Web browser is one of the most important internet facilities for surfing the internet. A good web browser must incorporate literally tens of features such as integrated search engine, automatic updates, etc. Each year, ten web browsers are formally introduced as top best reviewers by some organizations. In this paper, we propose the implementation of TOPSIS technique to rank ten web browsers. The proposed model of this paper uses five criteria including speed, features, security, technical support and supported configurations. In terms of speed, Safari is the best web reviewer followed by Google Chrome and Internet Explorer while Opera is the best web reviewer when we look into 20 different features. We have also ranked these web browsers using all five categories together and the results indicate that Opera, Internet explorer, Firefox and Google Chrome are the best web browsers to be chosen.

As the need for software increased, the number of software firms and the competition among them also increased. The software companies in developing countries like India can no longer survive based on cost advantage alone. The firms need to deliver competitively priced quality software products on time. This can be achieved through quantitatively managing the different phases or sub processes in software development process. But quantitative management of a process consisting of a set of interlinked sub processes or stages with the output of one sub pro-cess influencing that of subsequent stages and final output is not easy. The process performance models developed for quantitative management of software development process often model the final outcome in terms of factors from various stages together or focuses only on quantitatively managing a particular sub process independently. In manufacturing and other engineering indus-tries, the processes with multiple sub process are monitored and controlled using multistage pro-cess control methodology. This paper is an application of multistage statistical process control for managing the software development process. The suggested methodology is a combination of process performance models and control charts. The proposed methodology can be easily im-plemented for controlling various types of software projects like development projects, incre-mental development projects, testing projects etc. The methodology also provides the project manager the opportunity to tighten or relax the control at various sub processes based on the pro-ject team’s strengths and still achieve the goal on the final outcome.

Extensive brittle fracture curves are presented in the present paper for engineering components weakened by a U-shaped notch under different in-plane loading conditions from pure mode I to pure mode II. The curves were obtained in a computational manner on the basis of an appropriate brittle fracture model, namely the U-notched maximum tangential stress (UMTS) criterion, suggested and employed several times in the past by the author and his co-researchers to assess mixed mode fracture in numerous U-notched samples. Eight different notch tip radii were considered in the computations. Extensive brittle materials were also taken into consideration by using different values of the material critical distance in the calculations. By estimating theoretically the load-carrying capacity and the fracture initiation angle using solely the two basic material properties, namely the ultimate tensile strength and the plane-strain fracture toughness, engineers can design conveniently the U-notched brittle components and structures aiming to avoid abrupt fracture.

This paper investigates the instantaneous economic order quantity model by allocating the percentage of units lost due to deterioration in an on-hand inventory by framing promotional effort cost and variable ordering cost. The objective is to maximize the net profit so as to determine the order quantity, promotional effort factor, the cycle length and number of units lost due to deterioration. For any given number of replenishment cycles the existence of a unique optimal replenishment schedule are proved and mathematical model is developed to find some important characteristics for the concavity of the net profit function. Numerical examples are provided to illustrate the results of proposed model, which benefit the retailer and this policy is important, especially for wasting of deteriorating items. Finally, sensitivity analyses of the optimal solution with respect to the major parameters are carried out.

Municipal solid waste management has become a global concern during the past decades in many countries such as Canada and waste management technological advancements and regula-tions have been increased. Solid wastes emit greenhouse gases which result in global climate change, pollution of air and water which has tremendous negative impact on human health. Due to the excessive urbanization and fast economic development, municipal solid wastes have been increased in developing countries. In order to manage this emerging issue, polluted countries need a series of legislations and policies toward solid wastes. Accurate prediction of future mu-nicipal solid waste generation plays a critical role for future planning. This paper focuses on mu-nicipal solid waste generation in city of Tehran, the most populated city in Middle East. Three methods are explored in this paper to analyze the past solid waste time-series analysis: regres-sion, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The first method, which is the classical regression approach, is used as a baseline for considered neural networks models. The second method utilizes the past data as training example of neural network to find autocorrelation among target; lastly, the neuro-fuzzy learns the relation of data using fuzzy-rule. Mean Absolute Percentage Error (MAPE) metric is used to evaluate the per-formance. Finally, analysis of variance (ANOVA) and Duncan experiment are performed to ver-ify and validate the outcome of the experiments.

