A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

The main aim of this study is to propose a novel hybrid intelligent model named MBSVM which is an integration of the MultiBoost ensemble and a support vector machine (SVM) for modeling of susceptibility of landslides in the Uttarakhand State, Northern India. Firstly, a geospatial database for the study area was prepared, which includes 391 historical landslides and 16 landslide-affecting factors. Then, the sensitivity of different combinations of these factors for modeling was validated using the forward elimination technique. The MBSVM landslide model was built using the datasets generated from the best selected factors and validated utilizing the area under the receiver operating characteristic (ROC) curve (AUC), statistical indexes, and the Wilcoxon signed-rank test. Results show that this novel hybrid model has good performance both in terms of goodness of fit with the training dataset (AUC = 0.972) and the capability to predict landslides with the testing dataset (AUC = 0.966). The efficiency of the proposed model was then validated by comparison with logistic regression (LR), a single SVM, and another hybrid model of the AdaBoost ensemble and an SVM (ABSVM). Comparison results show that the MBSVM outperforms the LR, single SVM, and hybrid ABSVM models. Thus, the proposed model is a promising and good alternative tool for landslide hazard assessment in landslide-prone areas.

     

Investigation of 3,3′,5,5′-tetra-tert-butyl-4,4′-stilbenequinone-based catalyst in the reaction of liquid-phase oxidation of inorganic sulfides

In this paper, the intermediate and final reaction products of catalytic oxidation of inorganic sulfides in the presence of oxygen dissolved in the kerosene fraction and 3,3′,5,5′-tetra-tert-butyl-4,4′-stilbenequinone were investigated. The thiosulfate and sulfate are major products of the oxidation of sodium sulfide under these conditions. The intermediate and final products in the catalytic oxidation of sulfide sulfur do not affect the rate of its oxidation. The yield of catalytic oxidation products depends on the nature of the sulfide and on the pH of the solution. The catalytic cycle for sulfide oxidation in the presence of 3,3′,5,5′-tetra-tert-butyl-4,4′-stilbenequinone is shown. The role of 3,3′,5,5′-tetra-tert-butyl-4,4′-stilbenequinone is to create a new and a more effective way of electron transfer from the reducing agent (sulfide) to the oxidant (oxygen).     

Neuro-genetic,neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile

Prediction of pile-bearing capacity developing artificial intelligence models has been done over the last decade. Such predictive tools can assist geotechnical engineers to easily determine the ultimate pile bearing capacity instead of conducting any difficult field tests. The main aim of this study is to predict the bearing capacity of pile developing several smart models, i.e., neuro-genetic, neuro-imperialism, genetic programing (GP) and artificial neural network (ANN). For this purpose, a number of concrete pile characteristics and its dynamic load test specifications were investigated to select pile cross-sectional area, pile length, pile set, hammer weight and drop height as five input variables which have the most impacts on pile bearing capacity as the single output variable. It should be noted that all the aforementioned parameters were measured by conducting a series of pile driving analyzer tests on precast concrete piles located in Pekanbaru, Indonesia. The recorded data were used to establish a database of 50 test cases. With regard to data modelling, many smart models of neuro-genetic, neuro-imperialism, GP and ANN were developed and then evaluated based on the three most common statistical indices, i.e., root mean squared error (RMSE), coefficient determination (R²) and variance account for (VAF). Based on the simulation results and the computed indices’ values, it is observed that the proposed GP model with training and test RMSE values of 0.041 and 0.040, respectively, performs noticeably better than the proposed neuro-genetic model with RMSE values of 0.042 and 0.040, neuro-imperialism model with RMSE values of 0.045 and 0.059, and ANN model with RMSE values of 0.116 and 0.108 for training and test sets, respectively. Therefore, this GP-based model can provide a new applicable equation to effectively predict the ultimate pile bearing capacity.

     

Application of a non-ionic amphiphilic maleic diester as polymerizable surfactant in free-radical emulsion polymerization

Summary A non ionic amphiphilic maleic diester with a poly(ethylene oxide) hydrophilic chain was used as a copolymerizable surfactant in batch free-radical emulsion polymerizations of styrene and n-butylacrylate. A good stability during the polymerization process was observed together with a high incorporation of the surfactant. Received: 14 December 1998/Revised version: 10 February 1999/Accepted: 10 February 1999     

Preparation,characterization, and properties of graphene oxide/urushiol-formaldehyde polymer composite coating

Graphene oxide (GO) was modified by 3-methacryloxypropyltrimethoxysilane (MPS) to obtain modified graphene oxide (MGO). MGO was dispersed in urushiol-formaldehyde polymer by mechanical mixing and ultrasonic dispersion, and MGO/urushiol-formaldehyde polymer (UFP) coatings with different MGO contents were fabricated. The microstructure, physico-mechanical properties, and electrochemical properties of the MGO/UFP composite coatings were investigated. The results indicated that the hardness, adhesion, and corrosion resistance of the MGO/UFP composite coatings were obviously enhanced compared with the pure UFP coatings. The hardness and the adhesion grade of the MGO/UFP composite coatings with 3.5 wt% MGO (GO, 1.5 wt%, and MPS, 2.0 wt%) reached 6H and 2, respectively. Additionally, GO connected with MPS by chemical bond and the well-dispersed MGO in UFP could significantly enhance the anticorrosion performance of the UFP coatings, which could result from bending the diffusion pathway of penetrant species in the UFP coating matrix.     

Attention-based neural network with Generalized Mean Pooling for cross-view geo-localization between UAV and satellite

Artificial Life and Robotics - Cross-view geo-localization is finding images containing the same geographic target in multi-views. For example, given a query image from UAV view, a proposed...     

Organic Conjugated Trimers with Donor–Acceptor–Donor Structures for Photocatalytic Hydrogen Generation Application

Organic donor–acceptor–donor (D–A–D) polymers or small molecules are widely investigated in organic solar cells due to their broad light absorption, narrow band gap, excellent charge mobility, and exciton seperation at the interface. However, studies of conjugated small molecules with D–A–D molecule structures as photocatalytically active materials are still rare. In this study, an unprecedented demonstration that photocatalytic activity can in fact be affected by tuning the D and A is given. Especially, the EBE trimer, comprising 3,4-ethylenedioxythiophene (E) and benzothiadiazole (B) units, exhibits the best photophysical, chemical, and photocatalytic properties compared to other D–A–D combinations of D and A. Detailed kinetic studies show that all trimers in organic solution present relatively long-lived and highly emissive photogenerated singlet excitons (τ = 4–13 ns; ϕ_em = 0.5–0.9) as judged by photoluminescence and transient absorption measurements, while in specific cases formation of long-lived triplet states can be identified. Organic microparticles of the trimers are efficiently formed in aqueous solution by nanoprecipitation, and rapid photoinduced electron release/injection to the solvent is evidenced spectroscopically. The results indicate that organic small molecule structures with D–A–D structures pave a new pathway for photocatalytic solar-to-chemical energy conversion of novel small organic molecules.