In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

Abstract The interest in the oxo-enol tautomerism of 1,3-dioxo compounds is justified by their usefulness in many synthetic fields. A series of new 1,3-bis(1-adamantyl)propan-1,3-diones with a variably substituted phenyl ring at the \{C2\} position was prepared either by the reaction of an appropriate Grignard reagent with adamatane-1-carbonyl chloride or by \{SEAr\} on the unsubstituted 1,3-bis(1-adamantyl)-2-phenylpropan-1,3-dione. In addition to the single crystal X-ray diffraction analysis of three of the prepared compounds, the experimental 1H and 13C NMR, İR\} and Raman spectroscopic data were assigned and compared to those obtained by \{DFT\} computations. In the solid state, the syn-dioxo forms were exclusively observed, which are shown to also predominate in \{CHCl3\} solutions. The analysis of the Hirshfeld surface revealed that H⋯H and O⋯H contacts dominate the intermolecular interactions in the solid state, whereas π⋯π stacking plays a marginal role.

Geographic Information System (GIS) is one of the fastest growing technologies in the world. Its ability to analyze spatial data is impressive. Data is the backbone of GIS because its quality range affects the results of the application so one of the most important problems in GIS is how to choose the GIS (data), which suits the application requirements and user needs exactly and in the best way among set of data to avoid wrong outputs and any lose in time and money

Dihydrofolate reductase has been purified 40-fold to apparent homogeneity from a trimethoprim-resistant strain of Escherichia coli (RT 500) using a procedure that includes methotrexate affinity column chromatography. Determinations of the molecular weight of the enzyme based on its amino acid composition, sedimentation velocity, and sodium dodecyl sulfate gel electrophoresis gave values of 17680, 17470 and 18300, respectively. An aggregated form of the enzyme with a low specific activity can be separated from the monomer by gel filtration; treatment of the aggregate with mercaptoethanol or dithiothreitol results in an increase in enzymic activity and a regeneration of the monomer. Also, multiple molecular forms of the monomer have been detected by polyacrylamide gel electrophoresis. The unresolved enzyme exhibits two pH optima (pH 4.5 and pH 7.0) with dihydrofolate as a substrate. Highest activities are observed in buffers containing large organic cations. In 100 mM imidazolium chloride (pH 7), the specific activity is 47 mumol of dihydrofolate reduced per min per mg at 30 degrees. Folic acid also serves as a substrate with a single pH optimum of pH 4.5. At this pH the Km for folate is 16 muM, and the Vmax is 1/1000 of the rate observed with dihydrofolate as the substrate. Monovalent cations (Na+, K+, Rb+, and Cs+) inhibit dihydrofolate reductase; at a given ionic strength the degree of inhibition is a function of the ionic radius of the cation. Divalent cations are more potent inhibitors; the I50 of BaCl2 is 250 muM, as compared to 125 mM for KCl. Anions neither inhibit nor activate the enzyme.

This study focuses on an ad hoc machine-learning method for locating archaeological sites in arid environments. Pleiades (P1B) were uploaded to the cloud asset of the Google Earth Engine (GEE) environment because they are not yet available on the platform. The average of the SAR data was combined with the P1B image in the selected study area called Blad Talh at Gafsa, which is located in southern Tunisia. This pre-desert region has long been investigated as an important area of Roman civilization (106 BCE). The results show an accurate probability map with an overall accuracy and Kappa coefficient of 0.93 and 0.91, respectively, when validated with field survey data. The results of this research demonstrate, from the perspective of archaeologists, the capability of satellite data and machine learning to discover buried archaeological sites. This work shows that the area presents more archaeological sites, which has major implications for understanding the archaeological significance of the region. Remote sensing combined with machine learning algorithms provides an effective way to augment archaeological surveys and detect new cultural deposits.