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Non-ordinary state-based peridynamics (NOSB-PD) has been widely applied to rock fracture modeling due to its compatibility with continuum mechanics. However, the classical formulation frequently produces zero-energy modes and nonphysical displacement oscillations as a result of insufficient local deformation compatibility. Moreover, the direct use of classical shear failure criteria often leads to inconsistencies between simulated and experimentally observed shear crack paths. To address these issues, this study employs a bond-level deformation gradient constructed from the averaged deformation gradient at the bond scale, which restores local compatibility and effectively suppresses zero-energy modes. In addition, a triple shear energy criterion is incorporated to distinguish different shear-related mechanical states, thereby improving the physical consistency of shear crack identification and the accuracy of crack path prediction. The improved model is examined through three benchmark numerical examples and simulations of fractured rock. The results demonstrate that it can accurately reproduce crack initiation, propagation, and coalescence under complex loading conditions, while maintaining numerical stability and requiring minimal parameter calibration. Compared with existing NOSB-PD implementations, the approach offers a clearer mathematical structure and enhanced predictive capability, providing a reliable and applicable numerical tool for crack evolution analysis in rock materials.

Open rock masses are subject to prolonged loading and freeze-thaw cycles in cold regions. In this study, we take saturated fissured red sandstone as object to investigate the long-term mechanical response characteristics of fractured rock under freeze-thaw conditions. Experimental tests were conducted to analyze the creep characteristics of the rock after freeze-thaw cycles, considering different freeze-thaw frequencies and fracture orientations. The results reveal that (1) freeze-thaw cycles exert a significant influence on the rock’s creep behavior, with axial strain, instantaneous strain, and creep strain increasing progressively with the number of freeze-thaw cycles; (2) dual-fractured rock samples with varying fracture angles exhibit distinct differences in creep phenomena, where increased fracture angles result in pronounced increases in instantaneous and creep strains, and higher horizontal stress levels lead to greater strain generation; (3) all rock samples with different pre-existing fractures exhibit rock bridge breakthrough during creep failure, and the variation in fracture angle affects the failure mode; (4) and the long-term strength of the rock varies with changes in fracture angle and freeze-thaw cycle frequency, showing an increasing trend with greater fracture angles but a rapid decrease with increasing freeze-thaw cycles. These findings provide valuable insights for engineering design and risk assessment of open rock masses in cold regions. This study has guiding significance for the safety construction of rock mass engineering in cold regions.

Rock masses in cold regions deteriorate due to frost heave caused by fissure water, posing risks to engineering projects. This study investigates the long-term mechanical behavior of fully saturated fissured red sandstone under freeze–thaw conditions. Creep acoustic emission (AE) experiments were conducted to explore how freeze–thaw cycles and fissure dip angles influence rock creep and AE characteristics. Four freeze-thaw cycle levels (0, 30, 60, and 90) and three double-fissure orientations (15°–15°, 15°–75°, and 75°–75°) were examined in this study. Results show that: (1) Increasing freeze–thaw cycles lead to greater instantaneous and creep strains, higher AE signal amplitudes, exponential growth in AE energy rates, and faster b-value fluctuations. (2) With larger fissure dip angles, the instantaneous and creep strains significantly increase, while the amplitude density, intensity, and AE energy rates decrease. b-value fluctuations also slow down. (3) Abrupt b-value changes can predict freeze–thaw-induced rock failure. For intact samples subjected to 90 freeze–thaw cycles, intact non-freeze–thaw samples, and fissured non-freeze–thaw samples, b-value mutations occurred 12.96 s, 35.28 s, and 48.24 s earlier, respectively. These findings highlight b-value changes as an early indicator of freeze–thaw damage and provide theoretical insights into the creep failure of fissured rock masses under such conditions, aiding in the design and safety of cold-region engineering.

