Salinity is a major limiting factor for crop growth and productivity especially in arid and semi-arid areas. Therefore this study was conducted in order to compare the effect of applying Nano Silver on mitigating salinity negative effects on Bitter Vetch (Vicia ervilia L.) seeds. An experiment was conducted to evaluate the effects of silver nanoparticles (AgNPs), on the seed germination, germination percentage (GP), seedlings vigor index (VI), germination index (GI), tolerance index (TI), root and shoot length (RL and SL) of Bitter Vetch Seeds Survival under Salinity Levels. To create salinity stress, sodium chloride (NaCl) at the levels of 0 (as control), 4, 8, 12, 16 and 20 dS/m and five levels of silver nanoparticles (0, 5, 10, 15 and 20 ppm) on Bitter Vetch seed were tested. The germination percentage, shoot length, root length, seedling fresh weight and seedling dry weight traits showed significant differences among Bitter Vetch seed in treatment solutions. Overall, application of Ag nanoparticles was beneficial in improving salinity tolerance in the Bitter Vetch seedling and its application may stimulate the differences defense mechanisms of plants against salt toxicity

In recent times of wide growth of information technology there has been vast number of industries which depends upon networks connections for sensitive business trading. Communications and networks are facing high risks and vulnerabilities because of improve hacking techniques. Personal, governments as well as armed classified networks are facing more difficulties so it’s so much important to install safety measure for network from illegal modification, damage or leakage of serious information. Intrusion detection known as important entity towards network security which have ability to observe network activity as well as detect intrusion / attacks. This study highlights the developing research about the application of Machine Learning and Data Mining for internet security. We provide background, enthusiasm, discussion of challenges and recommendations for the application of ML/DM in the field of intrusion detection.

Internet of Things (IoT) paradigm is the underlying constituents of 4th Industrial Revolution that will also be able to affect the use of internet and industrial production in the future. More than 50 billion smart devices will be able to communicate with each other and internet services over the increasing network capabilities of wireless sensor networks nodes on IoT applications in the next ten years. One of the leading production areas where using IoT within wireless sensor networks is precision agricultural practices. In this study, a new sensor node design which including humidity and temperature sensors employing Bluetooth Low Energy (BLE) communication protocol is used with regard to IoT applications. In this paper a greenhouse where precision agriculture is carried out was developed with the sensor nodes and investigated their power consumption and management costs.

In the recent years there has been a rapid increase in the consumption of energy drinks. Undoubtedly, a broad spectrum of age of the users is an extremely dangerous issue, especially when adolescents are reaching for energy drinks. Producers strive to increase the attractiveness of the product offered by them by adding various substances stimulating and, in theory,improving the efficiency of the body. However, the effect of these additives on the organism, when different biologically active compounds are consumed as a mixture, is not fully known. In this study 25 energy drinks available on the European markets were tested. High-performance liquid chromatography analysis was performed to determine the content of caffeine, taurine, sweeteners, preservatives, and B vitamins. The conducted research is an in-depth analysis of the chemical composition and possible interaction occurring between various substances, and thus it may provide the basis for the analysis of potential negative impact of energy drinks on the human body.

Background and Objective: Nonalcoholic fatty liver disease is the major cause of chronic liver disease worldwide. The present study aims to prepare a dietary supplement rich in antioxidants that is able to prevent nonalcoholic fatty liver associating metabolic disorders and to limit its progression into steatohepatitis. Materials and Methods: This dietary supplement is in jelly form and composed of prunes, dried grapes, carrots, cape gooseberry, pomegranate peel, chicory, sumac, sweet corn, germinated soybeans and vitamins B12, C and D. HPLC analysis and the antioxidant activity for the formulated dietary supplement were carried out. A biological experiment was done on 30 male Wistar rats categorized into 5 groups for 4 weeks after non-alcoholic fatty liver induction by feeding rats high fat and cholesterol (HFC) diet with bile acids for 8 weeks. Biochemical analysis and histopathological examination were done. Results: The supplement antioxidant activity was relatively high and HPLC analysis revealed the presence of several polyphenolic compounds. Results revealed an altered lipid profile, an elevation in activity of plasma transaminases, lipid peroxides and tumor necrosis factor alpha (TNF-?), while there was a reduction in total protein, albumin and activity of antioxidant enzymes for HFC fed group. Adding the formula to the diet of the HFC fed rats nearly normalized the aforementioned parameters. Histopathological examination reinforced the biochemical findings. Conclusion: The obtained data proved that the prepared dietary supplement managed to restore the metabolic changes accompanying fatty liver and to limit its progression into steatohepatitis.

In this work, two algorithms are suggested in order to improve the performance of systematic Repeat-Accumulate (RA) decoding. The first one is accomplished by the insertion of pilot symbols among the data stream that entering the encoder. The positions where pilots should be inserted are chosen in such a way that to improve the minimum Hamming distance and/or to reduce the error coefficients of the code.\nThe second proposed algorithm includes the utilization of the inserted pilots to estimate scaling (correction) factors. Two-dimensional (2-D) correction factor was suggested in order to enhance the performance of traditional Minimum-Sum (MS) decoding of regular repeat accumulate codes. An adaptive method can be achieved for getting the correction factors by calculating the mean square difference (MSD) between the values of received pilots and the a-posteriori data of bit and check node related to them which created by the minimum-sum (MS) decoder.

Introduction: Maternal health is one of the major goals of reproductive health arena. Changes in pregnancy make women susceptible to mood and psychological disorders. The purpose of this study is to investigate the prevalence of depression and predicting its related factors in pregnant women.\nMaterials and Methods: This descriptive, cross-sectional study was performed on 907 pregnant women referring to health centers in Jahrom in 2016. The measurement tools were Beck Depression Inventory (BDI) and demographic information. The data were analyzed by descriptive statistics, linear regression and Chi-square test.\nResults: The mean age of women was 26.45 ± 4.49. The prevalence rate of depression in pregnant women was 80.5%. The score of depression in the first, second and third pregnancy trimesters was 67.9%, 83.5%, 85.8%, respectively and it was found that depression was significantly increased with the progression of pregnancy (p = 0.001).\nThe results of regression analysis showed that the risk factors for depression in pregnancy included female education (OR = 4.67 95% .CI = 1.97-11.07), spouse\