Building change detection after earthquake using multi-criteria decision analysis based on extracted information from high spatial resolution satellite images

Change detection of ground surface objects can provide essential and precious information for experts in the fields of Geomatics, emergency management, urban management, agriculture, and forestry. Space-borne remote-sensing images are one of the main sources for change detection. Various change detection methods have been proposed on remote-sensing applications. However, often, no single efficient method can be selected for a case study because the existing methods sometimes have good performance and sometimes perform poorly. Therefore, it is necessary to propose an integrated change detection method according to some change detection methods. Multi-criteria decision analysis is a powerful framework that can integrate several criteria that may be in contrast to each other. In this study, a multi-criteria decision analysis framework was used to integrate the spectral, textural, and transformed features for detecting building changes with the help of high spatial resolution satellite images. First, the spectral, textural, and transformed features were extracted from the pre- and post-event satellite images. Second, the spectral, textural, and transformed factor maps were produced by entering the related features to three separate Adaptive Network-Based Fuzzy Inference Systems (ANFIS). Third, the ANFIS model was used again to integrate the mentioned factor maps for producing the preliminary building change map. And finally, a comprehensive sensitivity analysis was carried out to determine the proper parameters of the ANFIS models leading to accurate change detection results. The proposed method was tested on the earthquake data set of Bam City in Iran. The achieved results indicated an overall accuracy of 89.62% for identifying the changed and unchanged building regions. Moreover, the obtained results proved the efficiency and accuracy of the proposed method with respect to other implemented methods regarding the Bam earthquake. Furthermore, the aggregation of the spectral, transformed, and textural features resulted in improving the change detection accuracy by about 5–15%, compared with the accuracy of every one of them for the mentioned purpose.     

Aggregation and clogging phenomena of rigid microparticles in microfluidics

We developed a numerical tool to investigate the phenomena of aggregation and clogging of rigid microparticles suspended in a Newtonian fluid transported through a straight microchannel. In a first step, we implement a time-dependent one-way coupling Discrete Element Method (DEM) technique to simulate the movement and effect of adhesion on rigid microparticles in two- and three-dimensional computational domains. The Johnson–Kendall–Roberts (JKR) theory of adhesion is applied to investigate the contact mechanics of particle–particle and particle–wall interactions. Using the one-way coupled solver, the agglomeration, aggregation and deposition behavior of the microparticles is studied by varying the Reynolds number and the particle adhesion. In a second step, we apply a two-way coupling CFD–DEM approach, which solves the equation of motion for each particle, and transfers the force field corresponding to particle–fluid interactions to the CFD toolbox OpenFOAM. Results for the one-way (DEM) and two-way (CFD–DEM) coupling techniques are compared in terms of aggregate size, aggregate percentages, spatial and temporal evaluation of aggregates in 2D and 3D. We conclude that two-way coupling is the more realistic approach, which can accurately capture the particle–fluid dynamics in microfluidic applications.     

Development of artificial neural networks for real time, in situ ellipsometry data reduction

Development of real time in situ monitoring and control of thin film depositions using ellipsometry requires both data acquisition and processing to be rapid. Present speeds of measurement and computation of basic parameters, Ψ and Δ, are sufficient for data acquisition which is essentially real time. However, computation of film parameters, such as thickness and optical properties, generally cannot keep up with the incoming data and must be performed in a batch mode after the deposition.

This work describes the development of enhanced, high speed data reduction algorithms using artificial neural networks (ANN). The networks are trained using computed data and subsequently give values of film parameters in the millisecond time regime. The ANN outputs are used as initial estimates in a variably damped least squares algorithm for accuracy improvement. The combination of these two algorithms provides very accurate solutions in 75 ms per point on a DEC VAX 8800 multiprocessor system running at a combined 12 Mips. This speed is suitable for real time film monitoring and control for growth rates up to 10 nm per second. Results for fixed angle of incidence, single wavelength, in situ data for Ni deposited on BK7 substrates are presented.     

