paper applies an InSAR permafrost monitoring method based on moving average models and annual variations to detect freezing and thawing deformation in the Russian Novaya Zemlya region from 2017 to 2021 using Sentinel-1 data. Most of the study area's deformation rates remained between 10 and 10 mm/yr, while in key oil extraction areas, they reached -20 mm/yr. Seasonal deformation amplitudes were relatively stable in urban areas, but reached 90 mm in regions with extensive development of thermokarst lakes, showing a significant increasing trend. To validate the accuracy of the new method in capturing seasonal deformations, we used seasonal deformations obtained from different methods to retrieval the Active Layer Thickness (ALT), and compared them with field ALT measurement data. The results showed that the new method had a smaller RMSE and improved accuracy by 5% and 30% in two different ALT observation areas, respectively, compared to previous methods. Additionally, by combining the spatial characteristics of seasonal deformation amplitudes and ALT, we analyzed the impact of impermeable surfaces, confirming that human-induced surface hardening alters the feedback mechanism of perennial frozen soil to climate.

Ground subsidence resulting from underground coal mining poses significant challenges to urban safety, infrastructure stability, and environmental protection, particularly in regions extending beneath water bodies. This study investigates subsidence patterns in the Kozlu coal basin by integrating Interferometric Synthetic Aperture Radar (InSAR), numerical modelling, and machine learning techniques. The Kozlu coal basin, located in Zonguldak, Turkey, serves as a critical example, where extensive mining activities have led to complex deformation patterns. InSAR effectively captures terrestrial subsidence but is limited in underwater regions. Numerical modelling provides insights into geological behaviour but requires extensive input data. Machine learning, specifically Gaussian Process Regression (GPR), bridges this gap by predicting subsidence in unobservable underwater zones with high accuracy. The integrated approach reveals consistent deformation trends across terrestrial and marine environments, offering practical tools for risk mitigation and resource management. These findings underscore the importance of interdisciplinary methods in addressing complex geological challenges and pave the way for future advancements in subsidence monitoring and prediction.

In this study, the effect of near-field and far-field ground motions on the seismic response of the soil pile system is investigated. The forward directivity effect, which includes a large velocity pulse at the beginning of the velocity time history of the ground motion is the most damaging phenomenon observed in near-field ground motions. To investigate the effect of near-field and far-field ground motions on the seismic response of a soil-pile system, a three-dimensional model consisting of the two-layer soil, liquefiable sand layer over dense sand, and the pile is utilized. Modeling is conducted in FLAC 3D software. The P2P Sand constitutive model is selected for sandy soil. Three fault-normal near-field and three far-field ground motion records were applied to the model. The numerical results show that near field velocity pulses have a considerable effect on the system behavior and sudden huge displacement demands were observed. Also, during the near-field ground motions, the exceeded pore water pressure coefficient (Ru) increases so that liquefaction occurs in the upper loose sand layer. Due to the pulse-like ground motions, a pulse-like relative displacement is created in response to the pile. Meanwhile the relative displacement response of the pile is entirely different due to the energy distribution during the far-field ground motions.

The soil strength of soft clay is influenced by strain rate effect. Models considering strain rate effect always ignore the impact of loading rate on pore pressure and have poor applicability to 3D engineering problems. Based on the classic inelastic core boundary surface model, a logarithmic rate function representing the strain rate effect of soft soil was introduced to the hardening law. A new parameter H was added to adjust the plastic modulus while another new parameter mu is introduced to account for the strain rate effect. A rate-effect boundary surface constitutive model suitable for saturated clay was subsequently proposed. Combined with the implicit integral numerical algorithm and stress-permeability coupling analysis, the innovative model was implemented in the finite element software and validated by comparing with the results of triaxial tests. By analysing the rate-effect of 11 types of soft soil, a formula to calculate the rate parameter was derived. The developed model was used to calculate the undrained vertical bearing capacity and sliding resistance of a movable subsea mudmat. The mudmat frictional coefficient from soil undrained to partial drained and finally undrained state was obtained and compared with those from the Modified Cam-Clay model. Identical results were obtained without considering the rate effect. When considering the strain rate effect on the improvement of soil strength, the friction resistance coefficient initially decreases and then increases with the decrease of the sliding speed, eventually stabilising after reaching the limit value. The rate-effect on the friction resistance coefficient is most prominent under undrained conditions with high sliding speeds. The soil strain rate effect is suggested to be considered in the design of the subsea mudmat avoid underestimating the friction resistance.

Fragile fruits, which are prone to mechanical damage and microbial infection, necessitate protective materials that possess both cushioning and antimicrobial properties. In this study, we present a novel genipin-crosslinked chitosan/gelatin aerogel (CS/GEL/GNP) synthesized through direct mixing and free-drying techniques. The mechanical properties and cushioning capacities of the CS/GEL/GNP aerogel were thoroughly characterized, alongside an evaluation of its antimicrobial efficacy. The composite aerogel demonstrated remarkable compressibility and shape recovery characteristics. In a transportation simulation test, the aerogel effectively protected strawberries from mechanical damage. Furthermore, the composite aerogel exhibited enhanced antimicrobial activities against Escherichia coli, Staphylococcus aureus and Botrytis cinerea in vitro. The quality of strawberries was successfully maintained at ambient temperature when packaged with the CS/GEL/GNP. Notably, the aerogel could be completely degraded in the soil within 21 days and is nontoxic to cells. Consequently, the dual-functional CS/GEL/GNP aerogel presents a promising option for packaging materials aimed at protecting delicate fruits.

When developing Arctic territories, studying and forecasting the state of cryogenic landscapes in the context of climate change plays an important role. General conclusions about permafrost degradation do not fully capture changes at regional and local levels, as the direction and pace of landscape transformation depend on many factors, including the specific characteristics of the terrain. Permafrost degradation and changes in the depth of the active layer thickness (ALT) can be accompanied by alterations in ground vegetation cover (GVC) and surface moisture, which can be recorded through remote sensing (RS) data. However, there is a knowledge gap regarding the use of RS data to identify long-term trends in the phytocenotic properties of GVC and soil moisture at different geomorphological levels, as well as to determine the relationship between these trends and changes in ALT. In this study, based on Landsat data from 1985 to 2024, changes in GVC and soil moisture across various geomorphological levels were identified in a local area of the Yamal Peninsula. The analysis used the NDVI vegetation index, the NDWI moisture index, and the WI (Wetness Index) temperature-vegetation index, which reflects the moisture content of GVC and soil. The general trend observed is an increase in the growth rates of these indices as the geomorphological levels rise from the floodplain to Terrace IV. A comparison of these observed trends in the NDVI, NDWI, and WI indices with in situ geocryological observations shows the potential of using these indices as indicators of ALT change.