Evaluation of suspended load transport rate using transport formulas and artificial neural network models （Case study： Chelchay Catchment）＊

Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r = 0.69) and lower Root Mean Square Error (RMSE = 0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.     

The FN method for solving the critical problem for a slab with a finite reflector

The F_N method is used to solve the critical problem for a slab reactor with a finite reflector. Numerical results for the critical thickness are shown for different values of the mean number of secondary neutrons per collision and reflector thickness using various orders of the F_N approximation. The results show the capability of the method to provide accurate solutions.     

Swallow swarm optimization algorithm: a new method to optimization

This paper presents an exposition of a new method of swarm intelligence–based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO.     

Free convection of water–Fe3O4 nanofluid in an inclined cavity subjected to a magnetic field: CFD modeling,sensitivity analysis

Modeling of natural convection heat transfer in an inclined C-shape cavity is studied in this paper. The enclosure is filled with H₂O-Fe₃O₄ nanofluid under the effect of magnetic field. The operating range of parameters used in this study were Hartmann number (Ha) from 0 to 80, Rayleigh number (Ra) from 1E2 to 1E6, nanoparticles volume fraction (φ) from 0 to 0.1, inclination angle (α) from 0 to 90 deg, and aspect ratio (AR) from 0.2 to 0.8. The employed model is solved using CFD tools based on the finite element method. The comparison with reference experimental data indicated the accuracy and generalization capability of the model. In addition, a novel correlation and an artificial neural network (ANN) model were productively developed for predicting Nu number as a function of aforementioned independent variables. The influence of the model parameters on the Nu number is precisely presented and discussed. It is shown that Ra number and aspect ratio have more impact on Nu than the other variables.     

Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach

In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.     

Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems

The aim of this study is to design adaptive neural-fuzzy inference system (ANFIS) model and fuzzy expert system for determination of concrete mix designs and finally compare their results. Idea of these systems based on two surveys: first, ACI structures and principles, second a concrete mix designs dataset that collected via Prof. I-Cheng Yeh. Datasets that loaded in to ANFIS has 552 mix designs and based on ACI mix designs. Moreover, in this study, we have designed fuzzy expert system. Input fields of fuzzy expert system are Slump, Maximum Size of Aggregate (D _max), Concrete Compressive Strength (CCS), and Fineness Modulus. Output fields are quantities of water, cement, fine aggregate (F.A.) and coarse aggregate (C.A.). In the ANFIS model, we have four layers (four ANFIS models): the first layer takes values of D _max and Slump and then determines the quantity of Water, the second layer takes values of Water (computed in the past layer) and CCS then measures the value of Cement, the third layer takes values of D _max and Slump to compute C.A. and the fourth layer takes values of Water, Cement, and C.A. (determined in past layers) and then measures the value of F.A. When these systems were designed and tested, comparison between two systems (FIS and ANFIS) results showed that results of ANFIS model are better than fuzzy expert system’s results. In the ANFIS model, for Water output field, training and average testing errors are 0.86 and 0.8. For cement field, training error and average testing error are in the orders of 0.21 and 0.22. Training and average testing error of C.A. are in the orders of 0.0001 and 0.0004 and finally, training and average testing errors of F.A. are in the orders of 0.0049 and 0.0063. Results of fuzzy expert system in comparison to ACI results follow average errors: average error of Water, Cement, C.A., and F.A. are in the orders of 9.5%, 27.6%, 96.5%, and 49%.     

Chemical Composition and Antibacterial Activity of Essential Oils of Iranian Herbs Against Staphylococcus Aureus Isolated from Milk

Chemical composition and antibacterial activity of essential oils from three endemic plants including, Thymus daenensis Celak, Dracocephalum multicaule Montbr & Auch., Satureja bachtiarica Bunge and one native plant, Tanacetum polycephalum Schultz-Bip were determined. Volatile oils analyzed using gas chromatography and gas chromatography-mass spectrometry. The in-vitro antibacterial activity was performed by agar disc diffusion and micro-dilution assays. The results revealed distinct differences in the compounds depending on sample plant. Among the plant species screened, essential oils of S. bachtiarica and T. daenensis showed highest antibacterial activity against Staphylococcus aureus.     

Liver Disease: Induction,Progression, Immunological Mechanisms,and Therapeutic Interventions

The liver is an organ with impressive regenerative potential and has been shown to heal sizable portions after their removal. However, certain diseases can overstimulate its potential to self-heal and cause excessive cellular matrix and collagen buildup. Decompensation of liver fibrosis leads to cirrhosis, a buildup of fibrotic ECM that impedes the liver’s ability to efficiently exchange fluid. This review summarizes the complex immunological activities in different liver diseases, and how failure to maintain liver homeostasis leads to progressive fibrotic tissue development. We also discuss a variety of pathologies that lead to liver cirrhosis, such as alcoholic liver disease and chronic hepatitis B virus (HBV). Mesenchymal stem cells are widely studied for their potential in tissue replacement and engineering. Herein, we discuss the potential of MSCs to regulate immune response and alter the disease state. Substantial efforts have been performed in preclinical animal testing, showing promising results following inhibition of host immunity. Finally, we outline the current state of clinical trials with mesenchymal stem cells and other cellular and non-cellular therapies as they relate to the detection and treatment of liver cirrhosis.     

The PTEN/MMAC1 tumor suppressor phosphatase functions as a negative regulator of the phosphoinositide 3-kinase/Akt pathway

The PTEN/MMAC1 phosphatase is a tumor suppressor gene implicated in a wide range of human cancers. Here we provide biochemical and functional evidence that PTEN/MMAC1 acts a negative regulator of the phosphoinositide 3-kinase (PI3-kinase)/Akt pathway. PTEN/MMAC1 impairs activation of endogenous Akt in cells and inhibits phosphorylation of 4E-BP1, a downstream target of the PI3-kinase/Akt pathway involved in protein translation, whereas a catalytically inactive, dominant negative PTEN/MMAC1 mutant enhances 4E-BP1 phosphorylation. In addition, PTEN/MMAC1 represses gene expression in a manner that is rescued by Akt but not PI3-kinase. Finally, higher levels of Akt activation are observed in human prostate cancer cell lines and xenografts lacking PTEN/MMAC1 expression when compared with PTEN/MMAC1-positive prostate tumors or normal prostate tissue. Because constitutive activation of either PI3-kinase or Akt is known to induce cellular transformation, an increase in the activation of this pathway caused by mutations in PTEN/MMAC1 provides a potential mechanism for its tumor suppressor function.     

Quantifying walking capability: a novel aggregated index based on spatial perspective and analyses

Urbanization and modernization of services in the world triggers decrement in the physical activities of citizens. The most common solution for this decrement is improving walking capacity in neighborhoods to engage the citizens in walking activity. In this research, a novel model is presented to quantify walkability based on several necessary criteria and indicators considering the physical and non-physical conditions. For this model, promising sciences, including GIS, RS, and MCDM, are utilized to compute indicators and aggregate them in the form of a spatial index in a central region of Tehran, Iran, at the neighborhood scale. For evaluating the result, the obtained walkability level is compared to the physical structure of neighborhoods, previous works, housing price along with some other socio-economic parameters, and municipal action plan in the neighborhoods. These evaluations show that the outcome of the proposed model is a robust tool for walkability planning.