The purpose of the current study was to investigate the determinants of small and medium manu-facturing enterprises’ export performance empirically in the context of Pakistan. The study adopted survey method to collect forty-five (45) responses from small and medium manufacturing firms through E-mail survey. The study produced four determinants of SME export performance, such as entrepreneurial orientation (EO), total quality management (TQM), business network (BN) and export market orientation (EMO). Thus, internal consistency reliability and discriminant validity were examined of the study instruments through panel expert’s opinion and the small sample size was investigated using statistical software SPSS-21 and smartPLS-3. The results of the study con-firmed that scale reliability and validity and moreover, current study found a positive relationship between TQM and SME export performance. On the other hand, the study also found insignificant relationship between EO, BN, EMO and SME export performance. Furthermore, the study recommended future study should validate the current study research framework on large sample size.

Earned Value Management (EVM) has been a well-known methodology used since the 1960s when the US department of defense proposed a standard method to measure project performance. This system relies on a set of often straightforward metrics to measure and evaluate the general health of a project. These metrics serve as early warning signals to timely detect project problems, or to exploit project opportunities. A key aspect of EVM is to estimate the completion cost of a project by considering both cost and schedule performance indices. However, good performance of cost and schedule performance indices does not necessarily guarantee cost effectiveness of the project regardless of the overhead costs. The reason is because, in most project-based organizations, overhead costs constitute a significant proportion of the total costs. However, EVM indices are usually calculated in the absence of the so-called overhead costs. This paper, first, seeks to remedy this problem by proposing a practical procedure of allocating overhead costs in project-based organizations. Then the traditional EVM indices are revised by consider-ing the allocated overhead costs. Finally, a case study demonstrates the applicability of the proposed method for a real-life project.

Understanding mechanical properties of healthy and unhealthy cerebral vessels is a key element in the development of their science and the relevant clinical diagnosis, prevention and treatment. Thirteen healthy samples were obtained from 23 middle cerebral arteries. The changes of force and deformation until the vessel rupture were recorded using a biaxial device. Thereafter, the stress-strain curve was plotted and fitted with a hyperelastic five-parameter Mooney-Rivlin model and the model parameters (C1, C2, C3, C4, and C5) were determined according to the best fit. For statistical comparison, the samples were divided into three age and two gender groups and subjected to non-parametric statistical analyses. Comparison of obtained results for different age groups showed that there is a significant difference between the "old" group and the other two groups (middle-aged and young). There was no significant difference between male and female groups. Therefore, the results demonstrate the changes of blood vessel wall properties with aging. The results also depicted that the arterial wall is stiffer in the circumferential direction than the axial direction. Anisotropy of cerebral vessels was confirmed by all of the tests. Therefore, the significance of the biaxial tests is in the spot light in the derived data. Moreover, good fitting of data illuminated that the use of multiple-parameter constitutive models is useful for mathematical demonstration of cerebral vessel tissue behavior. In conclusion, good fitting of data illuminated that the use of multiple-parameter constitutive models is useful for mathematical demonstration of cerebral vessel tissue behavior.

In financial industry, the accurate forecasting of the stock market is a major challenge to optimize and update portfolios and also to evaluate several financial derivatives. Artificial neural networks and technical analysis are becoming widely used by industry experts to predict stock market moves. In this paper, different technical analysis measures and resilient back-propagation neural networks are used to predict the price level of five major developed international stock markets, namely the US S & P500, Japanese Nikkei, UK FTSE100, German DAX, and the French CAC40. Four categories of technical analysis measures are compared. They are indicators, oscillators, stochastics, and indexes. The out-of-sample simulation results show a strong evidence of the effectiveness of the indicators category over the oscillators, stochastics, and indexes. In addition, it is found that combining all these measures lead to an increase of the prediction error. In sum, technical analysis indicators provide valuable information to predict the S & P500, Nikkei, FTSE100, DAX, and the CAC40 price level.

Cash is the life blood of the organizations and cash management is the important aspect of any organization. Corporate cash management boosts the companies from small to giant in the competitive environment. In this study we highlight three factors of a good cash management practices. Such factors are Cash Conversion Cycle (CCC), cash holding and credit score. Influence of one factor to other helps organizations manage their corporate cash in better way. In this study, we collect data from Capitaline corporate database of Mumbai, India over the period 2002-2011. Using some regression techniques, we observe that due to higher credit score companies forced to minimize their CCC and it helped to maintain lower level of working capital.

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.