In this paper, we investigate a single-server Poisson input queueing model, wherein ARRIVALS of units are in bulk. The arrival rate of the units is state dependent, and service time is arbitrary distributed. It is also assumed that the system is subject to breakdown, and the failed server immediately joins the repair facility which takes constant duration to repair the server. By using supplementary variable technique, we obtain the probability generating function of the number of units in the system which is further used to establish some performance indices such as the mean number of units in the system, mean waiting time, etc. Special cases are also discussed. In order to obtain approximate values of system state probabilities, the principle of maximum entropy is employed. Numerical results are also presented to validate the analytical formulae.

Inthe present age of globalization, technology-revolution and sustainable development, the presence of seasonality in tourist ARRIVALS is considered as a key policy issue that affects the global tourism industry by creating instability in the demand and revenues. The seasonal component in a time-series distorts the prediction attempts for policy-making. In this context, it is quintessential to suggest an accurate method of producing the reliable forecast of foreign tourist ARRIVALS. This paper evaluated the performance of Holt-Winters’ and Seasonal ARIMA models for forecasting foreign tourist ARRIVALS in India. The data on India’s inbound tourism from Jan-2001 to June-2018 were used for preparing the forecast for the period July-2018 to June-2020. On the basis of Mean Absolute Error, Mean Absolute Percentage Error and Mean Square Error, the findings infer the relative efficiency of Holt -Winters’model over Seasonal ARIMA model in forecasting the foreign tourist ARRIVALS in India.Thus, to reduce the perceived negative impacts of seasonality in Indian inbound tourism and to ensure foreign tourist visits round the year, niche products best suitable for Indian climatic and socio-cultural-institutional conditions need to be introduced and promoted in a large scale both at the national and global levels.

BATCH sizing in different planning period is categorized as a classical problem in production planning, that so many exact & heuristic methods have been proposed to solve this problem, each of which considering various aspects of the original problem. The solution obtained from majority – e.g. MRP – is in this format that there may be some periods of idleness or each period should produce as needed in different adjacent periods. If there are more the one final independent product to be produced in a factory, this makes the production planning experience strong variations in BATCH sizes for different periods, which production managers are opposed to these proposed production plans. In this paper, some of the models are proposed to solve this shortcoming of the production plan to smooth the variation of BATCH sizes and consequently to meet the managers ideal. Finally all of the proposed models are used in a real case problem and the best model is introduced in that case.

Nine commercial almond cultivars ('Sahand', 'Yalda', 'Shukofeh', 'Azar', 'Ferragnes', 'Nonpareil', 'Monagga', 'A200' and 'A230') were used for determination of the flower initiation time, stages of morphological development of flower bud, flower buds density in spur, branch spur number of and pistil length. The experiment was conducted in 'Sahand' Horticultural Station during three years. For determination of flower initiation, buds were sampled weekly since 15 August. Results showed a positive correlation between initiation and flowering times. The first flower initiation period was observed in 'Monagha' (27 August) and the last flower organogenesis period was observed in 'Sahand' (26 November). Analysis of variance showed significant differences among cultivars in all studied characteristice (P≤5%). 'Sahand' produced the highest spur in each meter of branches with average of 46 spurs and 'Monagha' produced the highest pistil length with average of 1.85 cm. The important point in all the cultivars used was normal fruit setting despite short pistil production in some of them.

A structured unsegregated cybernetic model is able to describe diauxic growth phenomena of cells colony in aerobic condition. This model shows that the metabolic activity of the cells is regulated by internal cellular controls which direct the micro-organism towards the most convenient metabolic pathway able to optimize the use of available resources. In this paper, for proving this reality, a structured unsegregated cybernetic model have been used for describing of the growth stages of Saccharomyces cerevisiae on mixed substrates. The results of simulation of baker’s yeast in BATCH and fed-BATCH bioreactor show that agreements are quite satisfactory between experimental data and simulation results. And it well performs in the simulation of the lag-phases and the diauxic growth. Finally, for preventing of oxygen starvation that causes ethanol production, oxygen mass transfer coefficient is simulated on the function of impeller speed and air flow rate and the effect of this parameter on biomass yield have been discussed.

This paper presents a comparison between BATCH and three different sets of fed BATCH fermentations for rhamnolipid production by Pseudomonas aeruginosa.The BATCH run was performed with 500 ml of culture medium having the initial glycerol and sodium nitrate concentrations of 30 and 8.3 g/l, respectively. For a fed BATCH run with nitrogen source in feed, 250 ml of the nitrogen excluded culture medium was in the bioreactor initially, and 250 ml culture medium containing 16.6 g/l sodium nitrate was fed to the bioreactor continuously.A similar procedure was repeated for fed BATCH runs with carbon, and phosphorus source in feed. Statistical analysis showed that fed BATCH runs were better than BATCH in term of rhamnolipid production, and among the fed BATCH runs the maximum amount of rhamnolipid.(4.12 g Rhamnose Equivalent/l) was for the fed BATCH run with the carbon source in feed.