Centering predictor variables in cross-sectional multilevel models: A new look at an old issue.

Appropriately centering Level 1 predictors is vital to the interpretation of intercept and slope parameters in multilevel models (MLMs). The issue of centering has been discussed in the literature, but it is still widely misunderstood. The purpose of this article is to provide a detailed overview of grand mean centering and group mean centering in the context of 2-level MLMs. The authors begin with a basic overview of centering and explore the differences between grand and group mean centering in the context of some prototypical research questions. Empirical analyses of artificial data sets are used to illustrate key points throughout. The article provides a number of practical recommendations designed to facilitate centering decisions in MLM applications. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Fatigue life estimation of MD36 and MD523 bogies based on damage accumulation and random fatigue theory

Bogies are one of the multifunctional parts of trains which are extremely subjected to random loads. This type of oscillating and random excitation arises from irregularities of the track including rail surface vertical roughness, rail joints, variance in super-elevation, and also wheel imperfections like wheel flats and unbalancy. Since most of the prementioned sources have random nature, a random based theory should be applied for fatigue life estimation of the bogie frame. Two methods of fatigue life estimation are investigated in this paper. The first approach which is being implemented in time domain is based on the damage accumulation (DA) approach. Using Monte-Carlo simulation algorithm, the rail surface roughness is generated. Finite element (FE) model of the bogie is subjected to the generated random excitation in the first approach and the stress time histories are obtained, and consequently the fatigue life is estimated by using the rain-flow algorithm. In the second approach, the fatigue life is estimated in frequency domain. Power spectral density (PSD) of the stress is obtained by using the FE model of the bogie frame and the fatigue life is estimated using Rayleigh technique in random fatigue theory. A comprehensive parametric study is carried out and effects of different parameters like the train speeds and level of the rail surface vertical roughness on the estimated fatigue life are investigated. This paper was recommended for publication in revised form by Associate Editor Hong Hee Yoo Davood Younesian received his MSc and PhD in Mechanical Engineering, both from Sharif University of Technology, Iran. He joined the Iran University of Science and Technology in 2005 as an Assistant Professor in the School of Railway Engineering. Dr Younesian’s research area is mainly focused on non-linear and random vibrations, optimal control of vibrations, dynamics and vibration of structures and railway vehicle systems. He has published more than 75 papers in international journals and conference proceedings in the areas of his research. Ali Solhmirzaei received his BSc in Railway Engineering (Rolling Stock) from Iran University of Science and Technology and his MSc in Mechanical Engineering from K.N.T University of Technology, Iran. His research is mainly focused on finite elements, railway vehicle dynamics and fatigue analysis of railway structures. Alireza Gachloo received his BSc in Railway Engineering (Rolling Stock) from Iran University of Science and Technology, Iran. His research is mainly focused on random vibration, railway vehicle dynamics and random fatigue.     

Electrooxidation of 4-methylcatechol in the presence of barbituric acid derivatives

Electrooxidation of 4-methylcatechol (1) in the presence of 1,3-dimethylbarbituric acid (2a) and 1,3-diethylthiobarbituric acid (2b) as nucleophiles has been studied in detail by cyclic voltammetry and controlled-potential coulometry. The results indicate that 1 can be oxidized to its related o-benzoquinone (1a) and without conversion to its quinone methide tautomeric form, via an ECEC mechanism pathway, is converted to barbiturate derivatives (5a-b). The electrochemical synthesis of 5a-b have been successfully performed in one-pot in an undivided cell.     

An artificial neural network for prediction of gas holdup in bubble columns with oily solutions

Gas holdup in a bubble column reactor filled with oil-based liquids was estimated by an artificial neural network (ANN). The ANN was trained using experimental data from the literature with various sparger pore diameters and a bubbly flow regime. The trained ANN was able to predict that the gas holdup of data did not seen during the training period over the studied range of physical properties, operating conditions, and sparger pore diameter with average normalized square error <0.05. Comparisons of the neural network predictions to correlations obtained from experimental data show that the neural network was properly designed and could powerfully estimate gas holdup in bubble column with oily solutions.     

Thermal analysis of a moving fin using the radial basis function approximation

In this study, the heat transfer and temperature distribution in a moving fin have been analyzed. The fin velocity was considered constant, and the thermal conductivity coefficient was variable with temperature, and the fin was under the effect of convection, radiation, and conduction heat transfer. The main equation of the problem was solved by the radial basis function method and validated by the numerical 4th-order Runge–Kutta method. Several parameters such as thermal conductivity parameter from 0 to 1, sink temperature parameter from 0.2 to 0.8, and N_r, N_c, Pe number from 1 to 4, were examined. The outcomes illustrate that increasing the thermal conductivity by 51.5% raises the conduction heat transfers as well as the dimensionless temperature by 3.42%. Moreover, increasing the sink temperature leads to a slow rise in ambient temperature by 22.8% in the maximum state. By raising the N_c and N_r parameters, near 33.3%, the temperature distribution profile is declined by 4% and 10.5%, respectively. And increasing the Pe number by 100% results in a rise in the temperature distribution by about 7%.     

