A meta-learning approach of optimisation for spatial prediction of landslides

Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an objective function, i.e., k-fold cross-validation accuracy. However, the overhead of hyperparameter optimisation can be prohibitive, especially for computationally expensive algorithms. This paper introduces an optimisation approach based on meta-learning for the spatial prediction of landslides. The proposed approach is tested in a dense tropical forested area of Cameron Highlands, Malaysia. Instead of optimising prediction models with a large number of hyperparameter configurations, the proposed approach begins with promising configurations based on several basic and statistical meta-features. The proposed meta-learning approach was tested based on Bayesian optimisation as a hyperparameter tuning algorithm and random forest (RF) as a prediction model. The spatial database was established with a total of 63 historical landslides and 15 conditioning factors. Three RF models were constructed based on (1) default parameters as suggested by the sklearn library, (2) parameters suggested by the Bayesian optimisation (BO), and (3) parameters suggested by the proposed meta-learning approach (BO-ML). Based on five-fold cross-validation accuracy, the Bayesian method achieved the best performance for both the training (0.810) and test (0.802) datasets. The meta-learning approach achieved slightly lower accuracies than the Bayesian method for the training (0.769) and test (0.800) datasets. Similarly, based on F1-score and area under the receiving operating characteristic curves (AUROC), the models with optimised parameters either by the Bayesian or meta-learning methods produced more accurate landslide susceptibility assessment than the model with the default parameters. In the present approach, instead of learning from scratch, the meta-learning would begin with hyperparameter configurations optimal for the most similar previous datasets, which can be considerably helpful and time-saving for landslide modelings

Antibacterial effects of carbon dots in combination with other antimicrobial reagents

<div><p>This study was designed to investigate the antimicrobial effects of CDots in combination with other antimicrobial reagents, including H₂O₂, Na₂CO₃, and AcOH (acetic acid). CDots were synthesized and passivated with 2,2’-(ethylenedioxy)bis(ethylamine) (EDA). The minimal inhibitory concentration (MIC) of CDots was 64 μg/mL on both Gram negative bacteria <i>E</i>.<i>coli</i> cells and Gram positive bacteria <i>Bacillus subtilis</i> cells. When CDots were combined with H₂O₂, antibacterial synergistic effects were observed based on the fractional inhibitory concentration (FIC) index, and further confirmed by an isobologram analysis and viable cell number counting methods. With the combination treatment of 10 μg/mL CDots with 8.82 mM H₂O₂, the viable <i>E</i>.<i>coli</i> cell numbers decreased 2.46 log, which was significant lower than the log reduction from 8.82 mM H₂O₂ (1.57 log) or 10 μg/mL CDots (0.14 log) treatment alone. However, the combination of CDots with Na₂CO₃ or AcOH did not show synergistic effects, instead, exhibiting indifference effects according to the FIC index. This study indicated that the combination of CDots with their synergistic antimicrobial reagents, such as H₂O₂, could reach the goal of inhibiting bacteria growth by using lower concentration of each individual chemical in the combination than using one chemical treatment alone, reduce the risks imposed on environmental health and the possibilities of the development of microbial resistances.</p></div