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Industrial robot machining systems are used extensively in processing and manufacturing sectors including aerospace, large ships, rail transit, engineering machinery, semiconductor processing, medical equipment, and new energy vehicles. However, chatter during the machining process can considerably diminish the machining accuracy and surface quality. In severe cases, it can cause damage to the tool and the machining robot. Robot machining chatter research is required to improve the stability of the machining process for high-efficiency, high-quality robotic machining. This review comprehensively summarizes research progress in three areas: robot machining chatter mechanisms, chatter stability prediction and online monitoring, and chatter suppression strategies. First, the generation mechanisms of regenerative chattering and modal-coupling chattering are clarified. Second, the methods of chattering stability prediction and online monitoring of robot machining are summarized. The differences between passive suppression, semi-active suppression, and active suppression strategies are explained. Existing problems and future directions in robot machining chattering research are summarized and prospected. [ABSTRACT FROM AUTHOR]

Accurate milling stability prediction is necessary for selecting chatter-free machining parameters to ensure the machining quality. With the development of machine learning techniques, data-driven methods have demonstrated powerful modelling capabilities for stability prediction. However, the significant performance of data-driven modelling usually requires a large labelled training dataset consisting of stable and unstable experimental data, which is expensive and time-consuming for metal-cutting scenarios. Therefore, how to design an experimental parameter set to build the experimental labelled dataset which is small but can provide sufficient support for data-driven stability prediction has been a critical problem and has received increasing attention. Existing research samples the experimental parameters by the grid or the boundary method, which inevitably brings lots of low-value data points for model training. To address this, this paper proposes a Physics-Guided High-Value (PGHV) data sampling method to reduce the required experiments for data-driven stability prediction. A novel value function is designed based on the physics information of milling dynamic stability to quantify the potential contribution of different experimental parameters. The optimal experimental parameter set can then be determined by maximising the dataset value. After that, the experimental labelled dataset can be constructed by performing cutting experiments under the sampled experimental parameters. Finally, the stability prediction model can be obtained by the data-driven modelling method with the experimental labelled dataset. Experimental verification shows that the proposed method can reduce the number of experiments by more than 60% compared to the existing sampling methods. [ABSTRACT FROM AUTHOR]

Abstract Serial industrial robots are widely in demand in the field of large-scale component processing and manufacturing. However, the structure characteristic of serial robots makes it easy to generate chatter during milling and the overhang length of milling cutter has an important influence on the stability of robotic milling. Milling cutter overhang length refers to the length from the tip of the cutter to the protrusion of the spindle, to study the influence of milling cutter overhang length on the stability behavior of robotic milling, the modal parameters of milling cutter under different overhang length were obtained by hammer experiment, and its dynamic characteristics were analyzed. The stability behavior for robotic milling under different cutter overhang lengths was analyzed by using the stability lobe diagrams, and the stability lobe diagrams were verified by robotic milling experiments. The results show that the rigidity and natural frequency of milling cutter decrease with the increase of overhang length. There is a big gap between the stability lobe diagrams and the actual machining state when the cutter overhang length is short, and the low frequency vibration is easy to occur in the robotic milling process when the spindle speed is low, which is most probably because that the absorbed vibration energy by the cutter has been transferred to the robot body before the milling cutter vibrates. When the cutter overhang is increased and the feed direction is along the y-axis of the robot global coordinate system, the stability lobe diagrams by considering the multi-mode coupling effect is more consistent with the actual milling state.

Prediction of rock slope stability is difficult and complicated owing to the variability of material parameters and the presence of discontinuities in the form of joints. Appropriate prediction tools are necessary for evaluating the susceptibility of jointed rock slopes to specific modes of failure. This paper presents a methodology for using artificial neural networks (ANN) to predict the susceptibility of rock slopes to planar failure. A dataset has been created with unit weight, cohesion of the rock material, joint wall compressive strength, joint roughness coefficient, and residual friction angle of the joint planes as the input parameters and the critical strength reduction factor (SRFc) as the output from 220 slope models simulated using the finite element method. With a hidden layer size of 10, the ANN model has shown maximum efficiency. The predicted values were observed to be in close proximity to the target values. An overall correlation coefficient of 0.99692 and mean squared error of 0.00039 suggest that the developed neural network has competently modelled the relationship between the input and the output features. The joint roughness coefficient (JRC) has the maximum correlation with SRFc. Simple regression analysis between JRC and the predicted values of SRFc has validated its influence on planar failure in the slope model. [ABSTRACT FROM AUTHOR]

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body-surrounding rock combination under high-stress conditions. Current monitoring data processing methods cannot fully consider the complexity of monitoring objects, the diversity of monitoring methods, and the dynamics of monitoring data. To solve this problem, this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill-surrounding rock combinations. The three-dimensional monitoring system of a large-area filling body-surrounding rock combination in Longshou Mine was constructed by using drilling stress, multipoint displacement meter, and inclinometer. Varied information, such as the stress and displacement of the filling body-surrounding rock combination, was continuously obtained. Combined with the average mutual information method and the false nearest neighbor point method, the phase space of the heterogeneous information of the filling body-surrounding rock combination was then constructed. In this paper, the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body-surrounding rock combination. The evaluated distances (ED) revealed a high sensitivity to the stability of the filling body-surrounding rock combination. The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine. The moments of mutation in these time series were at least 3 months ahead of the roadway return dates. In the ED prediction experiments, the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models (long short-term memory and Transformer). Furthermore, the root-mean-square error distribution of the prediction results peaked at 0.26, thus outperforming the no-prediction method in 70% of the cases. [ABSTRACT FROM AUTHOR]

