Due to the importance of trend study of rivers discharges, in this paper, the trend of Average discharge variations of the rivers in Urmia Lake basin was analyzed. Investigating the role of discharge changes, entering to the lake on lake’ s surface reduction was another goal of this study. For this purpose, the recorded data of 65 hydrometric stations (during 1978-2011) were analyzed. Non-parametric Mann-Kendall method considering autocorrelation effect was used for trend analysis in this paper. On the basis of the obtained results, discharge time series was significant at 95% confidence level in more than half of the stations (41 stations) having autocorrelation with 1-year lag. Also, more than half of the stations (58 stations) had a descending trend and this trend was significant at 95% confidence level in 38 stations. The Average volume of surface water entering the lake have been reached from 4654 MCM in years before 1995 (the starting year of drying) to 2134 MCM in years after 1995. The time series of surface water entering the Urmia Lake had a decreasing trend and on the basis of t-student, the difference between Average discharges during the two periods (after and before 1995) was significant at 95% confidence level. The results of this study shows a reduction in the surface waters across the whole basin area discharging to the Urmia Lake during the years after 1995 and the role of this phenomenon on the lake’ s water level fall. This emphasizes that the proper management of water resources in different consumption sectors including agriculture should be considered due to climatological governing situations.

Precise prediction of monthly Average discharge values input to water resources such as dams has a basic role in their planning, management, sustainable and optimal operation. Given the input discharge value to dam, the annual input water volume can be calculated, and well-management for water optimum allocating to various consumption sectors, including edible, agricultural, hydro-electrical production can be scheduled. There are various parameters affecting the input discharge value. They are not fully known, and their relationship with input discharge is non-linear and complex. Thus, giving analytical and mathematical relationship of this concern is difficult and impossible. Artificial Neural Networks, due to their unique properties, have high abilities in non-linear and complex relation simulation. In this study it is attempted to design multi-layer Perceptron with Back Propagation learning rule for recovering the non-linear relationship between dependant and independent variables, so that, using it, prediction of monthly Average input discharge to Queshlaq dam could be done. For further validation of the proposed model, obtained results from neural network model were compared with the ones obtaining from Khosla's empirical method. The results from the study showed that there is an acceptable overlapping between predicted values from Artificial Neural Networks and observed data, as well as the proposed neural network model and Khosla's empirical method predicts the monthly Average discharge with root mean square error as 1.49 and 11.88 respectively.

In this paper, the finitely many constraints of a fuzzy relation inequalities problem are studied and the linear objective function on the region defined by a fuzzy max-Average operator is optimized. A new simplification technique which accelerates the resolution of the problem by removing the components having no effect on the solution process is given together with an algorithm and a numerical example to illustrate the steps of the problem resolution process.

Shortage of water resources and the growing concern about the sustainable development have made the water supply for all of the potential needs nearly impossible. As an accurate prediction of river discharge is very important in water resources management, the development of a model to predict discharge has been carried out using the genetic programming and auto regression moving Average on the Amameh Watershed located in the Province of Tehran. The long-term rainfall, temperature, discharge, relative humidity, and evaporation data have been used. Satisfactorily, the results showed that genetic programming had a lower error and could estimate the observed discharge. Furthermore, the number 54 model with inputs of temperature, rain, the delay in rainfall of up to two days, relative humidity, evaporation, and the delay in discharge of up to two days were considered as the best fit model with the errors of 0. 001, 0. 031 and 0. 009 in the training stage and 0. 002, 0. 032, and 0. 009 at the testing stage respectively. On the other hand, the linear auto regression moving Average models showed a much higher error; they could neither predict the high discharge, nor low flow and have not been able to provide satisfactory results. Therefore, the application of a genetic programming model is recommended due to its high precision with the main operators and the standardized data.

When any extraordinary sacrifice or expenditure is intentionally and reasonably made or incurred for the common safety for the purpose of preserving from peril, the property (ship and cargoes) involved in a common maritime adventure. The interests saved, are required to make contributions in general Average to the owner of lost or damaged property, general Average rules originates from ancient maritime customs and laws. In cours of time, these rules were involved in changes and adopted, gradually, in national statues. of course, for the reason of some differences of national maritime laws, that its consequences affected, in case of conflict of laws, the parties to the disputes, the uniform York- Antwerp rules, were adopted in international level. The rules have been amended periodically, the latest changes were agreed upon at Sydney in 1994. In practice, owing to special clauses in standard from contracts - principally bills of lading – general Average is adjusted according to these rules.

