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Natural rock often suffers from cyclic wetting–drying involving different water types, and the resulting deterioration may differ from laboratory tests using distilled water or salt solutions. An inappropriate estimation of this deterioration effect may lead to fatal geological hazards and engineering failures. A multiscale study is conducted to investigate the physical and mechanical features of sandstone in Three Gorges Reservoir region (TGR sandstone) subjected to cyclic wetting–drying of Yangtze River water. During this study, three types of water, i.e., Yangtze River water, ionized water having similar ion compositions as the Yangtze River water, and distilled water, are used for comparison. The results show that the multiscale physical properties including mineral compositions (especially calcite and albite), micro-pore parameters, computed tomography values, and macro-mechanical parameters (i.e., Young’s modulus, uniaxial compression strength and tensile strength) are remarkably altered during the cyclic wetting–drying process. Significant correlations are found between these numerous multiscale properties. The results indicate that changes of mineral compositions and microstructure are the primary reasons for the deterioration of sandstone strength. The deterioration effect of distilled water on TGR sandstone is the least, while the effect of ionized water is the greatest, and that of river water being intermediate. These differences are ascribed to different chemical interactions, together with possible microorganism effects for river water, as microorganisms in river water potentially weaken the deterioration of cyclic wetting–drying of river water. In situ water is recommended for studying how rock properties are affected by water–rock interactions in real settings.

Accurate estimation of slope stability based on numerous candidate estimation methods is difficult as different results may be yielded. It becomes even more challenging when only limited data of geotechnical parameters (e.g., shear strength parameters) are available to evaluate slope reliability. Based on the Bayesian sequential updating technology, a hybrid framework for slope reliability was proposed in this study, through which prior knowledge, multiple estimation methods, and corresponding model uncertainties could be integrated to estimate slope reliability using a small amount of geotechnical data. Three slope examples with various stratigraphic configurations and soil properties were used to illustrate the accuracy and efficiency of the proposed framework, during which the Bishop’s simplified method, the upper bound limit analysis method, and the finite element method were adopted. The results showed that with results of direct Monte Carlo simulation based on each method as the benchmark, a compromised mean of the factor of safety (μFS), and conservative standard deviation of the factor of safety (σFS) and failure probability (Pf) were yielded through the proposed framework. When the sample size of geotechnical parameters was greater than a threshold, the estimated μFS was stable, while the σFS and Pf synchronously varied within a small range with the increase in sample size. Demonstrations of the three examples indicated that the proposed hybrid framework can provide reliable and accurate estimations of slope reliability. The proposed framework may serve as a promising vehicle for slope/landslide engineering including failure and preventative mechanisms, movement prediction, and back analysis of geotechnical parameters in a probabilistic context, and big data analysis of geological and geotechnical problems as well.

BackgroundGiven the lack of targeted therapies and frequent resistance to apoptosis-based treatments, triple-negative breast cancer (TNBC) remains a major clinical challenge. Exploring non-apoptotic cell death mechanisms may offer new therapeutic avenues to circumvent drug resistance in TNBC.MethodsThe anticancer activity of a novel cyclometalated iridium (III) compound, CIr2, was evaluated using cytotoxicity, clonogenic, and migration assays in multiple breast cancer cell lines. Mechanistic investigations included analyses of mitochondrial dysfunction, reactive oxygen species (ROS) production, ATP depletion, endoplasmic reticulum (ER) stress, and MAPK signaling. Transcriptomic profiling (RNA-seq), ultrastructural and morphological analyses, as well as pharmacological inhibitor studies targeting distinct cell death pathways, were performed to elucidate the mode of cell death induced by CIr2. The in vivo antitumor efficacy and safety of CIr2 were further assessed using a TNBC xenograft mouse model.ResultsCIr2 selectively inhibited the proliferation and migration of TNBC cells while exerting minimal cytotoxic effects on normal breast epithelial cells. CIr2 preferentially accumulated in mitochondria, leading to mitochondrial membrane potential collapse, excessive ROS production, and profound ATP depletion. Transcriptomic profiling and morphological analyses revealed pronounced ER stress, MAPK pathway activation, and paraptosis-associated ultrastructural alterations, including mitochondrial swelling and extensive cytoplasmic vacuolization. Pharmacological inhibition of apoptosis, necroptosis, ferroptosis, autophagy, ER stress, or p38 MAPK signaling failed to rescue CIr2-induced cytotoxicity, whereas ROS scavenging effectively reversed these effects, confirming a mitochondrial dysfunction and ROS-driven paraptotic mode of cell death. In vivo, CIr2 markedly suppressed TNBC xenograft tumor growth with minimal systemic toxicity.ConclusionCIr2 induces paraptosis through mitochondrial dysfunction and ER stress, offering a potential therapeutic strategy to overcome apoptosis resistance in TNBC. These findings provide a new mechanistic insight into iridium-based paraptosis induction.