Micro-milling, widely employed across various fields, faces significant challenges due to the small diameter and limited stiffness of its tools, making the process highly susceptible to cutting chatter and premature tool breakage. Ensuring stable and safe cutting processes necessitates the prediction of chatter by considering the tool breakage. Crucially, the modal parameters of the spindle–holder–tool system are important prerequisites for such stability prediction. In this paper, the frequency response functions (FRFs) of the micro-milling tool are calculated by direct FRFs of the micro-milling cutter and cross FRFs between a point on the shank and one on the tool tip. Additionally, by utilizing a cutting force model specific to micro-milling, the bending stress experienced by the tool is computed, and the tool breakage curve is subsequently determined based on the material's permissible maximum allowable stress. The FRFs of the micro-milling tool, alongside the tool breakage curve, are then integrated to generate the final stability lobe diagrams (SLDs). The effectiveness and reliability of the proposed methodology are confirmed through a comprehensive series of numerical and experimental validations. [ABSTRACT FROM AUTHOR]

Perovskite light‐emitting diodes (LEDs) with advantages of high electroluminescence efficiency at high brightness, good color purity, and tunable bandgap, are believed to have potential applications in the next generation display and lighting technologies. Due to the complex degradation process, mathematic models to describe the degradation process of perovskite LEDs are absent. In this work, it is found that the mathematical fitting methods which have been widely used to describe the decay trend of organic LEDs and quantum‐dot LEDs, are unable to accurately predict the lifetime of perovskite LEDs. Then an ensemble machine learning model is developed, which utilizes data augmentation technique to predict T50 of perovskite LEDs based on features before T80, achieving an accuracy of 0.995. Furthermore, the model can also accurately predict the T90 lifetime of quantum‐dot LEDs (QLEDs) using features before T98, suggesting it is a useful tool to efficiently evaluate LED lifetimes. [ABSTRACT FROM AUTHOR]

In the rapidly developing field of biomedical research, the search for new materials with improved properties is crucial to moving the entire field forward. Double phosphates have generated significant interest in a wide range of applications, ranging from drug delivery systems to catalysts for biomedical reactions, and the fields of biomedicine and tissue engineering are no exception. In this article, we propose a method for finding new double phosphate materials, which is based on machine learning, screening, and applying data from structural databases, and use this methodology combined with chemical knowledge to propose several promising materials for bone tissue engineering. For the selected candidates, we develop a solid-phase synthesis procedure and apply their physical characteristics to confirm the results. In addition, the role of morphology, that is, the porosity of frameworks based on these materials, is discussed from a biomedical point of view, and several synthetic ways to adjust this parameter are proposed and investigated. [ABSTRACT FROM AUTHOR]

G protein‐coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time‐consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR‐tm, a novel and personalized structurally driven web‐based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR‐tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR‐tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10‐fold cross‐validation and blind test sets, respectively. We observed that the (structural) graph‐based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR‐tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR‐tm is available through a user‐friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/. [ABSTRACT FROM AUTHOR]

Ball end milling cutters are commonly used for precision machining of complex curved parts in five axis CNC systems. To solve the problem of difficulty in adjusting the spindle speed to achieve stable milling during the five axis milling process of ball end cutters, this paper presents an analytical method for extracting the tool-workpiece engagement (TWE) from computer-aided manufacturing (CAM) data. This method extracts the TWE between the end portion of the ball-end mill and the surface of the workpiece during the processing of free-form surfaces, including slotting, first cutting, and following cutting. Further, the TWE extracted by this method is applied for constructing the cutting force model. The entry and exit angles for each of the micro-element cutting edges under different tool postures are obtained through the conversion matrix, and an efficient full discretization method is used for predicting the stability of the five-axis machining in the slotting mode. Finally, the accuracy and efficiency of the analytical method are verified through simulation and experimentation. The experimental results show that the efficiency of the proposed method increases by about 69% when compared to the arc-surface intersection method and that the predicted value of the five-axis machining stability shows good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

The underground powerhouse of the Shuangjiangkou hydropower station is one of the largest caverns under construction in China, and its stability during construction is crucial for safe construction. To study the stability of the surrounding rock during excavation, the displacement and stress of the surrounding rock were monitored by multi-point displacement meters and bolt stress meters. Based on the monitoring data, the elastic modulus, Poisson's ratio, friction angle, and cohesion of surrounding rock were inversely analyzed by the PSO-BP algorithm. Then, the back-analyzed parameters were used to simulate the subsequent excavations and predict the stability of surrounding rock during the following construction. The analysis results show that the surrounding rocks were generally stable during the initial four stages of excavation, and the main factors affecting their stability were blasts and unfavorable geological structures, including the lamprophyre vein and the F1 fault. These unfavorable geological structures also significantly decrease the mechanical parameters of surrounding rock as demonstrated by back analysis, and the stability prediction results show that the omnibus bar cave and the tailrace tunnel were at the greatest risk of instability during the subsequent excavations. This study provides a practical analysis for engineering excavation of the underground caverns. [ABSTRACT FROM AUTHOR]

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid's stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method. [ABSTRACT FROM AUTHOR]

Perovskite light‐emitting diodes (LEDs) with advantages of high electroluminescence efficiency at high brightness, good color purity, and tunable bandgap, are believed to have potential applications in the next generation display and lighting technologies. Due to the complex degradation process, mathematic models to describe the degradation process of perovskite LEDs are absent. In this work, it is found that the mathematical fitting methods which have been widely used to describe the decay trend of organic LEDs and quantum‐dot LEDs, are unable to accurately predict the lifetime of perovskite LEDs. Then an ensemble machine learning model is developed, which utilizes data augmentation technique to predict T50 of perovskite LEDs based on features before T80, achieving an accuracy of 0.995. Furthermore, the model can also accurately predict the T90 lifetime of quantum‐dot LEDs (QLEDs) using features before T98, suggesting it is a useful tool to efficiently evaluate LED lifetimes.