Introduction: The importance of planning and managing water resources, as well as an increasing population growth and the limitation of surface water resources in the country, has made the accurate prediction of rivers' flow by using modern tools and methods of modeling, as an inevitable necessity. In addition, proper river flow prediction in river management, flood warning systems, and especially planning for optimal operation is required. In order to predict the flow of a river, several methods have been developed over the past years. In general, these methods can be classified into two categories of conceptual models and models based on statistics or data. The basis of the most of the predictive methods is the simulation type of the current status of the system which is called "modeling". Considering that in most cases the conceptual models require accurate data and knowledge of processes affecting the phenomenon, and this has so far been accompanied by many problems, researchers have turned to the statistical models. Over the past four decades, time-series models have been widely used in predictive river flow as a statistical model. In each science, the collected statistics corresponding to the variable which is going to be predicted and which is available in the past periods are called time series. Indeed, a time series is a set of statistical data collected at equal and regular intervals. Statistical methods that use such statistical data are called analytical methods of time series. The basis of many decisions in hydrological processes and decisions on exploitation of water resources is according to the prediction and analysis of time series. Assessment of the temporal changes of base flow in watersheds, particularly in low flow seasons is very important. Methodology: Time series models are represented in three main forms: self-correlated models (AR), moving Average (MA) models, and self-correlated and moving Average (ARMA) models. The condition of using these models is the static nature of the used data. If the data is not static, the data series must be static with the existing methods. The existence of "I" in ARIMA indicates the non-static nature of the original data and the change in the data for modeling. If the data series has a cyclic and rotational state, the type of model is seasonal or SARIMA. Time series models have two components of (p, d, q) and s (P, D, Q). S (P, D, Q) is a seasonal component. P and q are respectively autoregressive parameters and non-seasonal moving Average. P and Q are autoregressive parameters and seasonal moving Average. The other parameters, D and d, are differential parameters for making the time series static. Result: The statistical and probabilistic models have been presented and developed. This study aimed to analyze and compare the performance of series 30 and 56 years and monthly Average discharge related to the Kakareza River in the Selsele city and the Kashkan Afrineh River in the Poldokhtar city in Lorestan province. To this end, the first climate in this region was determined. Next, the autocorrelation function and partial autocorrelation real data draws in XLSTAT software was done. Subsequently, the data was normalized using the Box-Cox and logarithmic. Then, the data trend that indicated non-stationary was determined. After that by using the different operator in MINITAB software, the data trend was removed and the suitable model with the lowest Akaike was selected. Then both periods 12 and 24 months for the two regions were simulated. Results showed that the selected models in 12 and 24 months periods had respectively a correlation coefficient of. 92 and. 86 for the kakareza river and. 94, . 88 for the Kashkan Afrineh river. Discussion and conclusion: The most significant difference between the observed and the simulated values is in two months of Esfand and Farvardin. In addition, due to high precipitation, there was a significant increase in the amount of discharge in Farvardin. According to the climatic conditions in the study areas, the model showed a better performance in semi-arid areas.

Background and objectives: Usually, patients with femur fractures, including peritrochanteric fractures, have associated comorbidities, which increase the duration of hospitalization, increase the rate of complications, and increase mortality. The aim of this study was to determine the Average time between hospitalization and surgery and discharge of patients over 60 years old with peritrochanteric fracture in Shahid Madani Hospital in 2022. Materials and methods: The current study is a cross-sectional study. Demographic information, underlying diseases, BMI, length of time between hospitalization and surgery, surgery and discharge, and hospitalization and discharge were collected. The relationship between prognostic factors and length of hospitalization was investigated and the significance level of p value was less than 0. 05 was considered. Findings: A total of 250 patients (114 women and 136 men) with an Average age of 75. 6 ± 98 years were included in the study. The Average time between hospitalization and surgery was 60. 7 ± 23. 8 hours, and the time between surgery and discharge was 24. 63. 3 ± 4 hours and the time between hospitalization and discharge was 124. 1 ± 35. 9 hours. In female gender, age over 75 years and suffering from diabetes, cardiovascular disease, hypertension and respiratory disease, the time between hospitalization and Surgery was significantly higher. In female gender, age over 75 years, increased BMI, diabetes and respiratory disease, the duration between surgery and discharge was significantly higher. In general, increasing the length of time between hospitalization and discharge was significantly related to female gender, age over 75 years, increased BMI and diabetes, cardiovascular disease, hypertension, and respiratory disease. Conclusion: Conducting this study led to the identification of risk factors that can be adjusted in patients, so that by reducing the complications of fracture and surgery, as well as reducing the duration of hospitalization, the chances of recovery and faster return to daily life can be increased.

Prediction of input flow into water resources is regarded as one of the most important issues in optimum planning and management in producing electro-water energy and optimum allocation of water into different consumption sources. Different parameters affect on input discharge into dams. Climate variables including temperature and rainfall have the most effect on input runoff rate to water resource in dry and semi-dry regions like Iran. A suitable monthly runoff-rainfall model is a strong tool to consider the climate changes effect on accessibility of water to produce electro-water energy. The investigations have shown that the relation between runoff rate and effective variables is non-linear and complicated. Artificial Neural Networks due to their unique properties have a tremendous capability in non-linear relations simulation. Artificial Neural Networks establish a great change in analyzing dynamic systems behavior in different water-science engineering. In this paper it has been attempted to design static network to recover the non-linear relations between dependant and independent variables, so that the intelligent discharge estimation of Average monthly input to Vahdat dam can be done by its help. In addition, by designing and extension of dynamic neural network model based on times series performance, the amount of the monthly input discharge to the dam was predicted. Considering the capability of Artificial Neural Networks, these networks were used for modeling the rivers monthly discharge non-linear time series. Analysis of time series having two major goals, random mechanism understanding or modeling and future series value prediction was done base on previous ones. Also, the performance of the designed models was evaluated by comparing results of the static and dynamic neural network. The results of the investigation showed that there is a good conformity between the predicted values given by combined neural network and observed data. Furthermore, the results showed that the time series dynamic neural network model predict the monthly discharge more accurate than static model.