A new intelligent algorithm for dynamic facility layout problem in state of fuzzy constraints

In the present paper, the dynamic facilities layout problem is studied in presence of ambiguity of information flow. Product demand (and consequently material flow) is defined as fuzzy numbers with different membership functions. The problem is modeled in fuzzy programming. Three models of expected value, chance-constrained programming and dependent-chance programming and two hybrid intelligent algorithms are then presented. At the end, efficiency of algorithms for solving fuzzy models of dynamic facilities layout is shown through some numerical examples.     

A Machine Learning-Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite-Based Materials

The present work is designed to discover new perovskite-based materials, which are expected to show high mechanical stability during their applications, using machine learning (ML) techniques, and based on the Pugh's criterion for distinguishing brittle and ductile behaviors. For this purpose, ML models to predict the moduli of materials, bulk (B) and shear (G), are built using their crystal structure and composition information. The ML process is initiated with the information of 5663 compounds, including composition, crystal structure and moduli, as listed in AFLOW database. Following a procedure of data characteristics, feature generation, feature processing, training, and testing, the ML models are constructed with acceptable accuracy (tenfold cross-validation R² score of 0.90 and 0.89 for B and G, respectively). The validation process of the models, which is conducted using the corresponding density functional theory calculations, reveals that these models are reliable to be employed in a large-scale screening process. Indeed, the B- and G-based ML models are incorporated in a screening process, and this is also conjugated with other screening criterions, to find out thermodynamically stable and formable perovskite-based materials with improved mechanical performance.     

Prevalence of virulent Yersinia enterocolitica in bulk raw milk and retail cheese in northern-west of Iran

To determine the prevalence of virulent Yersinia enterocolitica, 554 samples consisting of 354 bulk raw milks and 200 traditional cheeses were collected from different parts of Eastern-Azerbaijan province, during a 23-month period from 2008 to 2010. The occurrence of virulent strains of Y. enterocolitica in samples enriched in peptone sorbitol bile broth (PSBB) was evaluated via the detection of attachment invasion locus (ail) gene by PCR. The viability of virulent Y. enterocolitica in the PCR-positive samples was tested using conventional culture method and the isolates were confirmed by the second-phase ail-PCR. According to the results, 8.66% of total samples including 7.62% of bulk raw milks and 10.5% of raw milk cheeses were found ail-positive by PCR method; subsequently Y. enterocolitica was isolated by the culture method and confirmed by the second phase ail-PCR in 2.88% of total samples including 2.26% of raw milks and 4% of cheese samples. It was concluded that, a sample enrichment followed by ail-PCR was more sensitive and robust to detect and distinguish the virulent strains of Y. enterocolitica compared to the conventional culture method.     

Theoretical calculation simulation studies of ABV nuclear reactor coupled with desalination system

In this paper, research has been conducted on the floating type nuclear power plant named as ABV reactor which is designed for district heating, power, and sea water desalination by OKBM facility at Russia. This reactor was tested under different thermal loads during the designing phase, and three modules have been investigated. Theoretical calculations and simulation studies have been performed on these three modules having specifications as ABV‐6M with 47MW_th, ABV‐6 with 38MW_th, and ABV‐3 with 18MW_th.The results obtained from these modules have been calculated mathematically and verified by simulation. We have compared the originally derived data of ABV desalination system with our theoretical and simulation analysis. The results from two desalination techniques including RO and RO + MED have been calculated and are presented in this paper with details. The results obtained from both analysis show that the efficiency of ABV nuclear reactor desalination system increases with the decrease in corresponding water cost ratio. Copyright © 2015 John Wiley & Sons, Ltd.     

Experimental and Numerical Investigation on Free Convection From a Horizontal Cylinder Located Above an Adiabatic Surface

Steady two-dimensional free convection heat transfer from an isothermal horizontal cylinder located above an adiabatic horizontal surface is studied experimentally and numerically. Experiments are carried out using a Mach–Zehnder interferometer for the ratios of cylinder spacing from the adiabatic surface to its diameter L/D = 0, 0.1, 0.2, 0.3, 0.5, 0.7, and 0.9 and the Rayleigh number range of 500 to 15,000. Also, a specifically developed computer code based on the finite-volume method, the SIMPLE algorithm, and nonorthogonal discretization grid system is used for the solution of the mass-, momentum-, and energy-governing equations for the Rayleigh numbers ranging from 100 to 100,000 and L/D ranging from 0.1 to 1.7. The effects of the Rayleigh number and spacing from the adiabatic surface on both local and average Nusselt numbers around the cylinder are investigated. A correlation based on the numerical data for the average Nusselt number of the cylinder as a function of Rayleigh number and L/D is presented in the aforementioned ranges.     

Modeling the free convection heat transfer in a partitioned cavity using ANFIS

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used to predict the free convection in a partitioned cavity consisting of an adiabatic partition. The main focus of the present paper is to consider the effects of partition angle and Rayleigh number variation on average heat transfer in the partitioned cavity. The training data for optimizing the ANFIS structure is obtained experimentally. For the best ANFIS structure obtained in this study, the mean relative errors of the train and test data were found to be 0.055% and 1.735% respectively, which shows that ANFIS can predict the experimental results precisely.     

Toward intelligent transient stability enhancement in inverter-based microgrids

Neural Computing and Applications - Nowadays, the concept of multiple inverter-interfaced distributed generations (IIDGs)-based MG is recognized as a renowned notion. Encountering unexpected...     

Flexible skew-symmetric shape model for shape representation, classification, and sampling.

Skewness of shape data often arises in applications (e.g., medical image analysis) and is usually overlooked in statistical shape models. In such cases, a Gaussian assumption is unrealistic and a formulation of a general shape model which accounts for skewness is in order. In this paper, we present a novel statistical method for shape modeling, which we refer to as the flexible skew-symmetric shape model (FSSM). The model is sufficiently flexible to accommodate a departure from Gaussianity of the data and is fairly general to learn a "mean shape" (template), with a potential for classification and random generation of new realizations of a given shape. Robustness to skewness results from deriving the FSSM from an extended class of flexible skew-symmetric distributions. In addition, we demonstrate that the model allows us to extract principal curves in a point cloud. The idea is to view a shape as a realization of a spatial random process and to subsequently learn a shape distribution which captures the inherent variability of realizations, provided they remain, with high probability, within a certain neighborhood range around a mean. Specifically, given shape realizations, FSSM is formulated as a joint bimodal distribution of angle and distance from the centroid of an aggregate of random points. Mean shape is recovered from the modes of the distribution, while the maximum likelihood criterion is employed for classification.     

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.