In humans, FOXP gene family is involved in embryonic development and cancer progression. The FOXP4 (Forkhead box protein P4) gene belongs to this FOXP gene family. FOXP4 gene plays a crucial role in oncogenesis. Single nucleotide polymorphisms are biological markers and common determinants of human diseases. Mutations can largely affect the function of the corresponding protein. Therefore, the molecular mechanism of nsSNPs in the FOXP4 gene needs to be elucidated. Initially, the SNPs of the FOXP4 gene were extracted from the dbSNP database and a total of 23124 SNPs was found, where 555 nonsynonymous, 20525 intronic, 1114 noncoding transcript, 334 synonymous were obtained and the rest were unspecified. Then, a series of bioinformatics tools (SIFT, PolyPhen2, SNAP2, PhD SNP, PANTHER, I-Mutant2.0, MUpro, GOR IV, ConSurf, NetSurfP 2.0, HOPE, DynaMut2, GeneMANIA, STRING and Schrodinger) were used to explore the effect of nsSNPs on FOXP4 protein function and structural stability. First, 555 nsSNPs were analyzed using SIFT, of which 57 were found as deleterious. Following, PolyPhen2, SNAP2, PhD SNP and PANTHER analyses, 10 nsSNPs (rs372762294, rs141899153, rs142575732, rs376938850, rs367607523, rs112517943, rs140387832, rs373949416, rs373949416 and rs376160648) were common and observed as deleterious, damaging and diseases associated. Following that, using I-Mutant2.0 and MUpro servers, 7 nsSNPs were found to be the most unstable. GOR IV predicted that these seven nsSNPs affect protein structure by altering the protein contents of alpha helixes, extended strands, and random coils. Following DynaMut2, 5 nsSNPs showed a decrease in the ΔΔG value compared with the wild-type and were found to be responsible for destabilizing the corresponding protein. GeneMANIA and STRING network revealed interaction of FOXP4 with other genes. Finally, molecular dynamics simulation analysis revealed consistent fluctuation in RMSD and RMSF values, Rg and hydrogen bonds in the mutant proteins compared with WT, which might alter the functional and structural stability of the corresponding protein. As a result, the aforementioned integrated comprehensive bioinformatic analyses provide insight into how various nsSNPs of the FOXP4 gene change the structural and functional properties of the corresponding protein, potentially proceeding with the pathophysiology of human diseases.

The bent-blade cutter is widely used in machining typical deep-cavity parts such as turbine discs and disc shafts, but few scholars have studied the dynamics of the turning process. The existing mechanism of regenerative chatter in the metal-cutting process does not consider the influence of bending and torsional vibration, the change of tool profile and the complex machining geometry, so it cannot be directly used to reveal the underlying cause of the chatter phenomena in the deep inner cavity part turning process. This paper attempts to investigate the dynamic problem of the bent-blade cutter turning process. The dynamic model of a bent-blade cutter is proposed by considering the regenerative chatter effect. Based on the extended Timoshenko beam element (E-TBM) theory and finite element method (FEM), the coupling between the bending vibrations and the torsional vibrations, as well as the dynamic cutting forces, are modeled along the turning path. The vibration characteristics of the bending–torsion combination of cutter board and cutter bar, together with the dynamical governing equation, were analyzed theoretically. The chatter stability of a bent-blade cutter with a bending and torsion combination effect is predicted in the turning process. A series of turning experiments are carried out to verify the accuracy and efficiency of the presented model. Furthermore, the influence of cutting parameters on the cutting process is analyzed, and the results can be used to optimize the cutting parameters for suppressing machining vibration and improving machining process stability. [ABSTRACT FROM AUTHOR]

The prediction of the stability of ceramic suspensions with multiple particles is limited depend on classical DLVO theory or Stokes equation. The mechanical effect of particles settling in suspension was studied, and a simplified mechanical equilibrium model of particles was proposed, shown as follows: u = Δ ρ g d 2 18 η − τ y d 36 η. The sedimentation of particles is mainly influenced by the density difference between particles and fluid Δ ρ , particle size d , suspension viscosity η , as well as shear yield stress τ y. For a given suspension, Δ ρ , d and η are constants, and τ y can be directly measured through rheological methods. When calculated settling velocity of particles approaches infinitely small values, the suspensions can be considered stable. The expression derived in this work had been verified by the experimental data. In addition, correlation between the results from improved settlement height method and the calculated settling velocity was established. To further investigate the impact of additives on suspensions stability, the Kelvin-Voigt rheological model was employed to characterize viscoelastic properties during oscillation, and a coefficient K (K= τ y − G ' γ c τ y) was proposed to quantitatively determine the contribution of the "dashpot" component in influencing the yield stress τ y. The regulation strategy for additives in suspensions was established based on experiments: a K value exceeding 0.274 indicates excessive additives concentration and necessitates reduction, whereas a K value below 0.274 suggests an insufficient additives concentration and requires additional dosage. In summary, the above model is helpful to predict the stability of ceramic suspensions. [ABSTRACT FROM AUTHOR]