Advances in the Application of Magnetic Nanoparticles for Sensing

Magnetic nanoparticles (MNPs) are of high significance in sensing as they provide viable solutions to the enduring challenges related to lower detection limits and nonspecific effects. The rapid expansion in the applications of MNPs creates a need to overview the current state of the field of MNPs for sensing applications. In this review, the trends and concepts in the literature are critically appraised in terms of the opportunities and limitations of MNPs used for the most advanced sensing applications. The latest progress in MNP sensor technologies is overviewed with a focus on MNP structures and properties, as well as the strategies of incorporating these MNPs into devices. By looking at recent synthetic advancements, and the key challenges that face nanoparticle‐based sensors, this review aims to outline how to design, synthesize, and use MNPs to make the most effective and sensitive sensors.     

Study the effect of distribution of density of states on the depletion width of organic Schottky contacts

Organic semiconductor to metal Schottky contacts have been widely used in electronic devices and to investigate the properties of organic semiconductors. In designing and characterizing these devices the full depletion approximation is used. The analytical and numerical simulations presented in this paper suggest that this approximation is not generally valid. Simulations of a Schottky contact between regioregular-poly 3 hexylthiophene (rr-P3HT) and aluminum show that this approximation becomes worse as molecular order decreases, with the potential profile increasingly deviating from the expected quadratic function of position. Also the depletion width decreasing well below that predicted using the approximation. In this work the slope of the band tail is used as a measure of disorder.     

A kinetic study on the reduction of manganese III in presence of citric acid

The rate of reduction of manganese III stabilised by citric acid under variable conditions of Mn(III) concentration, ionic strength, pH and temperature in buffered media was found to obey a second order rate equation, first order with respect to Mn(III) autocatalysed by Mn(II). The reduction was found to proceed to an equilibrium value. The observed rate constants were found to follow a linear relationship with hydrogen ion concentration. Interpretation of results is based on the assumption that there are two reducible species in equilibrium; the aquocomplex and its hydrolysed form. A reaction mechanism is proposed to account for the experimental results. The overall energy (ΔE) and entropy (ΔS) of activation are compared with those for the reduction of Mn(III) in presence of other organic substrates.     

Hydrogen production by silica membrane reactor during dehydrogenation of methylcyclohexane: CFD analysis

A comprehensive computational fluid dynamic model has been developed using COMSOL Multiphysics 5.4 software to predict the behavior of a membrane reactor in dehydrogenation of methylcyclohexane for hydrogen production. A reliable reaction kinetic of dehydrogenation reaction and a permeation mechanism of hydrogen through silica membrane have been used in computational fluid dynamic modeling. For performance comparison, an equivalent traditional fixed bed reactor without hydrogen removal has been also modeled. After model validation, it has been used to evaluate the operating parameters effect on the performance of both the silica membrane reactor and the equivalent traditional reactor as well. The operating temperature ranged between 473 and 553 K, pressure between 1 and 2.5 bar, sweep factor from ?6.22 to 25 and feed flow rate from 1 to 5 × 10^?6 mol/s. The membrane reactor performed better than the equivalent traditional reactor, achieving as best result complete methylcyclohexane conversion and 96% hydrogen recovery.     

Hybrid solar parabolic dish power plant and high‐temperature phase change material energy storage system

In this study, a conventional steam power plant with two regenerative boilers is considered, and one of its boilers is replaced with parabolic solar dish collectors and storing the produced thermal energy by the phase change material (PCM) in a storage tank. The results show the necessity of the existence of an auxiliary fired‐gas boiler to provide constant load during the whole 24 hours. The performance of the proposed hybridized system is evaluated through energy and exergy analyses. It was demonstrated that substituting solar collectors with one of the boilers marginally lowers the energy efficiency but increases the exergy efficiency of the whole power plant up to 41.76%. Moreover, it is found out that this hybridization decreases the total irreversibility of the power plant in comparison with the base case, from 51.1 to 47.2 MW. The parametric analysis states that raising the mass flow rate of the heat transfer fluid in the solar collectors not only enhances the system performance but also increases the volume of the PCM tank.     

Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS,MERS and 2019-nCoV literature

Scientometrics - During the current century, each major coronavirus outbreak has triggered a quick and immediate surge of academic publications on its respective topic. The spike in research...