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English language learning students in China often feel challenged to learn English due to lack of motivation and confidence, pronunciation and grammar difference, lack of practice and people to communicate with etc., which affects students mental health. Adopting Big data and AI will help in overcoming these limitations as it provides personalized guidance to the students in all aspects. The paper has established an automatic early warning system to monitor the students' psychological state at any time period. The data is collected from 650 respondents from four different public universities in China. The data analysis has been done with the help of powerful SPSS software and the methodology which we used for determining sample size is, Random sampling. The study involves a qualitative assessment to identify participants' characteristics and categorize them to appropriate clusters. The findings of the research showed that the most obvious differences in mental health between students who used automatic warning and those who did not use automatic warning were: depression, anxiety, hostility, terror, and psychosis. The proportion of students who use early warning was less than those who did not use early warning. Research contributes to policymakers to emphasize the importance of incorporating mental health support and resources into educational policies. The novelty of the study seeks to provide a deeper understanding of how AI and big data can optimize mental health education for English students. With the support of AI and Big data there is a constant monitoring and improvement effect on English education students' mental health. [ABSTRACT FROM AUTHOR]

Multi-layer slopes are widely found in clay residue receiving fields. A generalized horizontal slice method (GHSM) for assessing the stability of multi-layer slopes that considers the energy dissipation between adjacent horizontal slices is presented. In view of the upper-bound limit analysis theory, the energy equation is derived and the ultimate failure mode is generated by comparing the sliding surface passing through the slope toe (mode A) with that below (mode B). In addition, the influence of the number of slices on the stability coefficients in the GHSM is studied and the stable value is obtained. Compared to the original method (Chen's method), the GHSM can acquire more precise results, which takes into account the energy dissipation in the inner sliding soil mass. Moreover, the GHSM, limit equilibrium method (LEM) and numerical simulation method (NSM) are applied to analyze the stability of a multi-layer slope with different slope angles and the results of the safety factor and failure mode are very close in each case. The ultimate failure modes are shown to be mode B when the slope angle is not more than 28°. It illustrates that the determination of the ultimate sliding surface requires comparison of multiple failure modes, not only mode A. [ABSTRACT FROM AUTHOR]

Owing to information asymmetry, evaluating the credit risk of small- and medium-sized enterprises (SMEs) is difficult. While previous studies evaluating the credit risk of SMEs have mostly focused on intrinsic risk generated by SMEs, our study considers both intrinsic and relational risks generated by neighbor firms' publicly available risk events. We propose a framework for quantifying relational risk based on publicly available risk events for SMEs' credit risk evaluation. Our proposed framework quantifies relational risk by weighting the impact of publicly available risk events of each firm in an interfirm network—considering the impact of interfirm network type, risk event type, and time dependence of risk events—and combines the relational risk score with financial and demographic features to evaluate SMEs credit risk. Our results reveal that relational risk score significantly improves both discrimination and granting performances of credit risk evaluation of SMEs, providing valuable managerial and practical implications for financial institutions. [ABSTRACT FROM AUTHOR]

The paper develops an analytical method to design the plane arrangement of stabilising piles under spatial lateral load of 3D colluvial landslides with multilayer sliding masses. Firstly, an approach based on the geological map and binary Lagrange Interpolation method was presented to establish 3D landslide calculation models. The improved 3D limit equilibrium method was then proposed to calculate the stability coefficient and spatial distributed lateral load of landslide with multilayer sliding masses. And main sliding direction of 3D multilayer landslide can be determined by the developed principle of minimum potential energy. Moreover, through assuming the piles and soil as elastic transversely isotropic continuum medium, analytical approach was employed to accurately calculate the stress distribution of soil between adjacent piles; and the maximum pile spacing can be calculated. Finally, the reasonable arrangement of stabilising piles can be determined based on Monte-Carlo method and cost of all piles. The finite difference method was employed to validate the theoretical results through the case study of Bazimen landslide. And it is found that the proposed method for the design of piles' arrangement is economically beneficial compared with the traditional method on the premise of ensuring safety. [ABSTRACT FROM AUTHOR]