The underground powerhouse of the Shuangjiangkou hydropower station is one of the largest caverns under construction in China, and its stability during construction is crucial for safe construction. To study the stability of the surrounding rock during excavation, the displacement and stress of the surrounding rock were monitored by multi-point displacement meters and bolt stress meters. Based on the monitoring data, the elastic modulus, Poisson’s ratio, friction angle, and cohesion of surrounding rock were inversely analyzed by the PSO-BP algorithm. Then, the back-analyzed parameters were used to simulate the subsequent excavations and predict the stability of surrounding rock during the following construction. The analysis results show that the surrounding rocks were generally stable during the initial four stages of excavation, and the main factors affecting their stability were blasts and unfavorable geological structures, including the lamprophyre vein and the F1 fault. These unfavorable geological structures also significantly decrease the mechanical parameters of surrounding rock as demonstrated by back analysis, and the stability prediction results show that the omnibus bar cave and the tailrace tunnel were at the greatest risk of instability during the subsequent excavations. This study provides a practical analysis for engineering excavation of the underground caverns.

Abstract Background Computational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability. Results We show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation. Conclusions Using a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well.

Improving protein stability is important for industrial applications, and one promising method for achieving this is backbone circularization. As connector length affects stability, predicting and elucidating a more stable connector length is necessary for development of the backbone circularization method. However, the relationship between connector length and protein stability has not been completely elucidated. Here, we determined the most stable connector length for granulocyte colony‐stimulating factor by changing one residue at a time to produce connector length variants and then measuring their thermal stability. Analysis of the local structures obtained from the predicted structures of the circularized variants revealed that an approach using helix length, dihedral backbone angle, and number of unbonded hydrogen bond donors and acceptors is suitable for identifying connector lengths with higher stability.

Micro-milling, widely employed across various fields, faces significant challenges due to the small diameter and limited stiffness of its tools, making the process highly susceptible to cutting chatter and premature tool breakage. Ensuring stable and safe cutting processes necessitates the prediction of chatter by considering the tool breakage. Crucially, the modal parameters of the spindle–holder–tool system are important prerequisites for such stability prediction. In this paper, the frequency response functions (FRFs) of the micro-milling tool are calculated by direct FRFs of the micro-milling cutter and cross FRFs between a point on the shank and one on the tool tip. Additionally, by utilizing a cutting force model specific to micro-milling, the bending stress experienced by the tool is computed, and the tool breakage curve is subsequently determined based on the material’s permissible maximum allowable stress. The FRFs of the micro-milling tool, alongside the tool breakage curve, are then integrated to generate the final stability lobe diagrams (SLDs). The effectiveness and reliability of the proposed methodology are confirmed through a comprehensive series of numerical and experimental validations.

International political stability has been a hot issue of global concern. Starting from the factors influencing international political stability, the study constructs a prediction model based on the CNN neural network and takes some international countries as the case study objects to predict the political stability of each country. The system of political stability influencing factor indicators is constructed. 13 influencing factor indicators are selected and divided into three categories, and the warning intervals of early warning indicators are determined. The study trained and validated the CNN neural network model, and the results showed that the mean square error of the prediction model was 1.862 × 108, the average accuracy of the model was 1.53%, and the relative error of each year was within ±4%, which reached the set model accuracy, and thus the prediction model proposed in this paper can be highly accurate. Subsequently, 44 countries along the route were selected to carry out political stability prediction research using the early warning model, and finally, the political stability of some countries was investigated according to the stability interval level classification method, and the results showed that the political stability of Syria, Iran, and many other countries had a high-risk phenomenon.

Predicting the stability of a Decentralized Smart Grid is key to the control of such systems. One of the key aspects that is necessary when observing the control of DSG systems is the need for rapid control. Due to this, the application of AI-based machine learning (ML) algorithms may be key to achieving a quick and precise stability prediction. In this paper, the authors utilize four algorithms—a multilayer perceptron (MLP), extreme gradient boosting (XGB), support vector machines (SVMs), and genetic programming (GP). A public dataset containing 30,000 points was used, with inputs consisting of τ —the time needed for a grid participant to adjust consumption/generation, p—generated power, and γ —the price elasticity coefficient for four grid elements; and outputs consisting of s t a b —the eigenvalue of stability and s t a b f , the categorical stability of the system. The system was modeled using the aforementioned methods as a regression model (targeting s t a b ) and a classification model (targeting s t a b f ). Modeling was performed with and without the τ values due to their low correlation. The best results were achieved with the XGB algorithm for classification, with and without the τ values as inputs—indicating them as being unnecessary. [ABSTRACT FROM AUTHOR]

Background: Computational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability. Results: We show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation. Conclusions: Using a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well. [ABSTRACT FROM AUTHOR]

Cell line instability is a common problem in biopharmaceutical manufacturing using Chinese hamster ovary (CHO) cells. Cell line instability, which refers to unintended and unpredicted genetic, epigenetic, and phenotypic changes over time, can increase production costs and threaten sales approval by reducing product quantity and quality. While a stability test is conducted to screen cell lines with stable transgene expression, this process requires several months, delaying the entire drug development timeline. To accelerate timeline for drug development, understanding, mitigation, and prediction of cell line instability are critical. In this review, we update recent research progresses regarding instability-inducing biological mechanisms and alleviating the intrinsic instability of CHO cells. We also discuss studies that contribute to predicting irregular phenotypic changes in recombinant protein-producing CHO cell lines, based on omics-based studies. These prediction strategies will contribute to complementing current instability alleviating strategies, thereby saving labor, cost, and time for the cell line development process as well as providing a comprehensive understanding of CHO cell biology. [ABSTRACT FROM AUTHOR]

