The root-knot nematode, Meloidogyne javanica, is one of the most damaging plant-parasitic nematodes, affecting chickpea and causing substantial yield losses worldwide. The damage potential and population dynamics of this nematode in chickpea in Ethiopia have yet to be investigated. In this study, six chickpea cultivars were tested using 12 ranges of initial population densities (Pi) of M. javanica second-stage juveniles (J2): 0, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64 and 128 J2 (g dry soil)-1 in a controlled glasshouse pot experiment. The Seinhorst yield loss and population dynamics models were fitted to describe population development and the effect on different measured growth variables. The tolerance limit (TTFW) for total fresh weight ranged from 0.05 to 1.22 J2 (g dry soil)-1, with corresponding yield losses ranging from 31 to 64%. The minimum yield for seed weight (mSW) ranged from 0.29 to 0.61, with estimated yield losses of 71 and 39%. The 'Haberu' and 'Geletu' cultivars were considered good hosts, with maximum population densities (M) of 16.27 and 5.64 J2 (g dry soil)-1 and maximum multiplication rate (a) values of 6.25 and 9.23, respectively. All other cultivars are moderate hosts for M. javanica; therefore, it is crucial to initiate chickpea-breeding strategies to manage the tropical root-knot nematode M. javanica in Ethiopia.

A group of earthquakes typically consists of a mainshock followed by multiple aftershocks. Exploration of the dynamic behaviors of soil subjected to sequential earthquake loading is crucial. In this paper, a series of cyclic simple shear tests were performed on the undisturbed soft clay under different cyclic stress amplitudes and reconsolidation degrees. The equivalent seismic shear stress was calculated based on the seismic intensity and soil buried depth. Furthermore, reconsolidation was conducted at the loading interval to investigate the influence of seismic history. An empirical model for predicting the variation of the accumulative dissipated energy with the number of cycles was established. The energy dissipation principle was employed to investigate the evolution of cyclic shear strain and equivalent pore pressure. The findings suggested that as the cyclic stress amplitude increased, incremental damage caused by the aftershock loading to the soil skeleton structure became more severe. This was manifested as the progressive increase in deformation and the rapid accumulation of dissipated energy. Concurrently, the reconsolidation process reduced the extent of the energy dissipation by inhibiting misalignment and slippage among soil particles, thereby enhancing the resistance of the soft clay to subsequent dynamic loading.

Buried pipelines are essential for the safe and efficient transportation of energy products such as oil, gas, and various chemical fluids. However, these pipelines are highly vulnerable to ground movements caused by geohazards such as seismic faults, landslide, liquefaction-induced lateral spreading, and soil creep, which can result in potential pipeline failures such as leaks or explosions. Response prediction of buried pipelines under such movements is critical for ensuring structural integrity, mitigating environmental risks, and avoiding costly disruptions. As such, this study adopts a Physics-Informed Neural Networks (PINNs) approach, integrated with a transfer learning technique, to predict structural response (e.g., strain) of both unreinforced and reinforced steel pipes subjected to Permanent Ground Displacement (PGD). The PINN method offers a meshless, simulation-free alternative to traditional numerical methods such as Finite Element Method (FEM) and Finite Difference Method (FDM), while eliminating the need for training data, unlike conventional machine learning approaches. The analyses can provide useful information for in-service pipe integrity assessment and reinforcement, if needed. The accuracy of the predicted results is verified against Finite Element (FE) and Finite Difference (FD) methods, showcasing the capability of PINNs in accurately predicting displacement and strain fields in pipelines under geohazard-induced ground movement.

Liquefaction resistance and post-liquefaction shear deformation are key aspects of the liquefaction behavior for granular soil. In this study, 3D discrete element method (DEM) is used to conduct undrained cyclic triaxial numerical tests on specimens with diverse initial fabrics and loading history to associate liquefaction resistance and post-liquefaction shear deformation with the fabric of granular material. The influence of several fabric features on liquefaction resistance is first analyzed, including the void ratio, particle orientation fabric anisotropy, contact normal fabric anisotropy, coordination number, and redundancy index. The results indicate that although the void ratio and anisotropy strongly influence liquefaction resistance, the initial coordination number or redundancy index can uniquely determine liquefaction resistance. Regarding post-liquefaction shear deformation, the above quantities do not dictate the shear strain induced after initial liquefaction. Instead, the mean neighboring particle distance (MNPD), a fabric measure previously introduced in 2D and extended to 3D in this study, is the governing factor for post-liquefaction shear. Most importantly, a unique relationship between the initial MNPD and ultimate saturated post-liquefaction shear strain is identified, providing a measurable state parameter for predicting the post-liquefaction shear of sand.