HighlightsA novel BAS-DNN model for prediction of TBM advance rate is developed.A database involving geotechnical and operating risks (RMR, TWCR, and RI) is set up.Quadratic relationships exist between RMR, TWCR, and AR.AR decreases while the descending rate gradually increases with RI increasing.The BAS-DNN model shows good performance in the tunnel section with a jamming event.

Zigui Basin is a major landslide-prone region in the Three Gorges Reservoir region of China, and the stabilizing pile is an effective and widely employed countermeasure to reinforce landslides in this region. However, stabilizing piles are mostly designed using deterministic and stability-oriented methods, which generally ignore the system performance and cost-effectiveness. Using the Majiagou landslide reinforced with stabilizing piles as a case study, a probabilistic multi-objective optimization framework for the design of stabilizing piles is proposed and illustrated. Specifically, performance objectives related to failure probability, system robustness and life-cycle cost of the landslide-stabilizing pile system with feasible designs are evaluated, then the best compromised design is obtained by means of Pareto optimality. Expert knowledge and professional judgment are required to set necessary restrictions and finally determine the optimal design. The results show that there is a better design of stabilizing piles than the existing one, with which acceptable reinforcement effectiveness, compromised life-cycle cost and robust system performance can be realized. The optimal design will also vary with the concerned performance objectives and knowledge-based judgment. Further relationships and interpretations between design parameters and system responses are discussed through parametric analyses.

Rock bolts are widely employed as an effective and efficient reinforcement method in geotechnical engineering. Sandwich composite structures formed by hard rock and weak rock are often encountered in practical projects. Furthermore, the spatial structure of the rock mass has a direct influence on the effect of the anchorage support. To investigate the impact of rock mass structure on the mechanical characteristics of anchorage interfaces, pull-out tests on reinforced specimens with different mudstone thicknesses and fracture dip angles are conducted. The experimental results indicate that the percentage of mudstone content and fracture dip angle have a significant influence on the pullout load of the samples. A weaker surrounding rock results in a lower peak load and a longer critical anchorage length, and vice versa. The results also show that 70% mudstone content can be considered a critical condition for impacting the peak load. Specifically, the percentage of mudstone content has a limited influence on the variation in the peak load when it exceeds 70%. Optical fiber deformation results show that compared to the rock mass with fracture dip angles of 0° and 60°, the rock mass with a fracture dip angle of 30° has a more uniformly distributed force at the anchorage interface. When the fracture dip angle exceeds 60°, the dip angle is no longer a key indicator of peak load. The accuracy of the experimentally obtained load-displacement curves is further verified although numerical simulation using the discrete element method.