Improving protein stability is important for industrial applications, and one promising method for achieving this is backbone circularization. As connector length affects stability, predicting and elucidating a more stable connector length is necessary for development of the backbone circularization method. However, the relationship between connector length and protein stability has not been completely elucidated. Here, we determined the most stable connector length for granulocyte colony‐stimulating factor by changing one residue at a time to produce connector length variants and then measuring their thermal stability. Analysis of the local structures obtained from the predicted structures of the circularized variants revealed that an approach using helix length, dihedral backbone angle, and number of unbonded hydrogen bond donors and acceptors is suitable for identifying connector lengths with higher stability. [ABSTRACT FROM AUTHOR]

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid’s stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method.

Two‐dimensional (2D) halide perovskites are an attractive class of hybrid perovskites that have additional optoelectronic tunability due to their accommodation of relatively large organic ligands. Nevertheless, contemporary ligand design depends on either expensive trial‐and‐error testing of whether a ligand can be integrated within the lattice or conservative heuristics that unduly limit the scope of ligand chemistries. Here, the structural determinants of stable ligand incorporation within Ruddlesden‐Popper (RP) phase perovskites are established by molecular dynamics (MD) simulations of over ten‐thousand RP‐phase perovskites and training of machine learning classifiers capable of predicting structural stability based solely on generalizable ligand features. The simulation results show near‐perfect predictions of positive and negative literature examples, predict trade‐offs between several ligand features and stability, and ultimately predict an inexhaustibly large 2D‐compatible ligand design‐space. [ABSTRACT FROM AUTHOR]

Heavy rainfall induces shallow landslides in the mountainous areas of China. There is a need for regional slope stability prediction to reduce the damage to infrastructure, residents, and the economy. This study attempts to demarcate areas prone to rainfall-induced shallow landslides using the transient rainfall infiltration and grid-based slope stability (TRIGRS) model under different rainfall conditions. After inputting the engineering geological and geotechnical characteristic data of the area in China, the slope stability was simulated and verified by a deformation monitoring landslide. The slope stability gradually declined under the influence of precipitation from 5–8 July 2021. Slope stability gradually decreased under the predicted rainfall intensity of 60 mm/d for 6 days. The percentage of the slope area with a factor of safety (FS) less than 1.0 increased from 0.00% (1 d) to 3.18% (6 d). The study results could be used for hazards mitigation in this region. [ABSTRACT FROM AUTHOR]

Prediction of lyophilized product shelf-life using accelerated stability data requires understanding the temperature dependence of the degradation rate. Despite the abundance of published studies on stability of freeze-dried formulations and other amorphous materials, there are no definitive conclusions on the type of pattern one can expect for the temperature dependence of degradation. This lack of consensus represents a significant gap which may impact development and regulatory acceptance of freeze-dried pharmaceuticals and biopharmaceuticals. Review of the literature demonstrates that the temperature dependence of degradation rate constants in lyophiles can be represented by the Arrhenius equation in most cases. In some instances there is a break in the Arrhenius plot around the glass transition temperature or a related characteristic temperature. The majority of the activation energies (E a), which are reported for various degradation pathways in lyophiles, falls in the range of 8 to 25 kcal/mol. The degradation E a values for lyophiles are compared with the E a for relaxation processes and diffusion in glasses, as wells as solution chemical reactions. Collectively, analysis of the literature demonstrates that the Arrhenius equation represents a reasonable empirical tool for analysis, presentation, and extrapolation of stability data for lyophiles, provided that specific conditions are met. [ABSTRACT FROM AUTHOR]

: Due to the drastic increase in global population as well as economy, electricity demand becomes considerably high. The recently developed smart grid (SG) technology has the ability to minimize power loss at the time of power distribution. Machine learning (ML) and deep learning (DL) models can be effectually developed for the design of SG stability techniques. This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability (SSODLSA-SGS) prediction model. Primarily, class imbalance data handling process is performed using Synthetic minority oversampling technique (SMOTE) technique. The SSODLSA-SGS model involves two stages of pre-processing namely data normalization and transformation. Besides, the SSODLSA-SGS model derives a deep belief-back propagation neural network (DBN-BN) model for the prediction of SG stability. Finally, social spider optimization (SSO) algorithm can be applied for determining the optimal hyperparameter values of the DBNBN model. The design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model. A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS model. The extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures. [ABSTRACT FROM AUTHOR]

Milling chatter will not only cause chatter marks on the surface of the machined parts and reduce the surface quality of the machined parts, but also aggravate tool wear and reduce the life of the machine tool. According to the influence of the tool posture of the ball-end milling cutter affecting on the flutter generated during the machining process, the milling dynamics model under the influence of the tool posture was built. The milling dynamic model was solved by the third-order Newton-Hermite interpolation fully discrete method, and the state transition matrix of the system was obtained. The Floquet theory was used to judge the milling stability of the milling cutter under different tool postures, and the tool posture stability prediction map of the ball-end milling cutter was constructed. The experiment determines that the horizontal upward cutting method has the best stability and the lowest surface roughness of the workpiece, which could verify the validity of the stability prediction map. [ABSTRACT FROM AUTHOR]

