Bidding price decision is a key issue for the contractors and construction companies. The success/failure of the contractors in competitive biddings is directly dependent on their bidding strategy. This paper aims to develop a hybrid statistical and mathematical modeling approach for determining the optimum bidding price in construction projects. By statistical analysis of historical data, some uncertain parameters like the number of competitors and the cost of the project are estimated. Then, a scenario-based mathematical model for bidding price decision is proposed. In order to present a model in more accordance with the real-world situations, factors like risk, minimum acceptable rate of return (MARR) and opportunistic behavior are taken into account. In order to achieve an insensitive solution to the change in the realization of the input data from the scenarios, a robust mathematical model is used. The performance of the model is evaluated through some numerical problems. Furthermore, sensitivity analysis of the key parameters and robustness evaluation of the model against uncertain parameters are conducted. To evaluate the model's effectiveness in real-world situations, a case study is analyzed by the proposed approach. Numerical results show that the proposed approach reduces the cost estimation errors and increases the average expected profit, which validates the applicability of the model in a real-world situation.

Financial contagion is the act of spreading of a phenomenon within or from the stock markets sector. Stock markets are highly interlinked and happenings on one side of the world are bound to affect the happenings on another side of the world. The contagion can be because of domestic or international factors. The performance of Nairobi Securities exchange (NSE) was measured by the use of the monthly stock prices as provided by the data vendors at the NSE. This research paper used a quantitative research design where econometric models were used in the analysis. The entire population of the listed firms in the NSE was used. Primary data was collected from the licensed market participants at the NSE. Secondary data during the pre-crisis (April 2006 to July 2007) and post crisis periods (August 2007 to December 2008) were collected using data collection sheets. The data entailed NSE 20 Share Index, FTSE 100 and Standard and Poors and was analyzed using excel and SPSS tools. Hypothesis was tested at 0.05 level of significance and the null hypothesis was rejected at both the primary and the secondary data obtained.

Friction Stir Welding (FSW) is a relatively new solid state joining technique which is used not only for joining the aluminum and its alloys but also has potential for joining dissimilar materials with very different physical and mechanical properties which are hard to weld using conventional fusion welding processes. Tensile shear testing is used to determine the Mechanical strength of friction stir lap (FSL) welds under static loading, fracture strength (σLap) corresponding to the maximum load in a test over the sample width is widely used as the strength value. During friction stir lap welding (FSLW) of dissimilar metals with large differences in melting temperatures, a metallurgical bond is established through the formation of interfacial intermetallic compounds. However, as these intermetallic compounds are generally believed to be brittle with little ductility, they are generally considered to have detrimental effect on fracture strength. The aim of the present research is to study how the interface structure is affected by FSW parameters and how the formation of interface structure affects fracture of Al-Steel and Al-Ti FSL welds.

In today’s business scenario, organizations are focusing on sustainable growth for performance measurement in supply chain. For this purpose, sustainable flexibility is an important issue to fulfill the environmental, economic and customer’s needs, which is helpful for sustainable growth for an industry. In this paper, different factors related to sustainable flexibility in supply chain management are identified through literature review and experts’ opinions in this domain. Further, an attempt has been made to develop the interactions among these factors using Total Interpretive Structural Modeling (T-ISM) technique. This paper also employs Cross Impact Matrix Multiplication Applied to Classifications (MICMAC) based analysis to create policy to implement for sustainable growth in industries. Findings of this research give valuable decision-making insight and implications about the relative importance and the interdependence of flexibility factors in sustainable supply chain management for academicians and practicing managers in sustainable growth of an industry.

The quantum-chemical modeling mechanism of the [4+2]-cycloaddition reaction of 2,3 dimethylbuta-1,3-diene and methyl acrylate was conducted. Its qualitative aspects were analyzed at the molecular level by the program MOPAC2012 and semiempirical method RM1. The potential energy surfaces of 2,3 dimethylbuta-1,3-diene and methyl acrylate [4+2] cycloaddition possible reaction pathways were constructed by restricted and unrestricted Hartree-Fock approximation. It has been established that the molecule of the final product methyl-3,4-dimethylcyclohex-3-encarboxylate has the half-chair shape, wherein the carboalkoxyl group is in the exo-orientation. Interaction between molecules of 2,3 dimethylbuta-1,3-diene and methyl acrylate occurs by a two-step mechanism more likely than one-step, since the activation parameters of this interaction maximum coincide with the experimental data.

Modeling the simple assembly line balancing (SALB) problem has covered a wide range of real-world applications. The recent advances in optimization problems have created the opportunities to tackle more challenging problems. This paper presents a multi-objective decision making problem to consider two objectives, cost and cycle time, for simple assembly line balancing. The problem is formulated as a mixed integer nonlinear optimization and the proposed study of this paper uses two metaheuristics to solve the resulted problem on some benchmark problems. The preliminary results have indicated that multi objective particle swarm optimization (MOPSO) has provided better quality solutions while the hybrid method based on MOPSO and simulated annealing has yielded more non-dominated Pareto solutions.

This research paper is aimed to present a fuzzy Hybrid Multi-criteria decision making (MCDM) methodology for selecting employees. The present study aspires to present the hybrid approach of Fuzzy multiple MCDM techniques with tactical viewpoint to support the recruitment process of wind turbine service technicians. The methodology is based on the application of Fuzzy ARAS (Additive Ratio Assessment) and Fuzzy MOORA (Multi-Objective Optimization on basis of Ratio Analysis) which are integrated through group decision making (GDM) method in the model for selection of wind turbine service technicians’ ranking. Here a group of experts from different fields of expertise are engaged to finalize the decision. Series of tests are conducted regarding physical fitness, technical written test, practical test along with general interview and medical examination to facilitate the final selection using the above techniques. In contrast to single decision making approaches, the proposed group decision making model efficiently supports the wind turbine service technicians ranking process. The effectiveness of the proposed approach manifest from the case study of service technicians required for the maintenance department of wind power plant using Fuzzy ARAS and Fuzzy MOORA. This set of potential technicians is evaluated based on five main criteria.

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.