The flexural toppling failure of anti-dip rock slopes (ADRSs) may happen under the action of external loads. Evaluating the stability of ADRSs subjected to external loads can guide slope protection and engineering construction. In this paper, the failure modes and failure surface of ADRSs are determined based on the experiments and numerical simulations. In the framework of the limit equilibrium method and cantilever beam model, an analytical model for assessing the stability of ADRSs is proposed. Then, the effects of the loading length, thickness of rock layer, strength parameters of persistent discontinuity and critical tensile strength of intact rock layer on the stability of ADRSs are discussed. It is found that the thickness of rock layer, cohesion and friction angle of the persistent discontinuity and critical tensile strength of intact rock layer significantly enhance the stability of ADRSs, while the external loads and loading length acting on the top edge are harmful to the stability of ADRSs. In addition, the correctness and practicality of the proposed method are verified by two typical cases. Factor of safety from the proposed method are consistent with those from the previous studies.

The responses of phytoplankton to nutrients vary for different natural bodies of water, which can finally affect the occurrence of phytoplankton bloom. However, the effect of high alkalinity characteristic on the nutrient thresholds of natural alkaline lake is rarely considered. Bioassay experiments were conducted to investigate the nutrient thresholds and the responses of phytoplankton growth to nutrients for the closed plateau Chenghai Lake, Southwest China, which has a high pH background of up to 9.66. The growth of the phytoplankton community was restricted by phosphorus without obvious correlation with the input of nitrogen sources. This can be explained by the nitrogen fixation function of cyanobacteria, which can meet their growth needs for nitrogen. In addition, nitrate nitrogen (NO3-N) could be utilized more efficiently than ammonia nitrogen (NH4-N) for the phytoplankton in Chenghai Lake. Interestingly, the eutrophication thresholds of soluble reactive phosphorus (SRP), NH4-N, and NO3-N should be targeted at below 0.05 mg/L, 0.30 mg/L, and 0.50 mg/L, respectively, which are higher than the usual standards for eutrophication. This can be explained by the inhibition effect of the high pH background on phytoplankton growth due to the damage to phytoplankton cells. Therefore, the prevention of phytoplankton blooms should be considered from not only the aspect of reducing nutrient input, especially phosphorus input, but also maintaining the high alkalinity characteristic in natural alkaline lake, which was formed due to the geological background of saline-alkali soil.

Progressive damage evolution in rock masses serves as the fundamental mechanism driving geological hazards by controlling deformation patterns and failure predictability. To address the critical challenge of predicting fracture behaviors in heterogeneous geological media, this study pioneers the integration of real-time computed tomography (CT) scanning and acoustic emission (AE) monitoring to investigate self-organized criticality and fracture predictability in Cretaceous sandstone under uniaxial compression. By systematically analyzing internal structural evolution and damage parameters, this established a multiparameter framework to characterize self-organized processes and critical phase transitions during progressive fracturing. Key findings include the following: (1) Distinct critical thresholds emerge during yield-stage self-organization, marked by abrupt transitions in AE signals and crack metrics—from microdamage coalescence initiating volumetric expansion (first critical point) to macrocrack nucleation preceding peak strength (second critical point). (2) AE-crack evolution follows power–law statistics, where elevated scaling exponents (r > 0.85) correlate with intensified nonlinear damage, accelerated localization, and progressive rate enhancement. Yield-stage power–law acceleration provides quantifiable failure precursors. (3) Yield-stage damage patterns exhibit 85% similarity with terminal failure configurations, confirming yield-stage as the definitive precursor with critical temporal signatures for failure prediction. A conceptual framework integrating multiparameter responses (AE signals, crack metrics) was developed to decipher self-organized critical phase transitions during deformation-failure processes. This work establishes methodological foundations for investigating damage mechanisms and predictive strategies in heterogeneous rock systems.

With the rapid increase in the number of drivers, traffic accidents due to driver distraction is a major threat around the world. In this paper, we present a novel long-term recurrent convolutional network (LRCN) model for real-time driver activity recognition during both day- and nighttime conditions. Unlike existing works that use static input images and rely on major pre-processing measures, we employ a TimeDistributed convolutional neural network (TimeDis-CNN) layer to process a sequential input to learn the spatial and temporal information of the driver activity without requiring any major pre-processing effort. A pre-trained (CNN) layer is applied for robust initialization and extraction of the primary spatial features of the sequential image inputs. Then, a long short-term memory (LSTM) network is employed to recognize and synthesize the dynamical long-range temporal information of the driver’s activity. The proposed system is capable of detecting nine types of driver activities: driving, drinking, texting, smoking, talking, controlling, looking outside, head nodding, and fainting. For evaluation, we utilized a real vehicle environment and collected data from 35 participants, where 14 of the drivers were in real driving scenarios and the remaining in non-driving conditions. The proposed model achieved accuracies of 88.7% and 92.4% for the daytime and nighttime datasets, respectively. Moreover, the binary classifier’s accuracy in determining whether the driver is non-distracted or in a distracted state was 93.9% and 99.2% for the daytime and nighttime datasets, respectively. In addition, we deployed the proposed model on a Jetson Xavier embedded board and verified its effectiveness by conducting real-time predictions.