The bent-blade cutter is widely used in machining typical deep-cavity parts such as turbine discs and disc shafts, but few scholars have studied the dynamics of the turning process. The existing mechanism of regenerative chatter in the metal-cutting process does not consider the influence of bending and torsional vibration, the change of tool profile and the complex machining geometry, so it cannot be directly used to reveal the underlying cause of the chatter phenomena in the deep inner cavity part turning process. This paper attempts to investigate the dynamic problem of the bent-blade cutter turning process. The dynamic model of a bent-blade cutter is proposed by considering the regenerative chatter effect. Based on the extended Timoshenko beam element (E-TBM) theory and finite element method (FEM), the coupling between the bending vibrations and the torsional vibrations, as well as the dynamic cutting forces, are modeled along the turning path. The vibration characteristics of the bending–torsion combination of cutter board and cutter bar, together with the dynamical governing equation, were analyzed theoretically. The chatter stability of a bent-blade cutter with a bending and torsion combination effect is predicted in the turning process. A series of turning experiments are carried out to verify the accuracy and efficiency of the presented model. Furthermore, the influence of cutting parameters on the cutting process is analyzed, and the results can be used to optimize the cutting parameters for suppressing machining vibration and improving machining process stability.

Cutting vibration has become a major problem to the limited high-efficiency and high-quality machining. The non-equal tooth effect of the variable pitch end mill can better adjust the phase mechanism of the system and suppress chatter. Therefore, an in-depth study is required to make full use of the vibration reduction property of the milling cutter. Considering the regenerative chatter mechanism, a nonlinear milling force model for the variable pitch end mill is analyzed first, then the time-varying coefficient matrix of the cutting force is developed, and finally, a dynamic model with multiple delays is proposed. The developed model is evaluated from the perspective of chatter stability, and the limiting cutting depth is determined by using the frequency domain and semi-discretization methods. Combining with the system dynamic detection tests, the dynamic model parameters and cutting coefficients are determined for predicting stability. The analytical solutions of stability are benchmarked against the results of the time domain digital simulation, and both predictions are validated through cutting tests. The relationship between the limiting cutting depth and pitch parameters is proposed by assessing the effect of multiple delays. A method for optimal pitch parameters is developed to maximize the stability limit. It is shown that the proposed method can improve the chatter stability of milling cutters and alleviate cutting vibration, which can play an important role in improving the machining efficiency and surface quality of workpieces, and enhancing the technological level of main components. [ABSTRACT FROM AUTHOR]

A cyber physical energy system (CPES) involves a combination of processing, network, and physical processes. The smart grid plays a vital role in the CPES model where information technology (IT) can be related to the physical system. At the same time, the machine learning (ML) models find useful for the smart grids integrated into the CPES for effective decision making. Also, the smart grids using ML and deep learning (DL) models are anticipated to lessen the requirement of placing many power plants for electricity utilization. In this aspect, this study designs optimal multi-head attention based bidirectional long short term memory (OMHA-MBLSTM) technique for smart grid stability prediction in CPES. The proposed OMHA-MBLSTM technique involves three subprocesses such as pre-processing, prediction, and hyperparameter optimization. The OMHA-MBLSTM technique employs min-max normalization as a pre-processing step. Besides, the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES. At the same time, the moth swarm algorithm (MHA) is utilized for optimally modifying the hyperparameters involved in the MBLSTM model. To ensure the enhanced outcomes of the OMHA-MBLSTM technique, a series of simulations were carried out and the results are inspected under several aspects. The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models. [ABSTRACT FROM AUTHOR]

The stability prediction is an effective approach to suppress the unstable milling process caused by the regenerative chatter. Through updating a numerical solution scheme (NSS), a fast prediction method is proposed in this paper. Firstly, a NSS is constructed by combining the Lagrange polynomial and Euler's method. Then, the solution of the differential equations described the milling process is transformed into the initial-value problem of the equation which is expressed by the built NSS over each small discrete time interval. Subsequently, the state transition matrix over one tooth passing period is established to predict the stability lobe diagram (SLD) via the Floquet theory. Finally, the computational performances of the proposed method are verified by the single and two DOF dynamic models. Compared with the complete discretization scheme, the computational accuracy and efficiency of the proposed method are increased by 2.69 times and 2.01 times, respectively. The proposed method reduces the computational accuracy by 2.16 times in comparison to the first-order full-discretization method, but it increases the computational efficiency by 2.2 times. [ABSTRACT FROM AUTHOR]

Smart Grid (SG) technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures, control systems, and communication technologies. In SGs, user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption. Since the entire procedure is valued on the basis of time, it is essential to perform adaptive estimation of the SG's stability. Recent advancements inMachine Learning (ML) andDeep Learning (DL) models enable the designing of effective stability prediction models in SGs. In this background, the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction (WWOODL-SGSP) model. The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner. To attain this, the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level. Then, WWO algorithm is applied to choose an optimal subset of features from the pre-processed data. Next, Deep Belief Network (DBN) model is followed to predict the stability level of SGs. Finally, Slime Mold Algorithm (SMA) is exploited to fine tune the hyperparameters involved in DBN model. In order to validate the enhanced performance of the proposedWWOODL-SGSP model, a wide range of experimental analyses was performed. The simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches. [ABSTRACT FROM AUTHOR]

