Photographing impact of molten molybdenum particles in a plasma spray

Plasma-sprayed molten molybdenum particles (∼40 μm in diameter) were photographed impinging at high velocity (∼140 m/s) on a glass substrate at room temperature. An optical sensor detected thermal radiation emitted by a droplet as it approached the substrate and activated a time delay unit. After a selected time interval, an Nd:YAG laser was triggered, emitting a 5 ns pulse that provided illumination for a charge-coupled device (CCD) camera to photograph the impacting droplet through a long-range microscope. By varying the delay before pulsing the laser, different stages of droplet deformation were recorded. Impacting droplets spread into a thin circular film that ruptured and broke into small fragments. An optical detector recording thermal radiation from the impacting droplet gave a signal that increased as the droplet spread out, reached a maximum when the liquid film began to rupture, and decreased as portions of the droplet recoiled because of surface tension and then flew out of view of the photodetector.     

EVALUATION OF SURFACE FORCE-PORE FLOW AND MODIFIED SURFACE FORCE-PORE FLOW MODELS FOR REVERSE OSMOSIS TRANSPORT

The surface force-pore flow (SF-PF) model of reverse osmosis transport and the extended and modified form (the MD-SF-PF model) have been employed to predict the performance of four aromatic polyamide (FilmTec, FT30)reverse osmosis membranes. The evaluation is based on a comparison of model predictions with experimental data. Dilute sodium chloride-water solution experimental data were used to estimate model parameters. The models are then used to predict flux and separation at various operating pressures and concentrations. Membrane performance (i.e., separation and permeate flux) can be well predicted by the MD-SF-PF model while the SF-PF model predicts the performance for the sodium chloride system less satisfactorily.     

Energy and load balancing routing protocol for IoT

Due to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods.     

A meshless method for solving 1D time-dependent heat source problem

A novel meshless numerical procedure based on the method of fundamental solutions (MFS) and the heat polynomials is proposed for recovering a time-dependent heat source and the boundary data simultaneously in an inverse heat conduction problem (IHCP). We will transform the problem into a homogeneous IHCP and initial value problems for the first-order ordinary differential equation. An improved method of MFS is used to solve the IHCP and a finite difference method is applied for solving the initial value problems. The advantage of applying the proposed meshless numerical scheme is producing the shape functions which provide the important delta function property to ensure that the essential conditions are fulfilled. Numerical experiments for some examples are provided to show the effectiveness of the proposed algorithm.     

An efficient classifier to diagnose of schizophrenia based on the EEG signals

In this paper, electroencephalogram (EEG) signals of 13 schizophrenic patients and 18 age-matched control participants are analyzed with the objective of classifying the two groups. For each case, multi-channels (22 electrodes) scalp EEG is recorded. Several features including autoregressive (AR) model parameters, band power and fractal dimension are extracted from the recorded signals. Leave-one (participant)-out cross validation is used to have an accurate estimation for the separability of the two groups. Boosted version of Direct Linear Discriminant Analysis (BDLDA) is selected as an efficient classifier which applied on the extracted features. To have comparison, classifiers such as standard LDA, Adaboost, support vector machine (SVM), and fuzzy SVM (FSVM) are applied on the features. Results show that the BDLDA is more discriminative than others such that their classification rates are reported 87.51%, 85.36% and 85.41% for the BDLDA, LDA, Adaboost, respectively. Results of SVM and FSVM classifiers were lower than 50% accuracy because they are more sensitive to outlier instances. In order to determine robustness of the suggested classifier, noises with different amplitudes are added to the test feature vectors and robustness of the BDLDA was higher than the other compared classifiers.     

Experimental and fundamental critical analysis of diffusion model of airflow drying

Scientific literature of agromaterial drying present contradictory conclusions in terms of the kinetic effect of airflow velocity. Some authors confirmed that it does not trigger any modification of drying, while some articles tried to establish empirical models of the effective diffusivity D_eff versus the airflow velocity, what is fundamentally erroneous. By analyzing internal and external transfer phenomena, this research aimed at recognizing that once air velocity is higher than a critical airflow velocity (CAV), the internal transfers become the limiting phenomenon. CAV depends on the effective diffusivity and the product size. It was calculated in the cases of two studied raw materials (apple and carrot), differently textured by instant controlled pressure drop (DIC). Values of CAV greatly depend on diffusivity of water within the matrix. At temperature T?=?40°C, they were 1?m/s for untreated carrot and 2.1?m/s for DIC-textured carrot, whose D_eff values were 1.31 and about 3?×?10^?10?m²/s, respectively. Also, at temperature T?=?40°C, they were 2.1?m/s for untreated apple and 3?m/s for DIC-textured apple, whose D_eff were 1.4 and about 10.4?×?10^?10?m²/s, respectively.     

Assessment of egg freshness by prediction of Haugh unit and albumen pH using an artificial neural network

Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were collected and stored for 30 days under room conditions (25?±?2 °C and 20?±?3%). After imaging, every other day, some internal and external quality characteristics including yolk height, yolk and albumen pH, yolk and albumen density and Haugh unit (HU) were measured as destructive parameters and area index (D) egg weight as non-destructive parameters. Based on Pearson correlation coefficients, area index were significantly correlated with all destructive variables (p?<?0.05). In order to predict egg freshness, artificial neural network was trained by Levenberg–Marquardt, scaled conjugate gradient, Bayesian regulation, resilient and radial basis algorithms. The best result of artificial neural network for HU and albumen pH prediction was achieved by the Levenberg–Marquardt algorithm with the correlation coefficient of 0.93 and 0.87, respectively.     

Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations

Viscosity is a parameter that plays a pivotal role in reservoir fluid estimations. Several approaches have been presented in the literature (Beal, 1946; Khan et al, 1987; Beggs and Robinson, 1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959; Elsharkawy and Alikhan, 1999; Labedi, 1992) for predicting the viscosity of crude oil. However, the results obtained by these methods have significant errors when compared with the experimental data. In this study a robust artificial neural network (ANN) code was developed in the MATLAB software environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the three well-established semi-empirical equations (Khan et al, 1987; Elsharkawy and Alikhan, 1999; Labedi, 1992) were compared with the experimental data. The prediction procedure was carried out at three different regimes: at, above and below the bubble-point pressure using the PVT data of 57 samples collected from central, southern and offshore oil fields of Iran. It is confirmed that in comparison with the models previously published in literature, the ANN model has a better accuracy and performance in predicting the viscosity of Iranian crudes.