Khorassan province is in a dry part of Iran. Frequent droughts, especially during recent years, have shown that drought is a very significant phenomenon which must be considered in any development program. For this purpose, rainfall data of 11 synoptic meteorological stations in Khorasan were studied to see whether drought is possible to be predicted. First order Markoff chain model was used in this study. For each station, the probability of being in a wet, normal, or dry condition in the year 2004 was derived. Based on this study, Khorasan province has been zonned for states of normal, wet and dry situation. Related and appropriate map and tables have also been presented.

Quantitative information about response of phenological development to temperature and photoperiod in faba bean is limited. This study was undertaken to examine phenological development of four faba bean cultivars (conducted Barakat, Saraziri, Eraghi, Gavi) under 11 sowing dates. The experiment was conducted in Gorgan during 2005-2006. Various response functions were evaluated for quantifying response flowering rate to temperature and photoperiod. Results indicated that response of faba bean flowering rate is described by beta- segmented (Barakat, Saraziri, Eraghi) and beta- quadratic (Gavi). Using these functions, cardinal temperatures (base, optimum, and ceiling) for flowering and critical photoperiod (the photoperiod below which flowering rate begins to decrease) and photoperiod sensitivity coefficient (slope of the function in response to photoperiod) for flowering rate were determined. Barakat, Saraziri and Eraghi Cultivars showed a qualitative long-day plant (LDP) response to photoperiod and progress towards flowering stopped when photoperiod decreased about 10 h per day. But Gavi cultivar showed a quantitative long-day plant (LDP) response to photoperiod. Critical photoperiod estimated by beta-segmented function for Barakat, Saraziri, Eraghi Cultivars were between 14.5 to 16.1 h and by beta- quadratic function for Gavi Cultivar was 15.3.

Background and Objectives: Although Boilover occurs with a low frequency, but in case of occurrence, it can cause severe damage to people and equipment around the tank. The prediction of the fireball of Boilover phenomenon has an important role to play in adopting appropriate strategies for fire suppression of the atmospheric storage tank. The purpose of this study is to predict the consequence of fireball caused by Boilover phenomenon using empirical models. Materials and Methods: This is a descriptive-analytic study. At first, empirical models presented for prediction of the Boilover consequence were identified. Then, the models used to predict the fireball geometry and its radiation heat flux around the tank. The results of model predictions were compared with the data of a small-scale experimental study and a large-scale real Boilover accident. Results: According to the results, the error of the INERIS and Buang model for predictions of radiation heat flux around the experimental Boilover were 23 and 31%, respectively. The error of these two models predictions of radiation heat flux of accident were 52% and 71%, respectively. Conclusion: The INERIS and Buang models showed a lower error for prediction of the Boilover fireball consequences compared to other models. The results of this study can be used to assess the risk of Boilover in atmospheric storage tanks and help accident commander’ s decision on strategies and tactics for the fire suppression of atmospheric storage tanks.

Many fruits, vegetables and ornamental crops of tropical origin experience physiological damage when subjected to low temperatures. Protection of plants from the effects of lethally low temperatures is important in agriculture, especially in horticultural production of high value fruits and vegetables. The objective of this study was to develop a simple model to predict the daily minimum air temperature for prediction of frost occurrence in Bajgah and Kooshkak semi-arid areas, Fars province. Initially, the relationship between the minimum temperature of the early morning of a day with some meteorological parameters of the previous day was developed. Meteorological parameters used in this analysis are daily relative humidity, wind speed, pan evaporation, rainfall, sunshine hours, and estimated dew-point temperature. Dewpoint is an important parameter which is related to the minimum temperature in different months with low temperature in Bajgah and Kooshkak areas. Many daily weather parameters used in the regression analysis showed no significant relationship with the daily early morning minimum temperature, except the dew-point and relative humidity. The regression equation between the differences between dew-point and minimum temperature with relative humidity as a simple model was proposed to be used to predict the minimum temperature and subsequently frost occurrence in the study regions. This model was validated by independent data set with an acceptable accuracy for the study regions.

Uncertain and stochastic conditions of accidents could affect the risk and complexity of decisions for managers. Accident prediction methods could be helpful to confront these challenges. Fuzzy inference systems (FIS) have developed a new attitude in this field in recent years. As lift truck accidents are one of the main challenges that industries face worldwide, this paper focuses on predicting the possibility of these types of accidents. At first, the data collection is done by using interviews, questionnaires, and surveys. An FIS approach is proposed to predict the possibility of lift truck accidents in industrial plants. Furthermore, our approach is validated using data from many real cases. The results are approved by the multivariate logistic regression method. Finally, the output of the fuzzy and logit models is compared with each other. The re-validation of the fuzzy control model and high consistent of the output of these two models is presented.