A series of novel dihydropyrimidin-2(1H)-ones derivatives is synthesized by using Rhizopus Oryzae lipase biocatalyst in deep eutectic solvent. The reaction is characterized by high efficiency and selectivity, short reaction time, mild and environmentally friendly reaction conditions. The yields are found to be significantly higher and reuse of both the lipase and deep eutectic solvent is possible up to four consecutive cycles. The products are found to exhibit appreciable in vitro antibacterial activity against Echerichia coli, Pseudomonas neumoniae and in vitro antifungal activity against Aspergillus niger and Candida albicans.

The classic ARIMA models use the information criteria for lag selection since 1990s. The information criteria are based on the summation of two expressions: a function of Residual Sum of Squares (RSS) and a penalty for decrease of degrees of freedom. However, the information criteria have some disadvantages since these two expressions do not have the same scale, so the information criteria are mainly based on the first expression (because of its bigger scale). In this paper, we propose a hybrid ARIMA model, which uses the Data Envelopment Analysis (DEA) model to select the best lags of AR and MA process called DEA-ARIMA. DEA is a linear programming technique, which computes a comparative ratio of multiple outputs to multiple inputs for each Decision Making Unit (DMU), which is reported as the relative efficiency score. We identify inputs as the number of AR and MA terms and outputs of the model are inverse of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In fact, in our proposed model, inputs consider as resources, so we are looking for some models with fewer resources and high efficiency. The DEA unlike the information criteria may have more than one solution and all of them are efficient so to compare this two models selection the mean of best DMUs is calculated. Experimental results demonstrate DEA-ARIMA will not trap in over fitting problem in contrast to classic ARIMA models because of considering a set of efficient ARIMA models.

In the era of technology, the demand of the software development increases at a very high speed, as software has touched the human’s life in all aspects. The better quality software development acquiring minimum development time leads to the team work in which a group of people has been formed that work together in a team for the software development. One of the most significant issues in effective and efficient teamwork is the team leader selection because the team lead-er is the person in any team that is going to handle all types of managerial activities such as leadership, motivation to others, etc. The team leader selection process may be dependent on numerous conflicting selection indexes that make it a Multi-Criteria Decision Making (MCDM) problem. In the present research, an MCDM approach namely, Euclidean Distance Based Approximation (EDBA) which is based on the calculation of the composite distance value for each alter-native from a hypothetical optimal point is presented. The result of this study provides a comprehensive ranking of team leaders that leads to the right selection of team leader in information technology (IT) sector.

In this paper, we develop an inventory model for deteriorating items with exponential time dependent demand rate. In this proposed model, shortages are allowed and partially backlogged. The model is explained with the help of numerical examples. We also show that total cost function is convex with time. Sensitivity analysis of the optimal solution with respect to various parameters of the system is given and results are discussed in details. Taylor’s series expansion is used for finding closed form optimal solution. Some results have been also obtained.

Nowadays, the importance of achieving sustainable organization with competitive advantage in complex environments has many researchers’ interest. So, in this study, we evaluate different views of MKM and its role to reach SCA within banking industry. This research is relatively the scarce empirical study and adds to its originality. The findings offer valuable insights on the generalizability of MKM in a research setting. In fact, the purpose of this study is to empirically test the effect of the marketing knowledge management (MKM) on sustainable competitive advantage (SCA) within banking industry of Iran. A valid research instrument was utilized to conduct a survey of 150 top- and middle-level managers from Mellat bank of Iran (MBI). With a response rate of 81.3 percent, 122 questionnaires were returned; the number of valid and usable questionnaires was 101. Using exploratory and confirmatory factor analyses, MKM were classified into organizational strategies, culture and performance of senior manager, information technology (IT), research and development (R & D), internal customer (personnel), and external customer (client). Moreover, MBI’ SCA was classified into three dimensions: market, customer, finance. Structural equation modelling was utilized to test the stated hypotheses and model. Statistical support was found for the hypothesized relationships. Moreover it has been shown that MKM maintained the greatest effect on the market centered SCA, while it had the least influence on the customer centered.