The cracking during the drying process of thickened tailings stack is a critical issue impacting its stability. This study establishes a comprehensive analytical framework that encompasses both mechanism cognition and technical methodologies by systematically integrating multidimensional research findings. Research indicates that cracking results from the coupling effects of environmental parameters and process conditions. The environmental chamber, with its precise control over external conditions, has emerged as essential experimental equipment for simulating actual working environments. From a mechanical perspective, water evaporation induces volume shrinkage, leading to microcrack formation when local tensile stress surpasses the matrix's tensile strength, ultimately resulting in a network of interconnected cracks. This process is governed by the dual parameters of matric suction and tensile strength. In terms of theoretical modeling, the fracture mechanics model analyzes crack propagation laws from an energy dissipation standpoint, while the stress path analysis model emphasizes the consolidation shrinkage coupling effect. The tensile damage model is particularly advantageous for engineering practice due to its parameter measurability. In numerical simulation technology, the finite element method is constrained by the predetermined crack path, whereas the discrete element method can dynamically reconstruct the crack evolution process but encounters the technical challenge of large-scale multi-field coupling calculations. Research suggests that future efforts should focus on optimizing theoretical prediction models that account for the characteristics and cracking behavior of tailings materials. Additionally, it is essential to develop a comprehensive equipment system that integrates real-time monitoring, intelligent regulation, and data analysis. This paper innovatively proposes the establishment of a multi-scale collaborative research paradigm that integrates indoor testing, numerical simulation, and on-site monitoring. By employing data fusion technology, it aims to enhance the accuracy of crack predictions and provide both theoretical support and technical guarantees for the safety prevention and control of thickened tailings stacks throughout their entire life cycle.

Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere. This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau, China, as well as in the related ecological and hydrological processes. However, the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques. Thus, this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected (GRD) data, the polarization decomposition parameters of Sentinel-1A single-look complex (SLC) data, the normalized difference vegetation index (NDVI) based on Sentinel-2B data, and the topographic factors based on digital elevation model (DEM) data. By combining these parameters with a machine learning model, we established a feature selection rule. A cumulative importance threshold was derived for feature variables, and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination (R2) and the unbiased root mean square error (ubRMSE). The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion, and the SHapley Additive exPlanations (SHAP) method was used to analyze the importance of these variables. The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion. Compared to the unfiltered model, the optimal feature combination led to a 0.09 increase in R2 and a 0.7% reduction in ubRMSE. Ultimately, the optimized model achieved a R2 of 0.87 and an ubRMSE of 5.6%. Analysis revealed that soil particle size had significant impact on soil water retention capacity. The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable, demonstrating a significant positive correlation. Moreover, the microtopographical features of hummocks interfered with soil moisture estimation, indicating that such terrain effects warrant increased attention in future studies within the permafrost regions. The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau, but also exhibits high computational efficiency (with a relative time reduction of 18.5%), striking an excellent balance between accuracy and efficiency. This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data, offering critical insights for ecological conservation, water resource management, and climate change adaptation on the Qinghai-Xizang Plateau.

To address the challenges of extraction difficulties and penetration risks associated with traditional spudcan jackup platforms, a new jack-up platform featuring a pile-leg mat foundation is proposed. The horizontal bearing capacity of hybrid foundations under the influence of dynamic loads is a critical factor that requires close attention. This research numerically examined the dynamic response of a hybrid foundation to horizontal cyclic loading on a sandy seabed. A user-defined subroutine was employed to incorporate the Cyclic Mobility (CM) model within Abaqus, facilitating the analysis of sand response under different densities. The horizontal cyclic bearing capacities of the foundation were investigated considering the effects of different loading conditions, sand density, and pile-leg penetration depth. Simulation results indicate that the cyclic loading amplitude, frequency, and load mode significantly influence the generation of soil excess pore water pressure (EPWP), subsequently affecting foundation displacement and unloading stiffness. Under cyclic loading, the loose sandy seabed shows the most pronounced fluctuations in EPWP and effective stress, leading to surface soil liquefaction. While surface soil in medium-dense and dense sand conditions remains non-liquefied, their effective stress still varies significantly. Increasing the pile-leg penetration depth enhances the foundation's horizontal bearing capacity while affecting its vertical bearing capacity slightly.