Most of the existing landslide dam stability prediction models are linear models, which cannot fully consider the complex nonlinear relationship between landslide dam stability and its morphological characteristics and hydrodynamic conditions.In view of this, a new SSA-Adam-BP model for predicting the stability of landslide dams is proposed by combining the back propagation neural network model and the salp optimization algorithm.The grid search method is used to select the best combination of hyperparameters that can determine the structure of the model.Then, the models with different optimization algorithms are evaluated by cross-validation and ROC curve drawing.The practical application of the model is explained and verified by using the global data of 153 landslide dams in the open source database.Compared with the traditional linear model, the combination of the SSA and Adam optimization algorithm improves the global search ability of the BP model, and its average cross-verification accuracy reaches 91.73%.It not only has a lower misjudgment rate but can also use fewer parameters to quickly and accurately predict the stability of landslide dams.The SSA-Adam-BP model can accurately predict the stability of typical projects in recent years, with certain practicality and system platform promotion application value.

Predicting the stability of a Decentralized Smart Grid is key to the control of such systems. One of the key aspects that is necessary when observing the control of DSG systems is the need for rapid control. Due to this, the application of AI-based machine learning (ML) algorithms may be key to achieving a quick and precise stability prediction. In this paper, the authors utilize four algorithms—a multilayer perceptron (MLP), extreme gradient boosting (XGB), support vector machines (SVMs), and genetic programming (GP). A public dataset containing 30,000 points was used, with inputs consisting of τ—the time needed for a grid participant to adjust consumption/generation, p—generated power, and γ—the price elasticity coefficient for four grid elements; and outputs consisting of stab—the eigenvalue of stability and stabf, the categorical stability of the system. The system was modeled using the aforementioned methods as a regression model (targeting stab) and a classification model (targeting stabf). Modeling was performed with and without the τ values due to their low correlation. The best results were achieved with the XGB algorithm for classification, with and without the τ values as inputs—indicating them as being unnecessary.

Soil-rock mixtures are geological materials with complex physical and mechanical properties. Therefore, the stability prediction of soil-rock mixture slopes using machine learning methods is an important topic in the field of geological engineering. This study uses the soil-rock mixture slopes investigated in detail as the dataset. An intelligent optimization algorithm-weighted mean of vectors algorithm (INFO) is coupled with a machine learning algorithm. One of the new ensemble learning models, which named IN-Voting, is coupled with INFO and voting model. Twelve single machine learning models and sixteen novel IN-Voting ensemble learning models are built to predict the stability of soil-rock mixture slopes. Then, the prediction accuracies of the above models are compared and evaluated using three evaluation metrics: coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). Finally, an IN-Voting ensemble learning model based on five weak learners is used as the final model for predicting the stability of soil-rock mixture slopes. This model is also used to analyze the importance of the input parameters. The results show that: 1) Among 12 single machine learning models for the stability prediction of soil-rock mixture slopes, MLP (Multilayer Perceptron) has the highest prediction accuracy. 2) The IN-Voting model has higher prediction accuracy than single machine learning models, with an accuracy of up to 0.9846) The structural factors affecting the stability of soil-rock mixture slopes in decreasing order are the rock content, bedrock inclination, slope height, and slope angle.

Excessive tool wear can shorten tool life and cause low machining surface quality and efficiency in the milling of Ti-6AI-4 V thin-walled workpieces. The influence mechanism between the tool flank wear and stability predication is of significant for its further development in milling Ti-6Al-4 V. However, the relationship between tool flank wear and stability predication needs to be further investigated as the effect of time-varying tool flank wear is ignored in conventional methods. In this work, a system stability prediction model considering time-varying tool flank wear effect in milling of Ti-6AI-4 V thin-walled workpiece is proposed. The tool flank wear region is discretized into differential elements, and then, the friction effect and process damping effect caused by extruding function between tool and workpiece are analyzed, and a time-varying milling force model is established. In this process, the relationship of cutting tool flank wear band width VB and section radius difference NB is determined, and the indentation volume between tool and workpiece is iteratively calculated, which is used to investigate process damping. After, the time-varying milling force coefficients are derived considering different tool flank wear status. Then, in modal space, the evolutionary process of stability lobe diagrams considering tool flank wear effect is determined. Subsequently, to effectively predict system stability, tool flank wear curves and dynamic cutting force coefficients are calibrated by slot milling, and the average errors between cutting force prediction values considering tool flank wear effect and experimental values in feed direction and normal direction are 7.3% and 12.1%, respectively. Finally, a series of machining tests are conducted to verify the effectiveness of tool flank wear on the machining stability in the milling of Ti-6AI-4 V thin-walled workpieces to some extent, and the experimental results show that the system stability prediction accuracy of the proposed method is improved by 23.8% compared with that using conventional method. [ABSTRACT FROM AUTHOR]

The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available. [ABSTRACT FROM AUTHOR]

