Due to the increasing environmental pollutions and reducing fossil energy sources, using new energy in the world is of utmost importance including the solar and wind energy that used in the world. In this context and to identify areas with high potential for solar and wind plants in Semnan province, the average relative humidity data, the number of days with thunderstorms, number of days with dust, the number of cloudy days, horizontal visibility below 2,000 meters, sunshine hours and average wind speed in a 20-year period for five synoptic stations in the province was used. After the analysis made on the province's meteorological parameters, point data was changed into the zone by Arc GIS software.  To  achieve  this goal first  of  multiple regression methods  were  used  and  the  correlation  between  latitude  and  altitude  parameters obtained. Results showed that in most cases, the correlation coefficient was more than 0.9 and determination coefficient was more than 0.8 in most cases. Studies showed that lowland areas of the province have enormous potential and highlands has less potential for constructing solar and wind power plants.

Wireless sensor networks (WSNs) consist of a number of nodes and one or two base stations (BS). Each node has limited energy. Therefore, the energy each node is very important parameter in network since accessing the nodes and recharging them are difficult or in some cases, are impossible. Thus, the main purpose of this article is to increase the lifetime of the wireless sensor networks by finding the optimal route to send the data to the base station in order to save the energy of each node. In this paper, a hybrid clustering method called Hybrid based on Bayesian Networks (HBN) is proposed based on Bayesian network which considers the radio range of each nodes. In this algorithm, four different parameters are considered including residual energy, the distance to the base station, distance to the neighbor nodes and the radio range of the sensor nodes. According to the simulation results, this algorithm enables an increase in network lifetime in comparison to other similar algorithms.

To study early autumn and late spring frosts and its relation to the yield of agriculture crops in Semnan province we used statistics the least daily minimum temperature of 6 stations in the months of October and November and April during the period (1993-2014) as well as information on yield of agricultural crops from horticulture department of agriculture Organization. Using Pearson correlation coefficient between yield and frequency of early autumn and late spring frosts were studied and finally induced to study the frosts trends for each station. Early frosts of autumn in the province date from 20 October to 23 November and latest frosts ranged from 3 to 23 April and Biarjomand station experience earliest autumn and latest spring frosts. The Relationship between crop yields with frost Occurrence frequency shows that by increasing the number of days of early autumn frosts reduced wheat yield. This condition is for crops such as peas, walnuts and grapes, too. Amongst, the greatest impact is on the potato crop, which is closely related to the pattern of cultivation and harvest. On the other hand, it was found that late spring frosts in the province have the greatest impact on horticultural crops such as cherries, peaches, walnuts and grapes. As a result, early and late frosts changes in Semnan and Miami has increased, which in coming years will be also affected by this situation. Also, early frosts of autumn at harvest time and late spring frosts during flowering have many effects on crop yield.