PROBABILITY density functions of the involved random variables are essential for the reliability-based design of offshore structures. The objective of present research was the derivation of PROBABILITY density function (PDF) for the local joint flexibility (LJF) factor, fLJF, in two-planar tubular DK-joints commonly found in jacket-type offshore structures. A total of 162 finite element (FE) analyses were carried out on 81 FE models of DK-joints subjected to two types of axial loading. Generated FE models were validated using available experimental data, FE results, and design formulas. Based on the results of parametric FE study, a sample database was prepared for the fLJF values and density histograms were generated for respective samples based on the Freedman-Diaconis rule. Nine theoretical PDFs were fitted to the developed histograms and the maximum likelihood (ML) method was applied to evaluate the parameters of fitted PDFs. In each case, the Kolmogorov-Smirnov and chi-squared tests were used to evaluate the goodness of fit. Finally, the Inverse Gaussian model was proposed as the governing PROBABILITY distribution function for the fLJF. After substituting the values of estimated parameters, two fully defined PDFs were presented for the fLJF in tubular DK-joints subjected to two types of axial loading.

Introduction: Models of observed daily weather sequences are frequently used in water engineering design, and agricultural, ecosystem or climate change simulations because observed ground-based meteorological data are often inadequate in terms of their length, completeness or spatial coverage. These statistical models are also known as ‘weather generators’ since they can fill missing data or produce indefinitely long synthetic weather series by simulating key properties of observed meteorological records (i.e., daily means, variances and co-variances, frequencies, extremes, etc.). To date, the majority of weather generators have focused on the precipitation process in recognition of the dominant control exerted by rainfall on many environmental processes, and due to the complexity of building internally consistent, multivariable models (Hutchinson, 1995). However, companion algorithms that simulate other meteorological variables are also in routine use.Rather than simulating rainfall OCCURRENCEs day by day, spell-length models operate by fitting PROBABILITY distributions to observed relative frequencies of wet and dry-spell lengths. This kind of model is sometimes called an ‘alternating renewal process’(Buishand, 1977; 1978; Roldan and Woolhiser, 1982), in that random numbers are generated alternately from the wet and dry spelllength distributions. That is, a new spell length (L) is generated only when a run of consecutive wet or dry days has come to an end, at which point a new spell of the opposite type is simulated.

Natural hazards in different areas each year will be causing great damage. Hail as one of the major climatic hazards, a lot of damage in different parts of the environment, especially agricultural and horticultural products are created. Understanding of tempo- spatial distribution this phenomenon, much to the prevention of damage caused by this phenomenon, it can help.In this study, data on days with hail, in the period 1987 to 2007 and number five synoptic stations in Kermanshah province have been used. Method used in this study, is the Piossio distribution PROBABILITY. The results showed that the PROBABILITY of this phenomenon in the growing season of horticultural crops is very high so that the highest percentage and PROBABILITY of OCCURRENCE of this phenomenon, to be in April and then May. In these months, still in early stages of plants and trees in bloom and are very vulnerable. A surveillance zone also showed that the most vulnerable areas of the hail event are the western province (Sarpolzahab station) and in the eastern province of Kermanshah in Kangavar station.

Lightning is one of the most severe weather hazards that will cause significant economic, social and environmental damage each year. The prediction of a lightning is a very difficult task due to the spatial and temporal expansion of weather either physically or dynamically. Therefore, timely forecasting of lightning and evaluation of the best data mining model is effective in reducing damage. In this research, the data of the years 2012_2018 of the Meteorological Station of Rasht were used, including dependent variable of OCCURRENCE and non-OCCURRENCE of lightning during 7 years and independent variables of factors affecting lightning including temperature, relative humidity, cloudy, wind speed, wind direction, pressure air and Previous day's lightning. After preprocessing and processing data, data mining models including Classification & Regression Tree (CART), Chi-squared Automatic Interaction Detector (CHAID), Induction of Decision Trees (C5) and neural networks Radial Basis Function (RBF), Multi Layer Perceptron (MLP) and Support Vector Machine (SVM) were used in SPSS Modeler Ver 20 software. The results of the models were compared with the Comparative Criteria and the Receiver operating characteristic (ROC) curve. According to the results of the models, the PROBABILITY of lightning OCCURRENCE is higher in the months of May, June and July than in other months and the rate of OCCURRENCE from spring to winter has a decreasing trend, while in winter it is at least. CHAID tree with a specificity rate of 0. 794 and a minimum false positive rate of 0. 205 and the SVM model with a correct prediction of 0. 773 and an error rate of 0. 475 and precision of 0. 855 have optimum performance compared with other models.

Marriage as a way of forming a family has always been the focus of cultures and societies. In recent decades, the pattern of marriage has undergone many changes. Singleness and delay in marriage are important changes in this regard. In our country, the delay in getting married and staying single has been an increasing trend over the years too. The study is to investigate the PROBABILITY of OCCURRENCE and sensitivity analysis of singleness drivers in Iran 1414. Using the drivers of singleness in Iran, assumptions were made for the future of singleness. The assumptions were matrixed by experts to comment on their PROBABILITY of OCCURRENCE. Data were analyzed by Smic-Prob Expert software. The PROBABILITY of the six hypotheses being plotted was obtained. The assumption of “ the increasing influence of mass media and social networks on changing youth attitudes” has the highest chance of occurring. PROBABILITY of potential scenarios showed that a future in which all assumptions occur, has the highest PROBABILITY of OCCURRENCE. The results of the sensitivity analysis also showed that the assumption of the influence of the mass media has the highest effect on other assumptions. This analysis showed the importance of this assumption and revealed the need for special attention to it in planning and policy making.

