Chemical composition and bioactivities of the volatile oil of the seeds of Eryngium bungei Boiss.

The volatile oil of the seeds of Eryngium bungei Boiss. (family Apiaceae), was obtained by hydrodistillation and analyzed by the GC-MS and the GC-FID. The analyses revealed at least 69 compounds representing 94 % of the total oil. The results showed that the oil was dominated by chrysanthenyl acetate (20.0 %), spathulenol (17.2 %), endo-isofenchol (10.8 %) and α-pinene (5.1 %). The free-radical-scavenging activity of the oil was evaluated by DPPH assay and RC50 value was calculated as 7.5 µg/mL. The antifungal and phytotoxic activities of the oil were tested against Sclerotinia sclerotiorum, and some common weeds. The MIC value for anti sclerotinia activity of the seed oil was evaluated as 12.5 µg/mL. The IC50 values were determined as 1.32-2.1 µg/mL for inhibitory effects of the oil on seed germination of different weeds. This is the first report on chemical composition and bioactivities of the volatile oils of the seeds of Eryngium bungei

An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems

Swarm intelligence is a relatively recent approach for solving optimization problems that usually adopts the social behavior of birds and animals. The most popular class of swarm intelligence is ant colony optimization (ACO), which simulates the behavior of ants in seeking and moving food. This chapter aim to briefly overview the important role of ant colony optimization methods in solving optimization problems in time-varying and dynamic environments. To this end, we describe concisely the dynamic optimization problems, challenges, methods, benchmarks, measures, and a brief review of methodologies designed using the ACO and its variants. Finally, a short bibliometric analysis is given for the ACO and its variants for solving dynamic optimization problems

Prediction of the Number of Carbon Atoms in Various Nanostructures by Using Geometrical Approach

With the growth of nanotechnology, many attempts have been made on the chemical and physical properties of nanostructures. Due to relation between physical properties and geometrical structure, understanding of the geometrical structure is very important. Moreover, this can be useful for finding unknown structures that have not been produced in laboratory yet. In the present work, first we have investigated the structure of some nanostructures from the geometrical point of view. Then an algorithm is proposed for discovering the number of carbon atoms in various nanotubes and fullerenes. In the presented algorithm, a nanosheet in 2D space is considered as a start point. Creating twelve nanocones in nanosheet makes it a nanostructure. Different nanostructures are produced by relocation of nanocones. The result shows that the number of carbon atoms in different nanostructures is a sequence that has infinite harmonies and follows a simple formula. Each harmony is an arithmetic progression

Using Multi-objective Optimization for Automated Graph Drawing

Abstract- A Genetic Algorithm (GA) is the process of constructing an optimization problem in which several objectives can be optimized at the same time. In this paper, Strength Pareto Evolutionary Algorithm (SPEA), a GA based multi-objective optimization technique, has been applied to a graph drawing (GD) problem. In this paper a measure (force equalization) which contributes to production of nicely drawing is introduced by a GA. We show that using the introduced objective and keeping the Pareto optimal solutions of the GD process in a pool, the following advantage can be achieved: A single run of the GA process leads to several suboptimal solutions which provide the user with a selection right; to choose the most effective drawing

A novel regularized weighted estimation method for information diffusion prediction in social networks

Abstract In recent years, social networks have become popular among Internet users, and various studies have been performed on the analysis of users’ behavior in social networks. Information diffusion analysis is one of the leading fields in social network analysis. In this context, users are influenced by other users in the social network, such as their friends. User behavior is analyzed using several models designed for information diffusion modeling and prediction. In this paper, first, the problem of estimating the diffusion probabilities for the independent cascade model is studied. We propose a method for estimating diffusion probabilities. This method assigns a weight to each individual diffusion sample within a network. To account for the different effects of diffusion samples, several weighting schemes are proposed. Afterward, the proposed method is applied to real cascade datasets such as Twitter and Digg. We try to estimate diffusion probabilities for the independent cascade model considering the continuous time of nodes’ infections. The results of our evaluation of our methods are presented based on several datasets. The results show the high performance of our methods in terms of training time as well as other metrics such as mean absolute error and F-measure

Generation of the Figures of Some Fullerenes by Using L-Systems

In 1968, Aristid Lindenmayer introduced a biologically-motivated formalism for simulating the development of multi-cellular organisms, subsequently named L-systems. The applications include, on one hand, the modeling and visualization of plants at different levels of abstraction for a variety of purposes, and, on the other hand, geometric modeling of curves and surfaces. In this paper, we introduce L-system for generating NiceGraph drawing of fullerenes C20 and C60, without information about the carbon atoms coordination. We chose L-systems because they can express drawing steps in a compact way and are parallel in nature. It will be a good trend for visualizing complex supramolecules

Active saltwater intrusion of shrinking Bakhtegan -Thask Lakes in South Iran threatens the freshwater resources of coastal aquifers

Study regionEight Coastal aquifers (CA) of Bakhtegan and Tashk lakes (BTL) and salt marsh, southern IranStudy focusBTL, Wetlands of International Importance, have been shrinking due to reduction in surface discharge, groundwater overexploitation, and drought. We show that this resulted in an increase of BTL total dissolved solids (TDS) from 45400 to 256000 mg/l. To characterize the hydrogeological behavior of the coastal aquifers after shrinkage, major ions, TDS and water level were measured along a transect from inside the BL to one of the aquifers. The spatial distribution of electrical conductivity (EC), discharge and iso-potential maps was determined. A support vector regression technique was used to forecast EC and water level in CA for the next decade.New hydrological insights for the regionFive zones based on EC changes and hydraulic gradient are recognized, namely: brine, highly saline, brackish, transition, and freshwater. The groundwater flow direction is from both the BTL and Fresh Water Zone, converging towards the intermediately positioned Transition Zone. Saltwater intrusion is active based on the hydraulic gradient towards the land and the gradient of water density. A schematic flow model of CA was proposed based on active SWI, groundwater flow direction, and chemical signature. Forecasting reveals a significant further deterioration of water quality and drop in groundwater levels, which emphasizes the need for systematic and sustainable water management

Temporal dynamics of inundation area, hydrochemistry and brine in Bakhtegan Lake, South-Central Iran

Study region: Bakhtegan Lake, southern Iran, is a “Wetland of International Importance” (Ramsar Site). Study focus: This study focuses on analyzing the time series of the lake's inundation area, identifying factors contributing to its shrinkage, and studying its hydrochemical characteristics. To map the inundation area, Landsat images from 1972 to 2019 were used and 64 water samples were collected from the lake during 2017–2019 for geochemical modeling. New hydrological insights for the region: The study reveals that the Bakhtegan Lake has become a seasonal lake with a long-term dry state since 2007. The lake's inundation area shows a significant correlation with the Kor River discharge, and the main reason for the lake's shrinkage is a decrease in river inflow due to over-exploitation in the basin and construction of two new dams since 2007. The lake water and brine below the lake bed have TDS concentrations varying between 70000 and 451000 mg/L and 118000–373000 mg/L, respectively. The Gibbs, Na-normalized ratio end-member diagrams show that the lake water chemistry is mainly controlled by evaporation. The saturation index indicates that brine samples were in an equilibrium state with gypsum, halite, and glauberite. The Spencer diagram and evolutionary pathway model suggest that water samples shifted toward natural sulfate-rich minerals during evaporation. The lake water evolution model predicts precipitation of halite, kieserite, and carnallite minerals during progressive evaporation