Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling

Water Resources Management - Identifying areas prone to flooding is a key step in flood risk management. The purpose of this study is to develop and present a novel flood susceptibility model based...     

Postbuckling analysis of hydrostatic pressurized FGM microsized shells including strain gradient and stress-driven nonlocal effects

Herein, with the aid of the newly proposed theory of nonlocal strain gradient elasticity, the size-dependent nonlinear buckling and postbuckling behavior of microsized shells made of functionally graded material (FGM) and subjected to hydrostatic pressure is examined. As a consequence, the both nonlocality and strain gradient micro-size dependency are incorporated to an exponential shear deformation shell theory to construct a more comprehensive size-dependent shell model with a refined distribution of shear deformation. The Mori–Tanaka homogenization scheme is utilized to estimate the effective material properties of FGM nanoshells. After deduction of the non-classical governing differential equations via boundary layer theory of shell buckling, a perturbation-based solving process is employed to extract explicit expressions for nonlocal strain gradient stability paths of hydrostatic pressurized FGM microsized shells. It is observed that the nonlocality size effect causes to decrease the critical hydrostatic pressure and associated end-shortening of microsized shells, while the strain gradient size dependency leads to increase them. In addition, it is found that the influence of the internal strain gradient length scale parameter on the nonlinear instability characteristics of hydrostatic pressurized FGM microsized shells is a bit more than that of the nonlocal one.

     

Comparative study of propylene polymerization using monosupported and bisupported titanium‐based Ziegler–Natta catalysts

Heterogeneous Ziegler–Natta systems—MgCl₂ (ethoxide type)/TiCl₄/di‐n‐butyl phthalate (DNBP)/triethylaluminum (TEA)/dimethoxymethylcyclohexylsilane (DMMCHS) and SiO₂/MgCl₂ (ethoxide type)/TiCl₄/DNBP/TEA/DMMCHS—were studied for the polymerization of propylene. The slurry polymerization of propylene was carried out with the catalyst systems in n‐heptane. Both systems performed with optimum activity at a particular [Al]/[DMMCHS]/[Ti] molar ratio. The ratio to reach the highest activity was much lower for the bisupported catalyst system. The productivity of the bisupported catalyst was higher than that of the monosupported one. Polypropylene of a high isotacticity index (II; >96%) was obtained with both systems and did not significantly change with an increasing [Al]/[DMMCHS]/[Ti] molar ratio. The addition of hydrogen as a chain‐transfer agent reduced II of the polymers obtained with both systems. The effect of the polymerization temperature (40–75°C) on the viscosity‐average molecular weight (M_v) and II showed a decrease in both cases. The bisupported catalyst system produced a polymer with higher M_v. The effect of temperature on II was similar for both the monosupported and bisupported systems. A monomer pressure of 2.02 × 10⁵ to 0.8 × 10⁶ Pa increased M_v of the obtained polymer. II of the polymer slightly decreased with increasing monomer pressure. The titanium content of the catalyst was 1.70 and 3.55% for the monosupported and bisupported systems, respectively. The surface area of the bisupported catalyst was higher than that of the monosupported catalyst. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 2220–2226, 2006     

A short-term learning approach based on similarity refinement in content-based image retrieval

Multimedia Tools and Applications - This paper presents a new relevance feedback approach based on similarity refinement. In the proposed approach weight correction of feature’s components is...     

A comprehensive review of past and present vision-based techniques for gait recognition

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available—for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance.     

An evolutionary‐based adaptive neuro‐fuzzy inference system for intelligent short‐term load forecasting

The continuing growth in size and complexity of electric power systems requires the development of applicable load forecasting models to estimate the future electrical energy demands accurately. This paper presents a novel load forecasting approach called genetic‐based adaptive neuro‐fuzzy inference system (GBANFIS) to construct short‐term load forecasting expert systems and controllers. At the first stage, all records of data are searched by a novel genetic algorithm (GA) to find the most suitable feature of inputs to construct the model. Then, determined inputs are fed into the adaptive neuro‐fuzzy inference system to evolve the initial knowledge‐base of the expert system. Finally, the initial knowledge‐base is searched by another robust GA to induce a better cooperation among the rules by rule weight derivation and rule selection mechanisms. We show the superiority and applicability of our approach by applying it to the Iranian monthly electrical energy demand problem and comparing it with the most frequently adopted approaches in this field. Results indicate that GBANFIS outperforms its rival approaches and is a promising tool for dealing with short‐term load forecasting problems.     

Trimethylolpropane trimethacrylate functionalized polypropylene/polyhexene-1 blend with enhanced melt strength

ABSTRACT

This study was aimed at investigating the variations in the melt strength of polypropylene as a result of blending with another polymer having long side branches in the presence of multi-functional agent and radical initiator. The formation of long chain branches was confirmed by strain hardening behavior in extensional viscometry and grafting efficiency data obtained from FTIR spectroscopy. strain hardening behavior, rheological parameters and grafting efficiency kept their ever-increasing trends by increasing the concentration of Trimethylol-propane-trimethacrylate (TMPTMA) until 3 phr, and the inefficiency of TMPTMA in branching reactions at concentrations higher than 3 phr is rooted in homo-polymerization of TMPTMA.     

La–Si–O–N glasses: Part I. Extension of the glass forming region

The nitrogen-rich part of the glass forming region in the La–Si–O–N system has been the subject of a comprehensive study. Glasses were prepared by heating powder mixtures of La metal, Si₃N₄ and SiO₂ in a nitrogen atmosphere at 1650–1800 °C. By this new synthesis route, glasses containing up to 68 e/o of N and 62 e/o of La were prepared, showing that the glass forming region is significantly larger than previously reported. The glasses were characterized by elemental analysis, differential thermal analysis, X-ray powder diffraction, and scanning electron microscopy. They were found to be X-ray amorphous and homogenous, with the majority of them containing small amounts of crystalline La silicides and elemental Si. Glass transition temperatures (T_g) were found to vary between 900 and 1100 °C and crystallization to occur typically 120 °C above T_g. The forming of the glasses was investigated by characterizing samples taken out at various steps of the heating cycle. The results indicate that the glass formation is strongly dependent on reaction kinetics. A strong exothermal reaction occurs at temperatures 900–1100 °C, leading to the formation of assemblies of amorphous and crystalline (oxy)nitride phases that melt upon further heating at 1650–1800 °C.     

Regionalization of Rainfall Intensity-Duration-Frequency using a Simple Scaling Model

Design storm is one of the most important tools to design hydraulic structures, hydrologic system and watershed management, mostly extracted by intensity- duration - frequency (IDF) curves for a given specific duration and return period. As for conventional methods to calculate IDF curves, the precipitation should be recorded for different durations so that foregoing curves can be extracted. Such data can be collected from rain gauge stations. In many areas, just daily precipitation data are available by which IDF curves cannot be extracted as per conventional methods. The aim of this research is to make IDF curves for short-term durations according to time scaling model as well as daily rainfalls. The relationships of this method are characterized with three variables including mean (μ ₂₄) and standard deviation (σ ₂₄) of daily rainfall intensity, and scaling exponent (H) by which all IDF curves might be drawn. The method used in present paper entails for less computational steps than conventional methods and by far has low parameters considerably than others in turn increases reliability. Scaling method is used to extract the IDF curves in rain-gauge stations in Khuzestan province located in southwest Iran and results proved the efficiency and robustness of the scaling method. Also ability of scaling concept method was examined in constructing of regional IDF.     

A hybrid evolutionary approach to segmentation of non-stationary signals

Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varri?s and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm.