Assessment of the thermodynamic,environmental and economic output of agro-ecosystems: onion set versus onion production

Clean Technologies and Environmental Policy - Farmers sow onion seeds either for the purpose of preparing onion sets or for producing onions. Thermodynamic, economic and environmental analyses were...     

Toward the design of robust multilayer graphene: mechanistic understanding of the role played by interlayer interactions

Journal of Materials Science - In the literature, while the mechanical responses of multilayer graphene have been investigated during uniaxial tensile stretch and nanoindentation tests, fundamental...     

Synthesis and Characterization of Magnetic Functionalized Ni and Cu Nano Catalysts and Their Application in Oxidation,Oxidative Coupling and Various Multi-Component Reactions

Catalysis Letters - Two magnetic nano catalysts of nickel and copper, Fe3O4@SiO2@DOP-BenPyr-M(II), (M=Ni and Cu) have been synthesized. These catalysts were applied as recoverable, efficient and...     

A latency-aware and energy-efficient computation offloading in mobile fog computing: a hidden Markov model-based approach

In recent years, Fog Computing (FC) is known as a good infrastructure for the Internet of Things (IoT). Using this architecture for the mobile applications in the IoT is named the Mobile Fog Computing (MFC). If we assume that an application includes some modules, thus, these modules can be sent to the Fog or Cloud layer because of the resource limitation or increased runtime at the mobile. This increases the efficiency of the whole system. As data is entered sequentially, and the input is given to the modules, the number of executable modules increases. So, this research is conducted to find the best place in order to run the modules that can be on the mobile, Fog, or Cloud. According to the proposed method, when the modules arrive at gateway, then, a Hidden Markov model Auto-scaling Offloading (HMAO) finds the best destination to execute the module to create a compromise between the energy consumption and execution time of the modules. The evaluation results obtained regarding the parameters of the energy consumption, execution cost, delay, and network resource usage shows that the proposed method on average is better than the local execution, First-Fit (FF), and Q-learning based method.

     

RenyiBS: Renyi entropy basis selection from wavelet packet decomposition tree for phonocardiogram classification

The Journal of Supercomputing - Wavelet packet transform (WPT) is a powerful mathematical tool for analyzing nonlinear biomedical signals, such as phonocardiogram (PCG). WPT decomposes a PCG signal...     

Ablation behavior of elastomeric insulator based on nitrile rubber containing silica or silica-clay aerogels

Iranian Polymer Journal - Nitrile-based nanocomposite heat insulators are very attractive materials, due to their higher deformation bearing in special applications such as high temperature and...     

Thermodynamic modeling of PVTx properties for several water/hydrocarbon systems in near-critical and supercritical conditions

Both the equation of state-excess Gibbs energy (EoS/G ^E ) model and the cubic plus association (CPA) equation of state (EoS) are compared in this study with respect to their accuracy in the correlation of PVTx for systems such as water/methanol, water/ethanol, water/benzene, water/toluene, water/methane, water/n-butane, water/n-pentane, water/n-hexane, water/heptane, and water/octane, in supercritical conditions within temperature and pressure ranges of (573–698 K) and (7.0–276.0 MPa), respectively. In the proposed EoS/G ^E model, Peng-Robinson (PR) equation of state, linear combination Vidal-Michelsen (LCVM) and Wong-Sandler (WS) mixing rules in conjunction with UNIQUAC activity coefficient model were used. Correlation of both CPA and EoS/G ^E models was evaluated by comparing the results with the experimental data. Average absolute relative deviation (AARD) for WS, LCVM, and CPA was found to be 2.99, 11.11 and 5.14%, respectively, indicating better correlation of WS model with the experimental data.     

An experimental investigation of pool boiling characteristics of alumina-water nanofluid over micro-/nanostructured surfaces

Abstract

Experiments were conducted to investigate the nucleate pool boiling heat transfer of pure water and alumina/water nanofluids on different micro- and nanostructured surfaces prepared via the thermal spray coating method. Results indicate that nanofluids boiling on all the test surfaces led to critical heat flux (CHF) values greater than that obtained for the base fluid (i.e., water). Higher roughness value, however, led to higher CHF values in boiling over the surfaces. Another finding of this study indicated that CHF values obtained with boiling on Cu-coated micro- and nanosurfaces were identical although the heat transfer coefficient (HTC) values obtained for boiling on the micro-structure surface were higher than those obtained for a nanostructured surface with almost the same roughness. A series of consecutive nanofluid boiling cycles were also performed on the aluminum-coated nanostructured surface. The CHF value obtained for water boiling on the surface undergoing repeated nanofluid boiling cycles was by 27% higher than that obtained for a clean surface although the relevant HTC values were nearly identical.     

Optimum shape design of arch dams for earthquake loading using a fuzzy inference system and wavelet neural networks

An efficient methodology is proposed to find the optimum shape of arch dams considering fluid-structure interaction subject to earthquake loading. The earthquake load is considered by time variant ground acceleration applied in the upstream–downstream direction of the arch dam. The optimization is carried out by particle swarm optimization, employing real values of design variables. To reduce the computational cost of the optimization process, two strategies are adopted. In the first strategy, the most influential design variables on arch-dam response from original variables are selected using an adaptive neuro-fuzzy inference system. In the second, arch-dam response is predicted by a properly trained wavelet radial basis function neural network employing the influential design variables as the inputs. In order to assess the effectiveness of the suggested methodology, a real arch dam is considered as a test example. The numerical results demonstrate the computational advantages of the proposed methodology for the optimal design of arch dams.