Regenerative chatter is the most important factor affecting the stability of the milling process. It is core for suppressing chatter and improving production efficiency to accurately and efficiently identify the stable region of milling chatter. Therefore, according to the theory of predictor-corrector, three predictor–corrector methods (PCM) are, respectively, proposed for the milling stability region by applying the fourth-order Adams-Bashforth-Moulton formula, Simpson formula, and Hamming formula. Firstly, the regenerative chatter milling process is described as a second-order time-delay differential equation (DDE) with periodic coefficients. Thus, the forced vibration time can uniformly be discretized as a time node set. Secondly, the fourth-order Adams-Bashforth formula is used to predict the displacement at every time node, whereas the fourth-order Adams-Moulton formula can be employed to correct this predicted value. In addition, the fourth-order Simpson formula and Hamming formula can also correct the predicted value. Thus, a higher precision discrete prediction-correction expansion is constructed for the transformation of DDE into the state transition express. The Floquet theory can be depended on to present the judgment criterion of milling stability. Moreover, finally, under the same milling process parameters, comparisons of both the stability lobe curve and the local discrete error curve show that the PCM has a faster convergence rate than the 1st-SDM (first-order semi-discretization method) and 2nd-FDM (second-order full-discretization method). This shows that the PCM can obtain better computational accuracy under the same discrete number, whereas the PCM is significantly higher computational efficiency over 1st-SDM and 2nd-FDM. Meanwhile, considering the actual machining environment, helix angle effect and multiple modes effect of the tool are analyzed; experimental verification considering multiple modes with helix angle further indicates the applicability of the PCM. [ABSTRACT FROM AUTHOR]

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Heavy rainfall induces shallow landslides in the mountainous areas of China. There is a need for regional slope stability prediction to reduce the damage to infrastructure, residents, and the economy. This study attempts to demarcate areas prone to rainfall-induced shallow landslides using the transient rainfall infiltration and grid-based slope stability (TRIGRS) model under different rainfall conditions. After inputting the engineering geological and geotechnical characteristic data of the area in China, the slope stability was simulated and verified by a deformation monitoring landslide. The slope stability gradually declined under the influence of precipitation from 5–8 July 2021. Slope stability gradually decreased under the predicted rainfall intensity of 60 mm/d for 6 days. The percentage of the slope area with a factor of safety (FS) less than 1.0 increased from 0.00% (1 d) to 3.18% (6 d). The study results could be used for hazards mitigation in this region.

Abstract This paper presents a set of new required programmes written in format data using the built-in programming FISH language available in three-dimensional Fast Lagrangian Analysis of Continua software (FLAC3D). These script data files were developed, to overcome difficulties noted during the nailed slope stability prediction. They established to analyse stability of general embankment slope cases under various soil nailing parameters design. To deal with 3D finite difference analysis results in terms of the factor of safety (FOS) and critical slip surface, both 2D finite difference and limit equilibrium (LEM) methods are also conducted. A comparison of the 3D and 2D results indicates that 2D analyses underestimate seriously stability predictions, notably in terms of the FOS. Therefore, it was concluded that, 3D analysis should be used as an alternative to the 2D analyses, even though it requires more time. Some useful conclusions are obtained from this work, which provides a good practice guidance for real life scenarios.

[Display omitted] • We explored structure- and element-specific factors of nanoalloy stability. • Bond and surface construction determine configuration stability of nanoalloy. • Element-specific descriptor is expressed via mono-metal physical properties. The practical applications of nanoalloys, which are known for their exceptional catalytic activity, are difficult to realize owing to their intricate stability of these systems, which is influenced by structural variations, configurational nuances, and elemental interactions. Many combinations resulting from the inclusion of many different possible constituent elements intensifies the complexity of their analysis, emphasizing the need for accurate stability prediction methods. This study investigated the stability of A−B binary nanoalloys composed of 3 d , 4 d , and 5 d late transition metal elements such as Ni, Cu, Ru, Rh, Pd, Ag, Os, Ir, Pt, and Au. Density functional theory (DFT) calculations and supervised learning (SL) were employed to predict the stability of these alloys. The excess energy, an indicator used to evaluate the stability of nanoalloys, was predicted using the structure- and element-specific descriptors in a two-stage SL method. The first SL stage involves expressing the excess energy through structure-specific descriptors such as bond fractions and element deviation within each coordination number (CN). The second SL stage involves expressing the regression coefficients of the structure-specific descriptors using element-specific descriptors. The element-specific descriptors predicting the element deviation in each CN correspond to differences in melting point and atomic radius. Simultaneously, the prediction of bond fractions relies on factors such as electronegativity difference and electron density discontinuity between the constituent elements. The study findings suggest that the stability of a nanoalloy can be broadly categorized into that of its surface and inner components. Monte Carlo simulations based on structure- and element-specific descriptors exhibit the capability to predict the stable configurations of binary nanoalloys without the need for DFT. The approach described in this study significantly enhances the efficiency with which these calculations may be executed, thereby expediting the analysis of the properties of these alloys. [ABSTRACT FROM AUTHOR]

To accurately and quickly analyze the stability of open-pit mine slopes, this paper proposes AdaBoost-Convolutional Neural Network(AdaBoost-CNN) to establish the nonlinear relationship between open-pit mine slope stability and influencing factors. AdaBoost-CNN is a new machine learning method that combines AdaBoost and CNN. The core idea is to combine the feature extraction capabilities of CNN with the ability of AdaBoost to process unbalanced data, which has the advantages of high reliability, high accuracy, and less training time. AdaBoost-CNN uses migration learning, which not only eliminates the limitation of CNN that require a large number of training samples but also solves the problem of reducing actual performance in the serialization process of AdaBoost. This paper uses BP neural network, Support Vctor Mchine, CNN, and AdaBoost-CNN to model and analyze the measured engineering data. By comparing RMSK and RPK, it is found that AdaBoost-CNN has the highest prediction accuracy and the strongest model generalization ability. The results show that AdaBoost-CNN can predict slope stability more accurately, and is a reliable tool for slope stability prediction. [ABSTRACT FROM AUTHOR]