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A novel Fe-Ni bimetallic nanoparticle synthesis technique, denoted pixel target ablation, is reported. The technique entails ablating a thin film target consisting of patterned Fe and Ni pixels with a selected ratio using a KrF excimer laser. The laser energy breaks a known amount of target materials into metal atoms, which then form nanoparticles by recombination in the gas phase. Due to the nature of thin-film ablation, splashing of large particles was eliminated with the added benefit of minimizing nanoparticle agglomeration. Plume dynamics and surface characterizations were carried out to exploit the formation of Fe-Ni nanoparticles more fully. The composition was readily controlled by varying the initial relative amount of Fe and Ni target pixels. Synthesis of multi-element nanoparticles by pixel target ablation should be possible with any element combination that can be prepared as a thin-film target. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is becoming increasingly popular and useful for modeling landslide movement processes due to its advantages of providing excellent generalization ability and accurately describing complex and nonlinear behavior. However, the identification of key variables is a crucial step in ensuring robustness and accuracy in AI modeling, but thus far, little attention has been given to this topic. In the present study, mutual information (MI)-based measures are proposed for input variable selection (IVS) and incorporated into optimized support vector regression (SVR) for the displacement prediction of seepage-driven landslides. The performance of optimized SVR models with ten MI-based IVS strategies is compared. A typical seepage-driven landslide was chosen for comparison. The experimental results indicate that IVS-based optimized SVR can significantly improve predictions. When the variable-reduced inputs were input into the optimized artificial bee colony (ABC)-SVR model, the mean values of normalized root mean square error (NRMSE) and Kling-Gupta efficiency (KGE) decreased and increased by as much as 71.6 and 95.2%, respectively, relative to those for the base model with all candidates. Furthermore, the joint mutual information (JMI) and double input symmetrical relevance (DISR) criteria are recommended for IVS for seepage-driven landslides because they achieve the best tradeoff between accuracy and stability. [ABSTRACT FROM AUTHOR]

Cyclic wetting and drying processes have been considered as important factors that accelerate the weathering process and have deteriorative effects on rock properties. In the present study, a fully nondestructive and noninvasive testing approach utilizing micro-CT and ultrasonic wave velocity tests was employed to investigate the microstructure of slate under wetting and drying cycles. We studied variations in the physical properties, including the dry weight and the velocities of P- and S-waves versus the number of wetting and drying cycles. The internal microstructural distributions were visualized and quantified by the 3D reconstruction and hybrid image segmentation of CT images. The degree of deterioration caused by wetting and drying cycles was reflected by exponential decreases of physical properties, including dry weight and velocities of the P- and S-waves. Parameters relating to the microfracture diameter, volume, etc. were quantified. The nondestructive and noninvasive testing approach utilizing micro-CT and ultrasonic wave velocity tests has potential for the detection and visualization of the internal microstructure of rock under wetting and drying cycles. [ABSTRACT FROM AUTHOR]

A laboratory model test is an effective method for studying landslide risk mitigation. In this study, thermal infrared (TIR) imagery, a modern no-contact technique, was introduced and integrated with terrestrial laser scanning (TLS) and particle tracking velocimetry (PTV) to characterize the failure of a landslide model. The characteristics of the failure initiation, motion, and region of interest, including landslide volume, deformation, velocity, surface temperature changes, and anomalies, were detected using the integrated monitoring system. The laboratory test results indicate that the integrated monitoring system is expected to be useful for characterizing the failure of landslide models. The preliminary results of this study suggest that a change in the relative TIR signal (ΔTIR) can be a useful index for landslide detection, and a decrease in the average value of the temperature change ( Δ T I R ¯ ) can be selected as a precursor to landslide failure. [ABSTRACT FROM AUTHOR]

Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and water cycle algorithm (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), metaheuristic support vector regression (SVR), and the nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, and convergence. The results obtained for the Shuping and Baishuihe landslides demonstrate that the hybrid approach can be utilized to determine the optimum hyperparameters and present statistical significance, thus enhancing accuracy and reliability in ML-based prediction. Significant differences were observed among the five metaheuristics. Based on the Friedman test, which was performed on the root mean square error (RMSE), Kling-Gupta efficiency (KGE), and computational time, PSO is recommended for hyperparameter tuning for SVR-based displacement prediction due to its ability to maintain a balance between precision, computational time, and robustness. The nonparametric Friedman test is promising for presenting statistical significance, thus enhancing reproducibility. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been extensively applied to model geohazards, yielding tremendous success. However, researchers and practitioners still face challenges in enhancing the reliability of ML models. In the present study, a systematic framework combining k-fold cross-validation (CV), metaheuristics (MHs), support vector regression (SVR), and Friedman and Nemenyi tests was proposed to improve the reliability and performance of geohazard modeling. The average normalized mean square error (NMSE) from k-fold CV sets was adopted as the fitness metric. Twenty of the most well-established MHs and the most recent MHs were adopted to tune the hyperparameters of SVR and were evaluated through nonparametric Friedman and post hoc Nemenyi tests to identify significant differences. Observations from a typical reservoir landslide were selected as a benchmark dataset, and the accuracy, robustness, computational time, and convergence speed of the MHs were compared. Significant performance differences among the twenty MHs were identified by Friedman and post hoc Nemenyi tests of the mean absolute error (MAE), root mean squared error (RMSE), Kling–Gupta efficiency (KGE), and computational time, with p values lower than 0.05. The comparison of results demonstrated that the multiverse optimizer (MVO) is among the highest-performing, most stable, and computationally efficient algorithms, providing superior performance to other methods, with nearly optimum values of the correlation coefficient (R), a low MAE (23.5086 versus 23.9360), a low mean RMSE (48.6946 versus 50.1882), and a high mean KGE (0.9803 versus 0.9893) in predicting the displacement of the Shuping landslide. This paper considerably enriches the literature regarding hyperparameter optimization algorithms and the enhancement of their reliability. In addition, Friedman and post hoc Nemenyi tests have the potential for evaluating and comparing various ML-based geohazard models. • Introducing a systematic process for building a machine learning based prediction model in geohazard modeling. • Meta-heuristics are adopted for hyperparameter tuning of support vector regression, thus enhancing predictive accuracy. • A comprehensive comparison of twenty meta-heuristics for prediction of landslide displacement by Friedman and post hoc Nemenyi tests. • The multi-verse optimization (MVO) is very competitive because of the best tradeoff between accuracy, stability, and efficiency. [ABSTRACT FROM AUTHOR]