A large-scale obliquely inclined bedding rockslide, activated by a heavy rainstorm, occurred on July 8, 2020, at 7:05 (UTC + 8) in Shiban Village, Songtao Miao Autonomous County, Guizhou Province, China. The loss of life in this event was greatly reduced owing to the local warning system for rainstorm-induced geohazards. To understand the failure characteristics, triggering factors, the genetic mechanism of the landslide, the geomorphological features, geological characteristics, hydrological conditions, and rainfall characteristics were systematically studied by a synthetic approach including field investigations, satellite imagery, unmanned aerial vehicle (UAV) photography, laboratory tests, and rainfall data statistics. The results indicated that the interface between the soft and hard rock, the well-developed joints, and the free face in front of the slope constituted the boundaries of this landslide. The concave topography at the back and southern edge of the landslide, the bare ground, and the cataclastic structure of the rock mass provided favorable conditions for the collection or infiltration of rainwater. The concentrated rainstorm was the direct trigger for the landslide, which led to a rapid inflow and retention of rainfall in the landslide through favorable landform and geological conditions. The groundwater recharge that cannot be drained in time caused the mechanical deterioration of rock mass and induced a rapid increase in pore water pressure in the landslide. Moreover, the water level of the Ganlong River at the toe of the slope also rose rapidly, and the uplift pressure in front of the slope increased accordingly. Under the combined action of these adverse factors, the overall anti-sliding force of the slope was less than the sliding force, finally resulting in the landslide. Remarkably, the local warning system for rainstorm-induced geohazards successfully forecasted the landslide, but the shortcoming is that the forecast time in advance is short. Nevertheless, the prediction has significantly reduced human casualties and provided valuable experience for the prediction of this type of landslide. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An increasing number of landslides are occurring during the construction of highways in mountainous areas all over the world. A recent highway landslide located in the mountainous region of Southwest China is taken as a representative example to carry out the initiation mechanism and deformation characteristics analysis, and study the intelligent partitioning method in this paper. The geological conditions of the landslide are obtained through detailed geological surveys and investigation. A three-dimensional (3D) monitoring network with a total of 72 multi-points, including 53 surface displacement monitoring points and 19 deep displacement monitoring holes, is established to examine the spatial deformation characteristics of the landslide for nearly 25 months. The monitoring results of the surface displacement show that the dynamic deformation of the landslide can be divided into three stages, namely initial deformation stage, accelerated deformation stage, and stabilization stage. Based on the strata distribution obtained by the borehole investigation and the results of deep displacement monitoring, the depth of the slip surface can be reliably determined. The smart contract method in blockchain technology combined with the multi-point monitoring dataset is novelty applied to realize the intelligent automatic partition of the highway landslide without any human involvement. This method can provide the basis for further independent analysis of different landslide zones. According to the investigation and analysis, the initiation mechanism for the highway landslides is attributed to both internal poor geological conditions and external intense precipitation and engineering excavation. [ABSTRACT FROM AUTHOR]

Jurassic facility-sliding strata have been identified as a fundamental factor affecting the occurrence of rainfall-reservoir induced landslides in western Hubei Province, China Three Gorges Reservoir area. Regional landslide susceptibility mapping is the most effective method for landslide prediction and mitigation. To solve the current problem of identifying the true landslides and non-landslides, a novel hybrid model based on the two steps self-organizing mapping-random forest (two steps SOM-RF) algorithm is proposed. The identified high and very high susceptibility zones are located within the hydro-fluctuation belt and regions with low altitude based on the datasets before 2014. Deviation and variance of other ten datasets are generated to evaluate the reliability of the maps for the problem of unbalanced sample sizes. Two typical landslides occurred in 2017 have been found in the very high susceptibility zone, which emphasized the validity of susceptibility mapping. To verify the effectiveness of selecting true landslides and non-landslides based on two steps SOM model, recorded landslides and non-landslides randomly chosen from landslide-free areas are put into the single RF model for comparison. The receiver operating characteristic curve and Accuracy index are applied to compare the performance of the landslide susceptibility maps created based on two steps SOM-RF and the single RF model. The results demonstrate that the consideration of true landslides and non-landslides is effective in producing a more accurate landslide susceptibility map with superior prediction skill and higher reliability. [ABSTRACT FROM AUTHOR]

On 22 October 2018, at 17:20 (UTC+8), a bedding rockslide occurred on the right bank of the Jian-En expressway in Jianshi County, Hubei Province, Central China. Fortunately, a systematic displacement monitoring network provided an early warning. Thus, no casualties were caused by the rockslide, although two transport vehicles and three houses were damaged. To deduce the triggering factors and failure mechanism of the bedding rockslide, the geological characteristics, hydrogeological characteristics, slope excavation scheme, and rainfall data statistics were systematically investigated based upon field investigations, UAV photography, geological drilling, and laboratory tests. It was found that the bedding characteristics, presence of well-developed joints, and properties of the weak interlayers were the internal factors inducing the rockslide. The free face in front of the slope, the weak interlayer with low shear strength, and the through-going joint that formed the back boundary of slope constituted the boundary of the rockslide. The combined effects of improper slope excavation and concentrated rainfall were external factors that directly triggered the rockslide. The well-developed joints provided an effective infiltration channel for rainwater, and continuous rainfall deteriorated the shear strength of the weak interlayer ultimately causing the rockslide. [ABSTRACT FROM AUTHOR]