The failure of rock in cold regions due to repeated freeze–thaw (F-T) cycles and periodic load-induced fatigue damage presents a significant challenge. This study investigates the evolution of the multi-scale structure of fractured granite under combined freeze–thaw (F-T) cycles and periodic loading and develops a constitutive damage model. The results indicate that after F-T cycles, network cracks develop around pre-existing cracks, accompanied by block-like spalling. After applying the fatigue load, the nuclear magnetic resonance (NMR) T2 spectrum shifts to the right, significantly increasing the amplitude of the third peak. The freeze–thaw process induces a “liquid–solid” phase transition, weakening the original pore structure of the rocks and leading to meso-damage accumulation. The pores in fractured granite progressively enlarge and interconnect, reducing the rock’s load-bearing capacity and fatigue resistance. The combined effects of F-T cycles and periodic loading induce particle movement and alter fracture modes within the rock, subsequently affecting its macro-damage characteristics. The theoretical curves of the constitutive model align with the experimental data. The findings can serve as a theoretical reference for preventing and controlling engineering disasters in fractured rock masses in cold regions.

What are the main findings? First successful biomass estimation in Olea europaea L. using integrated UAV-RGB and U2-Net. U2-Net combined with UAV-RGB images accurately extracted Olea europaea L. CA. What are the implications of the main findings? This study developed a high-accuracy biomass estimation model for Olea europaea L., providing critical technical support for the cultivation management and carbon sequestration assessment of this economically important species. By innovatively integrating UAV imagery with the U2-Net deep learning method, efficient and automated canopy extraction and biomass monitoring were achieved, demonstrating significant potential for broad application. Olea europaea L. is an economically and ecologically significant species, for which accurate biomass estimation provides critical insights for artificial propagation, yield forecasting, and carbon sequestration assessments. Currently, research on biomass estimation for Olea europaea L. remains scarce, and there is a lack of efficient, accurate, and scalable technical solutions. To address this gap, this study achieved, for the first time, non-destructive estimation of Olea europaea L. biomass across individual tree to plot scales by integrating UAV-RGB (Unmanned Aerial Vehicle-Red-Green-Blue) imagery with the U2-Net model. This study initially developed allometric models for W-D-H, CA-D, and CA-H in Olea europaea L. (where W = biomass, D = ground diameter, H = tree height, and CA = canopy area). A single-parameter CA-based whole-plant biomass model was subsequently developed utilizing the optimal models. An innovative whole-plant biomass estimation model (UAV-RGB, U2-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U2-Net at the sample-plot level was developed and assessed. The results revealed the following: (1) The model for Olea europaea L. aboveground biomass (AGB) was WA = 0.0025D1.943H0.690 (R2 = 0.912), the model for belowground biomass (BGB) was WB = 0.012D1.231H0.525 (R2 = 0.693), the model for CA-D was D = 4.31427C0.513 (R2 = 0.751), CA-H model was H = 226.51939C0.268 (R2 = 0.500). (2) The optimal AGB model for CA single-parameter was WA = 1.80901C1.181 (R2 = 0.845), and the model for BGB was WB = 1.25043C0.772 (R2 = 0.741). (3) The R2 of Olea europaea L. biomass, as estimated by CA derived from the U2-Net and UUTB models, was 0.855. This study presents the first integration of UAV-RGB imagery and the U2-Net model for biomass estimation in Olea europaea L., which not only addresses the research gap in species-specific allometric modeling but also overcomes the limitations of traditional manual measurement methods. The proposed approach provides a reliable technical foundation for accurate assessment of both economic yield and ecological carbon sequestration capacity.