A new predictor parameter for production rate of ornamental stones

Bulletin of Engineering Geology and the Environment - In this study, a new predictor parameter (NPP), which is based on the product of uniaxial compressive strength (UCS) and Mohs hardness (MH),...     

Core–shell structured Cu@Pt nanoparticles as effective electrocatalyst for ethanol oxidation in alkaline medium

Core–shell nanoparticles of Cu@Pt/C electrocatalysts were synthesized using various Pt:Cu atomic ratios with NaBH₄ as a reducing agent. The crystal structure and surface morphology were studied using X-ray diffraction (XRD) and transmission electron microscopy (TEM). The incorporation of copper in Cu@Pt/C electrocatalysts was found to shift all Pt diffraction planes in the negative direction with expanding the crystal lattice dimensions. The electrocatalytic activity of various Cu@Pt/C electrocatalysts was investigated using cyclic voltammetry, chronoamperometry and electrochemical impedance spectroscopy. Cu@Pt/C electrocatalysts containing Pt:Cu atomic ratios of 1:0.5 and 1:2 showed an enhanced electrochemical performance for ethanol oxidation when related to Pt/C.     

The Effect of Ti on Mechanical Properties of Extruded In-Situ Al-15 pct Mg2Si Composite

This work was carried out to investigate the effect of different Ti concentrations as a modifying agent on the microstructure and tensile properties of an in-situ Al-15 pctMg₂Si composite. Cast, modified, and homogenized small ingots were extruded at 753 K (480 °C) at the extrusion ratio of 18:1 and ram speed of 1 mm/s. Various techniques including metallography, tensile testing, and scanning electron microscopy were used to characterize the mechanical behavior, microstructural observations, and fracture mechanisms of this composite. The results showed that 0.5 pctTi addition and homogenizing treatment were highly effective in modifying Mg₂Si particles. The results also exhibited that the addition of Ti up to 0.5 pct increases both ultimate tensile strength (UTS) and tensile elongation values. The highest UTS and elongation values were found to be 245 MPa and 9.5 pct for homogenized and extruded Al-15 pctMg₂Si-0.5 pctTi composite, respectively. Fracture surface examinations revealed a transition from brittle fracture mode in the as-cast composite to ductile fracture in homogenized and extruded specimens. This can be attributed to the changes in size and morphology of Mg₂Si intermetallic and porosity content.     

A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches

Computational Economics - This paper has scrutinized the process of testing market efficiency, data generation process and the feasibility of market prediction with a detailed, coherent and...     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

Synthesis of Ion Imprinted Polymers by Copolymerization of Zn(II) and Al(III)8-hydroxy Quinolone Complexes with Divinylbenzene and Methacryclic Acid

In this research work, Zinc(II) and Aluminum(III)-IIP's were synthesized by optimizing the amount of methacrylic acid as monomer, divinylbenzene as cross-linker. The IIP's were functionalized with 8-hydroxy quinolone complexes of the two metal ions under thermal conditions by copolymerization with monomer and cross-linker. The IIP's and Non-IIP's were characterized using FT-IR, TGA, and SEM analysis. A quite remarkable difference in the size was observed of the polymers (Zn(II) 1.0 µm and Al(III) 0.1 µm). A stronger affinity was observed with IIP in comparison with Non-IIP at pH 3.1 and 4.5 for Zn(II) and Al(III) ions on their respective polymers.     

Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability against probabilistic events. The predominant approach to quantify the impact of natural disasters on infrastructure systems is the Monte Carlo approach, which still suffers from high computational cost, especially when applied to large systems. This article presents a deep learning framework for accelerating seismic reliability analysis, on a transportation network case study. Two distinct deep neural network surrogates are constructed and studied: (1) a classifier surrogate that speeds up the connectivity determination of networks and (2) an end‐to‐end surrogate that replaces modules such as roadway status realization, connectivity determination, and connectivity averaging. Numerical results from k‐terminal connectivity analysis of a California transportation network subject to a probabilistic earthquake event demonstrate the effectiveness of the proposed surrogates in accelerating reliability analysis while achieving accuracies of at least 99%.     

Exploring the best sequence LSTM modeling architecture for flood prediction

Neural Computing and Applications - Accurate and efficient models for rainfall–runoff (RR) simulations are crucial for flood risk management. Recently, the success of the recurrent neural...