a b s t r a c tUrmia lake basin located in northwestern Iran is the second largest saline lake in the world. Due to many reasons i.e. climate changes, several dam constructions, building a bridge across the Lake, extra agricultural consumption and improper management of water resources, the water level of the lake has been decreased since 1997 and thousand hectares of emerged salty land has made numerous ecological and environmental problems. Therefore, an accurate forecast of the entrance runoff to the lake is important in managing the river flow and water transfer within basins. There are various methods for time-series based forecasting; in the presented study Feed-forward Neural Network and Autocorrelation Regressive Integrated Moving Average (ARIMA) models were applied to forecast the monthly rainfall in Urmia lake basin. The results showed that the estimated values of monthly rainfall through Feed-forward NN were close to ARIMA model with coefficient of correlation 0.62 and the root mean square error of 12.43 mm over the 6 years test period; then rainfall amount were predicted for a 6-year period starting from

Purpose An analysis and identification of the hidden relationships between effective factors in the mortality rate caused by road accidents in Fars Province of Iran to prevent and reduce traffic accidents in the future. Methods This cross-sectional study was conducted to integrate all the pervious researches performed on mortality rate of road traffic accidents in Fars Province from March 21, 2013 to March 20, 2017. In order to reveal the relationships between the factors affecting mortality rates of road traffic accidents, the data regarding road traffic accidents extracted from resources such as Legal Medicine Organization, Traffic Police, Accident & Emergency Department, as well as Department of Roads and Urban Development of Fars Province, then cleaned and the applicable attributes embedded in the data all aggregated for further analysis. It should be noted that the data not related to Fars Province were deleted, the data analyzed, converted and the aggregation between various attributes identified. The aggregation between these different attributes as well as the FP-growth algorithm and two indexes of support and confidence calculated and interesting and effective rules extracted. In the end, several accident-provoking factors, the degree of consecutive and interdependence of each one in road accidents identified and introduced. The statistical analysis was conducted by using Rapid Miner software. Results Of the 6216 people dead due to road traffic accidents, 4865 (79.02%) were male and 1292 (20.98%) were female, 59 of them have no clear gender. The largest portion of people died of road traffic accidents belonged to married and self-employed men who collided with motorcycles in autumn. Moreover, young individuals (aged 19–40 years) with secondary educational level who died of accidents in summer at 12:00 a.m. and then 5:00 p.m. in outer city main roads of Kazerun-Shiraz, then Darab-Shiraz, Fasa-Darab and in within-city main streets had the highest mortality rates. Among women, the middle-aged group (aged 41–65 years) followed by young-aged group (aged 19–40 years) with elementary educational level and then illiterate accounted for the highest mortality rate of road traffic accidents. The automobiles involved in accidents included Pride, Peugeot 405, Peykan pickup, Samand, Peugeot Pars, other vehicles and motorcycles. Conclusion The high mortality rate of illiterate and low-literate in various age groups indicates that educational level plays a crucial role as a factor in road accidents, requiring related organizations such as Traffic Police and Ministry of Education to take necessary measures and policies.

Propolis is a resinous substance produced by honey bees that is very popular as a natural remedy in traditional medicine. The current research is the first study on the biological properties of ethanolic extracts of propolis (EEP) from several different regions (12) of Iran. Total phenolic and flavonoid contents (TPC and TFC) of Iranian EEPs were variable between 26.59-221.38 mg GAE/g EEP and 4.8-100.03 mg QE/g EEP. The DPPH scavenging assay showed all the studied EEP samples, except for the sample with the lowest TPC and TFC (P6), have suitable antioxidant activity. All the EEPs inhibited both cholinesterase enzymes (acetylcholinesterase: AChE, butyrylcholinesterase: BuChE) but most of them exhibited a distinct selectivity over BuChE.Evaluation of the antibacterial activity of the EEP samples using four pathogenic bacteria (B. cereus, S. aureus, A. baumannii, and P. aeruginosa) demonstrated that the antibacterial properties of propolis are more effective on the gram-positive bacterium.Spearman correlation analysis showed a strong positive correlation between TPC and TFC of the Iranian EEPs and their antioxidant, anticholinesterase, and antibacterial activities. Considering that there is ample evidence of anticholinesterase activity of flavonoids and a significant correlation between the anticholinesterase activity of the studied Iranian EEPs and their total flavonoid content was observed, the interaction of 17 well-known propolis flavonoids with AChE and BuChE was explored using molecular docking. The results indicated that all the flavonoids interact with the active site gorge of both enzymes with high affinity. Summing up, the obtained results suggest that Iranian propolis possesses great potential for further studies.

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