It is reported that there are many colluvial landslides with multilayered sliding masses; however, previous studies of the pile spacing of stabilizing piles mainly focus on the single-layered sliding mass, which may lead to design errors for pile spacing. Consequently, the paper presents a novel method to determine the pile spacing with considering interactions of multilayered sliding masses in colluvial landslides. Based on a generalized landslide model, equations for calculating stability coefficients of multilayered sliding masses were improved by examining the interactions among sliding masses. An accordingly colluvial landslide model with double-layered sliding masses was established by the finite differential method. The distribution of vertical landslide driving force and horizontal loading between adjacent piles were studied based on the colluvial landslide. A novel method of calculating the maximum pile spacing and minimum pile spacing was deduced by considering the soil arching effect and the interactions among multilayered sliding masses. The reasonable pile spacing was obtained considering cost and performance of stabilizing piles. The calculational process, which determines optimal pile spacing in multilayered masses, were shown based on the Bazimen landslide. The variations in pile spacing affected by various soil-layer sequences was illustrated by employing the Bazimen landslide model. The calculation results indicate that the pile spacing is positively correlated with the depth of soil with the maximum resistance sliding force. Effectiveness and significance of the presented method were proved through verify the calculational results by using numerical modeling approaches. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to better understand the movement characteristics and patterns of a sudden landslide that occurred in the Three Gorges Reservoir, China (the Sept. 2, 2014 Shanshucao landslide) by using three periods of multi-source remote sensing images in combination with a field survey. A georeferenced, aerophotographic orthomosaic and corresponding 1:2000 topographic map of the pre-landslide area was referenced to rectify the micro-unmanned aerial vehicle (UAV) based orthomosaic, digital surface model (DSM) and Google Earth satellite image of the post-landslide area. By using orthomosaics of the post-landslide area, especially the very high spatial resolution micro-UAV based orthomosaic, the landslide boundaries were accurately delineated. By comparing the images of the area both pre- and post-landslide, three landslide subzones were identified, and two boundaries between the three subareas were divided more precisely than in previous studies. Based on the digital elevation model (DEM) of the pre-landslide area and DSM of the post-landslide area, the change in altitude and volume of the landslide were quantified. Then, the movement pattern of the landslide was proposed. The results showed the rocky, southern sliding part was the main sliding zone with the longest distance and deepest sliding surface, and the northern soil mass did not experience any overall movement. However, loose soil mass collapse and features of counterclockwise rotation were observed under the traction of the rocky, southern sliding part. Additionally, more accurate knowledge has been obtained including the location of the original lateral boundary, shear outlet altitudes, and main triggering factors of the rocky sliding mass. This study has demonstrated that the use of multi-source remote sensing images, which include both pre- and post-landslide images, can effectively describe the characteristics, patterns, and even the mechanism of a sudden landslide. [ABSTRACT FROM AUTHOR]

Monitoring has been considered to be the most effective means of preventing and mitigating geo-hazards. Unmanned aerial vehicle (UAV) based remote sensing has been proven to be a viable method for monitoring relatively flat areas, but has typically encountered challenges on steep mountainous slopes. The Qinglingou Slope, which is located at the head of the Three Gorges Reservoir, China, was selected as a case study, and a UAV-based remote sensing method was applied to monitor this steep mountainous slope. The UAV, which was equipped with a camera, was used to acquire images on May 10 and Sept. 13, 2015. At the same time, a plan for establishing and measuring a detailed network of ground control points (GCPs) and check points was carefully designed and implemented, according to the local conditions. Georeferenced digital surface models (DSMs) and digital orthophotos were obtained by photogrammetric processing. Based on the results, a simple and quick method that identified and quantified slope deformation by calculating and analyzing the changes in the DSMs was developed. The results showed that the UAV-based remote sensing method was rapid, safe and effective, and it could be valuable for monitoring a large number of steep mountainous slopes in the Three Gorges Reservoir. [ABSTRACT FROM AUTHOR]