Accurate estimation of crop water requirement is the basic step for designing and scheduling of irrigation and drainage projects. Evapotranspiration (ET) is a stochastic variable, therefore to reduce the risk level, it is needed to select and use proper evapotranspiration PROBABILITY levels. In this research, 24 years meteorology data of Orumieh synoptic station have been used to estimate ET0. Also for investigation the effect of ET0 calculation methods on its value, FAO Penman-Monteith (FP-M) and Hargreaves-Samani (H-S) models were selected. Daily ET0 distribution curves with different PROBABILITY levels for each method were extracted.Differences between maximum daily ET0 values (calculated by FP-M method) with 75% and 50% PROBABILITY level was 1.0 mm/day (equivalent to 13%) and between 90% and 50% was 1.9 mm/day (24%). Similarly for H-S method the difference was 0.5 and 0.9 mm/day (8% and 15%), respectively. Also at a particular PROBABILITY level, there was noticeable difference between values of FP-M and H-S methods.In order to understand the impacts of calculation period length on mean daily ET0 value, daily ET0 with 99% PROBABILITY, and moving average of daily ET0 were calculated for 1, 3, 5, 7, 10, 15, 20, 25 and 30 day perrids. After determination of OCCURRENCE date of average maximum daily ET0 for the above periods, average of maximum daily ET0 with different PROBABILITY levels for the periods of 1 to 30 days were determined. The OCCURRENCE of peak dates within 1 to 20 day periods, for FP-M method was 2 weeks after H-S method but for 25 and 30 day periods the dates were the same. The results showed that the peak mean daily ET0 of FP-M method for 5 day periods at 50% and 75% PROBABILITY levels were 7.8 and 8.7 mm/day (12% difference) and for H-S method they were 6.1 and 6.6 mm/day (8% difference).

Thunderstorms are one of the climatic phenomena that cause numerous damages in different parts of the world, due to the accompaniment with thunder, high winds, hail and heavy precipitation. In this study, the PROBABILITY of thunderstorm days in Tabriz in the spring is analyzed using probabilistic rules and Markov chain model. For this purpose, the daily data related to thunderstorms (codes 95 to 99) was used for 65 years (1951-2015). At first, the daily data is classified into the normal days (code 0) and thunderstorm days (code 1). Then the frequency matrix is formed and the PROBABILITY matrix is created accordingly based on maximum likelihood method. The Markov chain properties such as empirical PROBABILITY and equilibrium PROBABILITY, Frequency of OCCURRENCE, mean time periods and weather cycle were investigated. Finally, OCCURRENCE PROBABILITY and return period of these spells were determined. The results show that the shortest weather cycle is in May, which has the highest frequency of thunderstorms. Conversely, the longest weather cycle is in April, which has the lowest frequency of thunderstorms. Also, in 65 years period, the one-day and two-day stormy sequences have the highest frequency. And For longer sequences, the frequency of thunderstorm days is reduced. The return period of one-day and twoday stormy spells is 1. 5 and 5 days. Regression relation between the observed and estimated values of n-step periods of thunderstorm days shows that the considered accuracy and reliability for all months is more than 99%.

In this research based on 51 synoptic, climatologic and rain gauge stations of Golestan province with at least 20 years length the dry spell with zero and 1 mm threshold have analyzed using Markov chain models.Spatial distribution of precipitation in Golestan is depends on longitude, latitude and specially elevation variables. So that the precipitation increases across north to south while the wet and dry days have changed with. Dry days OCCURRENCE PROBABILITY in Golestan is 0.73 to 0.91 by the mean of 0.83 while it is 0.765 to 0.91 and 0.84 respectively for precipitation less than 1 mm. The range of OCCURRENCE PROBABILITY decreasd when the precipitation threshold increased with OCCURRENCE PROBABILITY growth.The spatial changes of dry days PROBABILITY is not notable, so with based on the relationship between dry day PROBABILITY and precipitation rates concluded that increase in precipitation is caused decrease in the dry day toward less precipitation regions.  The results of this paper show more intense precipitation in south of province in compare with north parts in a few days. The dry spell is very shorter in the southern part of province in compare to the northern part in similar days.

Purpose: Diabetes is one of the chronic diseases and the most common disease related to metabolism in humans. Diabetic nephropathy, one of the dangerous complications of diabetes, has a relatively high prevalence in diabetic patients and causes kidney failure. Several factors have an effect on this disorder. The aim of this study was to determine the cumulative PROBABILITY of OCCURRENCE of nephropathy complication of type 2 diabetes using survival analysis. Methods: This study was a historical cohort study of survival analysis, which was conducted in a descriptive-analytical manner in 2019. The required sample size was estimated at 410 patients. Data were calculated using Stata. ver14 software and descriptive tests and then by Kaplan Meyer method and Cox survival proportional hazards model. Results: The variables of regular visits and smoking showed a significant relationship with the chance of nephropathy complications (p<0. 05). Therefore, the chance of nephropathy complications in people who did not have regular visits was 1. 03 times that of people who had regular visits. The Log-Rank test showed that the median survival time (months) of the OCCURRENCE of nephropathy is related to the variables of fasting blood sugar, blood pressure, systolic, and blood lipids. Conclusion: With early diagnosis of diabetes and control of the factors that accelerate the development of diabetic nephropathy and effective and timely treatments, it is possible to reduce the progress of the disease and it can be used in the field of prevention and treatment of diabetic patients and prevention of subsequent lesions and complications.