Ground subsidence is a common urban geological hazard in several regions worldwide. The settlement of loess fill foundations exhibits more complex subsidence issues under the coupled effects of geomechanical and seepage-driven processes. This study selected 21 ascending Sentinel-1 A radar images from April 2023 to October 2024 to monitor the loess fill foundation in Shaanxi, China. To minimize errors caused by the orbital phase and residual flat-earth phase, this research combined PS-InSAR technology with the three-threshold method to improve the SBAS-InSAR processing workflow, thereby exploring time-series deformation of the loess fill foundation. Compared with conventional SBAS-InSAR technology, the improved SBAS-InSAR technique provided more consistent deformation time-series results with leveling data, effectively capturing the deformation characteristics of the fill foundation. Additionally, geographic information system (GIS) spatial analysis techniques and statistical methods were employed to analyze the overall characteristics and spatiotemporal evolution of the ground surface deformation in the study area. On the other hand, the major drivers of the subsidence in the study area were also discussed based on indoor experiments and engineering geological data. The results showed gradual and temporal shifts of the subsidence center toward areas with the maximum fill depths. In addition, two directions of uneven subsidence were observed within the fill foundation study area. The differences in the fill depth and soil properties caused by the building foundation construction were the main factors contributing to the uneven settlement of the foundations. Foundation deformation was also positively and negatively affected by surface water infiltration. This study integrates remote sensing and engineering geological data to provide a scientific basis for accurately monitoring and predicting loess fill foundation settlement. It also offers practical guidance for regional infrastructure development and geological hazard prevention.

Alpine wet meadow (AWM), an important wetland type on the Qinghai-Tibet Plateau (QTP), is sensitive to climate change, which alters the soil hydrothermal regime and impacts ecological and hydrological functions in permafrost regions. The mechanisms underlying extreme AWM degradation in the QTP and hydrothermal factors controlling permafrost degradation remain unclear. In this study, soil hydrothermal processes, soil heat migration, and the permafrost state were measured in AWM and extremely degraded AWM (EDAWM). The results showed that the EDAWM exhibited delayed onset of both soil thawing and freezing, shortened thawing period, and extended freezing period at the lower boundary of the active layer. The lower ground temperatures resulted in a 0.2 m shallower active layer thickness in the EDAWM compared with the AWM. Moreover, the EDAWM altered soil thermal dynamics by redistributing energy, modifying soil moisture, preserving soil organic matter, and adjusting soil thermal properties. As for energy budget, a substantial amount of heat in the EDAWM was consumed by turbulent heat fluxes, particularly latent heat flux, which reduced the amount of heat transferred to the ground. Additionally, the higher soil organic matter content in EDAWM decreased the annual mean soil thermal conductivity from 1.42 W m- 1 K-1 in AWM to 1.26 W m- 1 K-1 in EDAWM, slowing down heat transfer within the active layer and consequently mitigating permafrost degradation. However, with continued climate warming, the soil organic matter content in EDAWM will inevitably decline due to microbial decomposition in the absence of new organic inputs. As the soil organic matter content diminishes, soil heat transfer processes will likely accelerate, and the permafrost warming rate may surpass that in undistributed AWM. These findings enhance our understanding of how alpine ecosystem succession influences regional hydrological cycles and greenhouse gas emissions.

Salinization of road base aggregates poses a critical challenge to the performance of coastal roads, as the intrusion of chlorine salts adversely affects the stability and durability of pavement structures. To investigate the cyclic behavior of salinized road base aggregates under controlled solution concentration, c, and crystallization degree, omega, a series of unsaturated cyclic tests were conducted with a large-scale triaxial apparatus. The results showed that variations in solution concentration had a negligible influence on the resilient modulus of road base aggregates, and no significant differences were observed in their shakedown behavior. However, the long-term deformational response of the aggregates was affected by the precipitation of crystalline salt. At low crystallization degrees, a significant increase in accumulated axial strain and a decrease in resilient modulus were observed with increasing omega. Once the crystallization degree exceeded a critical threshold (omega(c)), there was a reduction in accumulated strain and an increase in resilient modulus. The precipitation of crystalline salt also disrupted the shakedown behavior of road base aggregates. During the nascent stages of crystallization (omega < 0.33), the presence of fine crystalline powders and clusters in the saltwater mixture destabilized the soil skeleton, resulting in a transition from the plastic shakedown stage to the plastic creep stage. This poses potential risks to the long-term characteristics and durability of the road base courses.