On December 18, 2023, a magnitude MS6.2 earthquake struck Jishishan County, Gansu Province, triggering over 40 seismic subsidence sites within a seismic intensity VI zone, 32 km from the epicenter.The earthquake caused tens of millions in economic losses to mountain photovoltaic power stations. Extensive geological surveys and comparisons with similar landslides (such as soil loosening, widespread cracks, and stepped displacements) triggered by the 1920 Haiyuan MS8.5 earthquake and the 1995 Yongdeng MS5.8 earthquake, this study preliminarily identifies one subsidence sites as a seismic-collapsed loess landslide. To investigate its disaster-causing mechanism: the dynamic triaxial test was conducted to assess the seismic subsidence potential of the loess at the site, and the maximum subsidence amount under different seismic loads were calculated by combining actual data from nearby bedrock stations with site amplification data from the active source; simulation of the destabilization evolution of seismic-collapsed loess landslides by large-scale shaking table tests; and a three-dimensional slope model was developed using finite element method to study the complex seismic conditions responsible for site damage. The research findings provide a theoretical foundation for further investigations into the disaster mechanisms of seismic-collapsed loess landslides.

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.

Mexican rural communities suffer significant impacts on the health of their population due to the application of pesticides that contaminate local air, water, soil, and food. Prolonged exposure to these toxic substances affects the long-term health of the population, especially children, who are extremely vulnerable to damage to their physical and neurocognitive development. This problem is analyzed in the context of industrial and extractive agriculture, which focuses on monocultures for national and export markets within the framework of a lax and permissive behavior of the Mexican state that protects neither the health of workers nor the rights of children to a healthy life. This article presents the results of a case study in a rural locality in a region of intensive agriculture. Two urine samples were taken from 180 schoolchildren, ages 3-14, to analyze their exposure to pesticides in a mass chromatograph; the samples were supplemented with a survey of their families and an ethnographic study.

To investigate the effect of interface temperature on the soil-reinforcement interaction mechanism, a series of pullout tests were conducted considering different types of reinforcement (geogrid and non-woven geotextile), backfill (dry sand, wet sand, and clay), and six interface temperatures. The test results indicate that at interface temperatures of 0 degrees C and above, reinforcement failure didn't occur during the pullout tests, whereas it predominantly occurred at subzero temperatures. Besides, the pullout resistance for the same soil-reinforcement interface gradually decreased as the interface temperature rose. At a given positive interface temperature, the pullout resistance between wet sand and reinforcement was significantly higher than that of the clayreinforcement interface but lower than that of the dry sand-reinforcement interface. Compared with geotextile reinforcements, geogrids were more difficult to pull out under the same interface temperature and backfill conditions. In addition, the lag effect in the transfer of tensile forces within the reinforcements was significantly influenced by the type of soil-reinforcement interface and the interface temperature. Finally, the progressive deformation mechanism along the reinforcement length at different interface temperatures was analyzed based on the strain distribution in the reinforcement.

The aerosol scattering phase function (ASPF), a crucial element of aerosol optical properties, is pivotal for radiative forcing calculations and aerosol remote sensing detection. Current detection methods for the ASPF include multi-sensor detection, single-sensor rotational detection and imaging detection. However, these methods face challenges in achieving high-resolution full-angle measurement, particularly for small forward (i.e., less than 10 degrees) or backward (i.e., more than 170 degrees) scattering angles in open path. In this work, a full-angle ASPF detection system based on the multi-field-of-view Scheimpflug lidar technique has been proposed and demonstrated. A 450 nm continuous-wave semiconductor laser was utilized as the light source and four CMOS image sensors were employed as detectors. To detect the full-angle ASPF, four receiving units capture angular scattering signals across different angle ranges, namely 0 degrees-20 degrees, 10 degrees-96 degrees, 84 degrees-170 degrees, 160 degrees-180 degrees, respectively. The influence of the relative illumination and angular response of the used image sensors have been corrected, and a signal stitching algorithm was developed to obtain a complete 0-180 degrees angular scattering signal. Atmospheric measurements have been conducted by employing the full-angle ASPF detection system in open path. The experimental results of the ASPF have been compared with the AERONET data from the Socheongcho station and simulated ASPF based on the typical aerosol models in mainland China, showing excellent agreement. The promising results demonstrated in this work have shown a great potential for detecting the full-angle ASPF in open path.

