Financial decision-making is a principal part of any decisions; hence, great efforts are made to improve the methods to assess and analyze the stocks in financial markets as an important part of the financial decisions. This paper addresses the stock selection by discovering the investor's utility function. Investors in the Stock Exchange consider diverse criteria to buy securities and bonds. Due to the criteria development in stock selection, understanding the investor's behavior by a consultant is a prominent issue. Recognizing an exclusive utility function, according to the characteristics of the investors, facilitates acquiring each share's value for the decision-maker (DM). In this study, UTASTAR method is used to estimate the marginal value function by using 3 appropriate criteria (risk, return, and liquidity) and, finally, fit the total utility function. It provides an opportunity to make a rational decision adjustable to the investor's mentality that considers their ranking, prioritization, selection, or classification. The ranking of the options is as compatible as possible to the original one. The method is applied to an example from Iran Stock Exchange.

Three main methods area usually used for the synthesis of star-shaped polymers, where each one has its own advantages and disadvantages. Among these techniques, "in-out" method is the best one to produce hetero-arm star polymers with narrow molecular weight distribution and long arms with active sites at the end of the second-generation arms. This method, however, has some difficulties during the synthesis of hetero-arm star polymers with styrene or dienes as the second monomers. The reason is that in the "out" step, the carbanions of the growing chains attack the existing double bonds of the other cores and produce an irreversible gel. The new "in-in" method mentioned in this paper, is used to synthesize double-star polystyrene polymers with relatively narrow molecular weight distribution and without occurrence of any gelation during the process; With the monomers, which are able to attack the existing double bonds of the cores of the star polymers, it is also possible to produce hetero-arm star polymers using this new method.

‎A perfect star packing in a given graph $G$ can be defined as a spanning subgraph of $G$‎, ‎wherein each component is isomorphic to the star graph $K_{1,3}$‎. ‎A perfect star packing of a fullerene graph $G$ is of type $P0$ if all the centers of stars lie on hexagons of $G$‎. ‎Many fullerene graphs arise from smaller fullerene graphs by applying some transformations‎. ‎In this paper‎, ‎we introduce two transformations for fullerene graphs that have the perfect star packing of type $P0$ and examine some characteristics of the graphs obtained from this transformation‎.

Non-Dimensional star pattern recognition uses planar angles as its recognition feature. This feature is independent of image focal length and optical axis offset. However, this independency doesn’t mean that the algorithm conserves its robustness in presence of any type of errors. These errors arise from poor hardware calibration and software inaccuracy that causes the angles to differ from their true amounts stored in the database. In order to evaluate the effect of angle differences on algorithm performance, overall disposition of bright point centers is modeled. The monte-carlo simulation method is then used to evaluate the algorithm’s performance for different amounts of error.Results demonstrate that 0.1 pixel size error is admissible in conserve the trade-off between desired update frequency, hardware accuracy and algorithm’s robustness.