New approach to bone tissue engineering: simultaneous application of hydroxyapatite and bioactive glass coated on a poly(L-lactic acid) scaffold

A combination of bioceramics and polymeric nanofibers holds promising potential for bone tissue engineering applications. In the present study, hydroxyapatite (HA), bioactive glass (BG), and tricalcium phosphate (TCP) particles were coated on the surface of electrospun poly(L-lactic acid) (PLLA) nanofibers, and the capacity of the PLLA, BG-PLLA, HA-PLLA, HA-BG-PLLA, and TCP-PLLA scaffolds for bone regeneration was investigated in rat critical-size defects using digital mammography, multislice spiral-computed tomography (MSCT) imaging, and histological analysis. Electrospun scaffolds exhibited a nanofibrous structure with a homogeneous distribution of bioceramics along the surface of PLLA nanofibers. A total of 8 weeks after implantation, no sign of complication or inflammation was observed at the site of the calvarial bone defect. On the basis of imaging analysis, a higher level of bone reconstruction was observed in the animals receiving HA-, BG-, and TCP-coated scaffolds compared to an untreated control group. In addition, simultaneous coating of HA and BG induced the highest regeneration among all groups. Histological staining confirmed these findings and also showed an efficient osseointegration in HA-BG-coated nanofibers. On the whole, it was demonstrated that nanofibrous structures could serve as an appropriate support to guide the healing process, and coating their surface with bioceramics enhanced bone reconstruction. These bioceramic-coated scaffolds can be used as new bone-graft substitutes capable of efficiently inducing osteoconduction and osseointegration in orthopedic fractures and defects.     

A genetic algorithm for the simultaneous lot sizing and scheduling problem in capacitated flow shop with complex setups and backlogging

In this paper the capacitated lot sizing and scheduling problem with sequence-dependent setups, setup carryover, and backlogging has been studied. The problem can be formulated as a mixed-integer program. Most lot sizing problems are hard to solve, especially in medium and large scale. In recent years, to deal with the complexity and find optimal or near-optimal results in reasonable computational time, a growing number of researchers have employed metaheuristic approaches to lot sizing problems. One of the most popular metaheuristics is genetic algorithm which has been applied to different optimization problems successfully. Therefore, we have developed a genetic algorithm to solve this model. To test the accuracy of the genetic algorithm, a lower bound is developed and compared against the genetic algorithm. In computational experiments, proposed genetic algorithm performed extremely well. It is concluded that the genetic algorithm is efficient and effective for this problem.     

Theoretical investigation of aromatics production enhancement in thermal coupling of naphtha reforming and hydrodealkylation of toluene

In the current research, an exothermic reaction is proposed to be coupled with naphtha reforming reactions. Hydrodealkylation (HDA) of toluene, which is a well-known petrochemical reaction, is discussed and is suggested as a potential exothermic reaction to be coupled with the endothermic naphtha reforming reactions. The first, the second, and the third reactor of the conventional naphtha reforming process have been substituted in three different cases by thermally coupled reactors and optimized parameters of the final case have been investigated. Considering lower operational costs due to the elimination of inter stage heaters, investigation of thermally coupled reactors has been the first priority of this research. The investigation shows that substitution of the first two reactors and, in the final case, all conventional reactors by the new configuration can improve the production yield of the aromatics by 14% and 21%, respectively compared with conventional naphtha reforming process. The final case has been optimized as well, and 45% and 11% improvement in aromatics and hydrogen production has been observed.     

Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)

Inflow prediction of reservoirs is of considerable importance due to its application in water resources management related to downstream water release planning and flood protection. Therefore, in this research, different new input patterns for predicting inflow to Zayandehroud dam reservoir is proposed employing artificial neural network (ANN) and support vector machine (SVM) models. Nine different models with different patterns of input data such as inflow to the dam reservoir considering time duration lags, time index, and monthly rainfall of Ghaleh-Shahrokh station have been proposed to predict the inflow to the dam reservoir. Comparison of the results indicates that the ninth proposed model has the least error for inflow prediction in which the results of SVM model outperform those of ANN model. That is, the least error has been obtained using the ninth SVM (ANN) model with correlation coefficient (R) values of 0.8962 (0.89296), 0.9303 (0.92983) and 0.9622 (0.95333) and root mean squared error (RMSE) values of 47.9346 (48.5441), 42.69093 (43.748) and 23.56193 (28.5125) for training, validation and test data, respectively.