Based on the machine learning algorithms, prediction models for landslide displacement with step-like behavior in the reservoir area were established for landslides prevention and reduction; these models could predict a given test set very well. However, due to the length and the sequence of the training set in prediction models, the predictive ability of these prediction models could not be evaluated accurately if just validated with a given test set. To solve the problem, a hybrid reliability model was proposed. Complimentary ensemble empirical mode decomposition (CEEMD) algorithm was used to decompose the accumulated displacement into the trend displacement and the periodic displacement firstly. The Gauss function was used to predict the trend displacement, and the random forest (RF) and back propagation neural network (BPNN) algorithms were employed to predict the periodic displacement. Furthermore, a novel performance function for the reliability analysis of the displacement prediction model was derived to address failure probabilities in different cases. The Baijiabao landslide in the Three Gorges Reservoir Area was taken as an example for reliability analysis of the prediction model for landslide displacement with step-like behavior, and the predictive ability of the CEEMD-RF model and the CEEMD-BPNN model were compared. The results indicated that the CEEMD-RF model and CEEMD-BPNN model both can accurately predict the accumulated displacement of the given test set; the predictive value obtained with the CEEMD-RF model or the CEEMD-BPNN model showed uncertainties of the prediction model for landslide displacement and the predictive ability of the CEEMD-RF model was more reliable than the CEEMD-BPNN model under different cases. The failure probability proposed in the paper could evaluate the predictive ability of the model more accurately and comprehensively compared with the existing assessment indices.

The strength parameters and the size effect of stochastic jointed limestone rock mass is investigated in this paper. Based on extensive statistics of joint parameters of rock mass in the research region, the probable distribution of geometric characteristic parameters of discontinuities are obtained by the probability graph method. Then the Monte-Carlo method is used for discontinuities network modeling. In addition, 3DEC software and its built-in FISH programming language are used to establish the stochastic jointed rock mass network model based on discrete element method. Triaxial numerical simulation tests under variable confining pressure are conducted with different model sizes and dip angles of bedding planes. The numerical simulation results indicate that the jointed rock mass exhibits weak anisotropy property and significant size effect when it is cut by stochastic discontinuities; the mechanical strength parameters of rock mass begins to fluctuate distinctly as the model size increases, and tend to be stable once the model size reaches or exceeds 4 m × 4 m × 8 m. Besides, the comprehensive mechanical parameters of rock mass in the research region are determined and failure modes of rock mass are analyzed as well based on the numerical simulation results.

Rainfall and reservoir level fluctuation are two of the main factors contributing to reservoir landslides. However, in China’s Three Gorges Reservoir Area, when the reservoir water level fluctuates significantly, it comes at a time of abundant rainfall, which makes it difficult to distinguish which factor dominates the deformation of the landslide. This study focuses on how rainfall and reservoir water level decline affect the seepage and displacement field of Baijiabao landslide spatially and temporally during drawdown of reservoir water level in the Three Gorges Reservoir Area, thus exploring its movement mechanism. The monitoring data of the landslide in the past 10 years were analyzed, and the correlation between rainfall, reservoir water level decline, and landslide displacement was clarified. By the numerical simulation method, the deformation evolution mechanism of this landslide during drawdown of reservoir water level was revealed, respectively, under three conditions, namely, rainfall, reservoir water level decline, and coupling of the above two conditions. The results showed that the deformation of the Baijiabao landslide was the coupling effect of rainfall and reservoir water level decline, while the latter effect is more pronounced.

Conclusion This study pioneers the application of the distributed deep learning (DDL) strategy for landslide displacement prediction and proposes an innovative DDL-CFNN model for accurate forecasting of landslide deformation within the constraints of data silos. The DDL-CFNN model enables multiple landslide institutions to collaboratively train their prediction models through the communication of model features instead of exchanging raw data. This approach allows each institution to enhance the performance of its prediction model by indirectly expanding the training dataset through useful information shared from others. The effectiveness of the DDL-CFNN model is demonstrated through a case study of the Baijiabao landslide in the Three Gorges Reservoir Area (TGRA), showcasing its ability to accurately predict landslide deformation under data silo conditions. Notably, the DDL-CFNN model outperforms traditional DL models that are trained individually reducing prediction error by approximately 62%–76% across all evaluated scenarios. The proposed method holds significant potential for improving the prediction and forecasting of geological hazards amidst data silos and can provide crucial data support for the development of landslide geohazard control strategies.