The presence of frozen volatiles (especially H2O ice) has been proposed in the permanently shadowed regions (PSRs) near the poles of the Moon, based on various remote measurements including the visible and near-infrared (VNIR) spectroscopy. Compared with the middle- and low-latitude areas, the VNIR spectral signals in the PSRs are noisy due to poor solar illumination. Coupled with the lunar regolith coverage and mixing effects, the available VNIR spectral characteristics for the identification of H2O ice in the PSRs are limited. Deep learning models, as emerging techniques in lunar exploration, are able to learn spectral features and patterns, and discover complex spectral patterns and nonlinear relationships from large datasets, enabling them applicable on lunar hyperspectral remote sensing data and H2O-ice identification task. Here we present H2O ice identification results by a deep learning-based model named one-dimensional convolutional autoencoder. During the model application, there are intrinsic differences between the remote sensing spectra obtained by the orbital spectrometers and the laboratory spectra acquired by state-of-the-art instruments. To address the challenges of limited training data and the difficulty of matching laboratory and remote sensing spectra, we introduce self-supervised learning method to achieve pixel-level identification and mapping of H2O ice in the lunar south polar region. Our model is applied to the level 2 reflectance data of Moon Mineralogy Mapper. The spectra of the identified H2O ice-bearing pixels were extracted to perform dual validation using spectral angle mapping and peak clustering methods, further confirming the identification of most pixels containing H2O ice. The spectral characteristics of H2O ice in the lunar south polar region related to the crystal structure, grain size, and mixing effect of H2O ice are also discussed. H2O ice in the lunar south polar region tends to exist in the form of smaller particles (similar to 70 mu m in size), while the weak/absent 2-mu m absorption indicate the existence of unusually large particles. Crystalline ice is the main phase responsible for the identified spectra of ice-bearing surface however the possibility of amorphous H2O ice beneath optically sensed depth cannot be ruled out.

This study systematically investigated the pore structure response of kaolin and illite/smectite mixed-layer rich clay in a reconstituted state to one-dimensional (1D) compression by first performing oedometer tests on saturated clay slurries, followed by characterising their pore structure using multi-scale characterisation techniques, with the primary objective of advancing the current understanding of the microstructural mechanisms underlying the macroscopic deformation of such clays. Under 1D loading, the volume reduction observed at the macro level essentially represented the macroscopic manifestation of changes in inter-aggregate porosity at the pore scale. It was the inter-particle pores that were compressed, despite the interlayer pores remaining stable. Two distinct pore collapse mechanisms were identified: kaolin exhibited a progressive collapse of particular larger pore population in an ordered manner, whereas illite/smectite mixed-layer rich clay demonstrated overall compression of inter-aggregate pores. Accordingly, mathematical relationships between the porosity and compressibility parameters for these two soils were proposed, with the two exhibiting opposite trends arising from their distinct microstructural features. Approaching from the unique perspective of pore structure, quantitative analysis of pore orientation and morphology on the vertical and horizontal planes demonstrated some progressively increasing anisotropy during compression. These findings provide important insights into porescale mechanisms governing clay compression behaviour and enrich the limited microporosity database in soil mechanics.

