The generalized exponential distribution could be a good option to analyse lifetime data, as an alternative for the use of standard existing lifetime distributions as exponential, Weibull or gamma distributions. Assuming different non-informative prior distributions for the parameters of the model, we introduce a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Some numerical illustrations considering simulated and real lifetime data are presented to illustrate the proposed methodology, especially the effects of different priors on the posterior summaries of interest.

In this paper, modified hydrothermal method for Ti-MWW catalyst preparation has been shown. Instrumental analysis of the zeolite material Ti-MWW has been performed by means of UV-vis spectrometry, infrared spectrometry (IR), scanning electron microscope (SEM), X-ray diffraction (XRD), and X-ray microanalysis. Moreover, the results of the epoxidation of diallyl ether (DAE) over the titanium silicate catalyst Ti-MWW and in the presence of methanol have been presented. t-Butyl hydroperoxide have been applied for the first time as an oxidant for this process. The influence of temperature (20-130°C), DAE/TBHP molar ratio (1:1-3:1), methanol concentration (10-80 wt%), amount of catalyst (1-7 wt%) and reaction time (60-1440 min.) was studied. The main functions describing the process were determined on the basis of the results obtained from the gas chromatography method.

The purpose of this study is to analyze the influence of social media marketing, product innovation, market orientation on marketing performance. The study uses quantitative methods and data analysis techniques are based on Structural Equation Modeling using SmartPLS 3.0 software. The sample selection method uses the snowball sampling method. Online questionnaires are sent to 320 SMEs in Banten Province, where 300 responses are used. The results of data analysis show that social media marketing has a significant effect on marketing performance, product innovation has a significant effect on marketing performance and market orientation has a significant effect on marketing performance.

The interphase is a region between fibers and a matrix, which has different properties from the matrix and the fibers, but is dependent on them. Considering the interphase region has a significant effect on the accuracy of obtained residual stresses. So far, in order to obtain the micromechanical residual stresses, the interphase properties are considered as an average. In this paper, the properties of the interphase are assumed variable by using a suitable UMAT code in the ABAQUS software. The equations of previous studies that have acquired interphase properties to be variable are used to write the UMAT code. A representative volume element (RVE) in polymer composites is modeled in three phases in the ABAQUS software and the interphase properties are considered as FGM by using the UMAT code. Temperature variation during curing to environment temperature is the only loading factor in the RVE. The matrix, fiber and interphase stresses are obtained in the ABAQUS software. The achieved stresses were compared with the results of previous studies that considered interphase properties as average. Finite element and energy methods were used in previous papers but in the present study just the finite element method with variable interphase properties was use. In addition, residual stress diagrams with the variable interphase properties are plotted to study the effect of the thermal expansion coefficient. The results of this study are similar to those in previous ones, and the curves are improved.

In today’s era of higher competition in the business, there are many conditions such as offered concession in bulk purchasing, seasonality, higher ordering cost, etc., which force a retailer to purchase more quantities than needed or exceed the storage capacity. So in this situation the retailer has to purchase an extra warehouse named as rented warehouse to stock the extra quantity. In this paper an inventory model for deteriorating products with selling price dependent rate is developed. The occurring shortages are assumed to be partially backlogged and cycle time is also variable. The purpose of the development of this model is to compute the amount and time of order which can optimize the total average cost of the system. A solution procedure and numerical example are presented to illustrate the implementation of the proposed study. Sensitivity analysis concerning with distinct system parameters is also presented to demonstrate the model.

The information of trip demand as the freight origin-destination (O-D) matrices plays a significant role in intercity transportation engineering and planning. Access to this matrix from conventional methods needs to spend time, money and human resources. During the recent years, numerous studies have been conducted in order to obtain the freight O-D matrix using freight vehicle count information in network links. Furthermore, the estimation of O-D matrix and its communicating with freight vehicles can greatly help planners improve logistic, safety and the maintenance issues. This paper presents a method based on the minimization of the deviations between the observed and estimated values for finding the freight O-D matrix. To solve the model, traffic count data of trucks in the network links as well as other related information resources are used. The model is solved for a case study in Iran using meta-heuristic Simulated Annealing Algorithm.

DFT calculations indicate that the decomposition reaction of nitroethyl benzoates in the presence of 1,3-dimethylimidazolium cation takes place much faster than in the case of the non-catalyzed process. Additionally, our calculations suggest one-step polar mechanism of title reactions.

Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ?-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE) is applied.

This study proposes to explore the relationship between perceived risk, subjective norm, perceived value, and continuous usage intention via trust as a mediating variable. This research applied a survey which involved 320 IM users (160 from millennials and 160 from Zillennial) in Indonesia. The literature’s existing scales were used to operationalize the constructs proposed in this study. The analyses were conducted using partial least squares structural equation modeling (PLS-SEM) to test hypotheses. The results of the study show that there are differences in the results of the influence of perceived risk on continuous usage intention in the Millennial and Zillennial generations. The results of the study found that continuous usage intention received negative direct impact from perceived risk, positive direct effect from subjective norm, perceived value, and trust on Millennial. In addition, continuous usage intention received negative and not significant direct impact from perceived risk, positive direct effect from subjective norm, perceived value, and trust on Zillennial. The results are useful for instant messaging management into formulating strategies to retain their users in Indonesia. These findings provided theoretical and managerial contributions as well as future research directions.

This study aims to analysis the factors influencing on demand for Vietnam Electricity. In this paper, the author builds the Vietnam Electricity Quantity Demand function based on the Alfred Marshall model. The data used to build this function contains 276 observations (from Jan 1995 to December 2017). The author also analyzes the elasticity and the impacts of different factors to influence the Vietnam Electricity Quantity demanded. The study uses multiple linear regression model and the results indicate that all observations were meaningful when the level of significance was five percent. The results also indicate that demand for electricity will increase over the next few years and policy makers have to take some action for the development of the electricity industry.