Ensuring the accuracy of free-field inversion is crucial in determining seismic excitation for soil-structure interaction (SSI) systems. Due to the spherical and cylindrical diffusion properties of body waves and surface waves, the near-fault zone presents distinct free-field responses compared to the far-fault zone. Consequently, existing far-fault free-field inversion techniques are insufficient for providing accurate seismic excitation for SSI systems within the near-fault zone. To address this limitation, a tailored near-fault free-field inversion method based on a multi-objective optimization algorithm is proposed in this study. The proposed method establishes an inversion framework for both spherical body waves and cylindrical surface waves and then transforms the overdetermined problem in inversion process into an optimization problem. Within the multi-objective optimization model, objective functions are formulated by minimizing the three-component waveform differences between the observation point and the delayed reference point. Additionally, constraint conditions are determined based on the attenuation property of propagating seismic waves. The accuracy of the proposed method is then verified through near-fault wave motion characteristics and validated against real downhole recordings. Finally, the application of the proposed method is investigated, with emphasis on examining the impulsive property of underground motions and analyzing the seismic responses of SSI systems. The results show that the proposed method refines the theoretical framework of near-fault inversion and accurately restores the free-field characteristics, particularly the impulsive features of near-fault motions, thereby providing reliable excitation for seismic response assessments of SSI systems.

The development of biodegradable and recyclable food packaging materials derived from biomass is a promising solution to mitigate resource depletion and minimize ecological contamination. In this study, lignin nanoparticles (LNPs) were effectively produced from bamboo powder using an eco-friendly recyclable acid hydrotrope (RAH) strategy. A sustainable CA/LNPs nanocomposite film was then designed by incorporating these LNPs into a casein (CA) matrix. The LNPs served as nucleation templates, inducing ordered hydrogen bonding and close packing of the CA chains. The addition of 5 wt% LNPs significantly enhanced the mechanical properties of the film, with tensile strength enhanced to 21.42 MPa (219.7 % improvement) and elastic modulus rising to 354.88 MPa (220.3 % enhancement) compared to pure CA film. Notably, the resultant CA/LNPs nanocomposite film exhibited recyclable recasting characteristics, maintaining a reasonable mechanical strength even after three recasting cycles. The incorporation of LNPs also decreased the water solubility of the pure CA film from 31.65 % to 24.81 % indicating some interactions are taking place, while endowing the film with superior UV-blocking ability, achieving nearly complete absorption in the 200-400 nm range. Moreover, the inherent properties of LNPs imparted improved antibacterial and antioxidant activities to the CA/LNPs nanocomposite film. Owing to its comprehensive properties, the CA/LNPs nanocomposite film effectively extended the storage life of strawberries. A soil burial degradation test confirmed over 100 % mass loss within 45 days, highlighting excellent degradability of the films. Therefore, the simple extraction of LNPs and the easily recovery of p-TsOH provide significant promise and feasibility for extending the developed methodologies in this work to rapidly promote the produced films in fields such as degradable and packaging materials.

Thawing-triggered slope failures and landslides are becoming an increasing concern in cold regions due to the ongoing climate change. Predicting and understanding the behaviour of frozen soils under these changing conditions is therefore critical and has led to a growing interest in the research community. To address this challenge, we present the first mesh-free smoothed particle hydrodynamics (SPH) computational framework designed to handle the multi-phase and multi-physic coupled thermo-hydro-mechanical (THM) process in frozen soils, namely the THM-SPH computational framework. The frozen soil is considered a tri-phase mixture (i.e., soil, water and ice), whose governing equations are then established based on u-p-T formulations. A critical-state elasto-plastic Clay and Sand Model for Frozen soils (CASM-F), formulated in terms of solid-phase stress, is then introduced to describe the transition response and large deformation behaviour of frozen soils due to thawing action for the first time. Several numerical verifications and demonstrations highlight the usefulness of this advanced THM-SPH computational framework in addressing challenging problems involving thawing-induced large deformation and failures of slopes. The results indicate that our proposed single-layer, fully coupled THM-SPH model can predict the entire failure process of thawing-induced landslides, from the initiation to post-failure responses, capturing the complex interaction among multiple coupled phases. This represents a significant advancement in the numerical modelling of frozen soils and their thawing-induced failure mechanisms in cold regions.