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Stock data sets usually consist of many varied components or multiple periods of stock prices, resulting in a tedious stock market prediction using such high dimensional data. To reduce data dimensions, it is crucial to fuse high dimensional data into a useful forecasting factor without losing information contained in the original variables. Decision makers may desire low variability associated with a chosen weighting vector, further complicating proper weight assignment for past stock prices. In this paper a new time series algorithm is proposed to overcome above mentioned shortcomings, which employs a minimal variation order weighted average (OWA) operator to aggregate values of high dimensional data into a single attribute. Based on the proposed model a hybrid network based fuzzy inference system combined with fuzzy c-means clustering is used to forecast Bombay Stock Exchange Index (BSE30).

We present a three-step iterative method of convergence order five for solving systems of nonlinear equations. The methodology is based on Newton's and Newton-like iterations. Hence, the name Newton-like method. Computational efficiency of the new method is considered and compared with well-known existing methods. Numerical tests are performed on some problems of different nature, which confirm robust and efficient convergence behavior of the proposed technique. Moreover, theoretical results concerning order of convergence and computational efficiency are verified in the numerical problems. It is shown that, in general, the new method is more efficient than the existing counterparts.

Based on the family of three-point derivative free methods without memory of eighth order convergence proposed by Džunic et al. [J. Džunic, M.S. Petkovic, L.D. Petkovic, Three-point methods with and without memory for solving nonlinear equations, Appl. Math. Comput. 218 (2012) 4917–4927], we present three methods with memory by suitable variation of a free parameter in each iterative step. This free parameter is calculated using Newton’s interpolatory polynomial of the third degree in two ways and Newton’s interpolatory polynomial of the fourth degree. Consequently, the R -order of convergence is increased from 8 to 11 + 137 2 ≈ 11.352 , 6 + 4 2 ≈ 11.657 and 12. The increase in the convergence order is achieved without any additional function evaluations and therefore, the proposed methods possess a very high computational efficiency. Numerical examples are presented and the performance is compared with the existing three-point methods with and without memory of the basic family. Moreover, theoretical order of convergence is verified on the examples.

In this paper, we develop a fourth order method for solving the systems of nonlinear equations. The algorithm is composed of two weighted-Newton steps and requires the information of one function and two first Frechet derivatives. Therefore, for a system of n equations, per iteration it uses n?+?2n 2 evaluations. Computational efficiency is compared with Newton's method and some other recently published methods. Numerical tests are performed, which confirm the theoretical results. From the comparison with known methods it is observed that present method shows good stability and robustness.

Aluminium alloys display complex phase transitions to achieve their desired properties.Many of these involve elaborated precipitation sequences where the main role is not played by ther-modynamically stable species, but by metastable precipitates instead. An interplay between phasestability, crystal symmetry, diffusion, volume and particle/matrix interfaces sets the pace for the ki-netics. Thermodynamic modelling, which focuses on stable precipitates, is not an aid in describingsuch processes, as it is usually transitional phases that achieve the desired properties. The model pre-sented here combines first--principles to obtain the transition precipitate energetics (both at the bulkand at the interface with the matrix) with thermochemical databases to describe the overall kineticsof stable precipitates. Precipitate size and number density are captured via the Kampmann--Wagnernumerical approach, which is embedded in a genetic algorithm to obtain optimal compositional andheat treatment scenarios for the optimisation of the mechanical properties in aluminium alloys of the 7000 series.

We present here a one-parameter family of iterative methods for solving nonlinear equations. All the methods of the family have third-order convergence, except the one which has the fourth-order convergence. Per iteration, all these methods require two evaluations of the function and one evaluation of the first derivative. Therefore, the fourth-order method is more economic than the third-order methods. Numerical examples are given to support that the methods thus obtained are competitive with other similar robust methods. Moreover, it is shown by way of illustration that the fourth-order method is very useful in the applications requiring high precision in computations.

In this paper, we derive two higher order multipoint methods for solving nonlinear equations. The methodology is based on Ostrowski’s method and further developed by using cubic interpolation process. The adaptation of this strategy increases the order of Ostrowski’s method from four to eight and its efficiency index from 1.587 to 1.682. The methods are compared with closest competitors in a series of numerical examples. Moreover, theoretical order of convergence is verified on the examples.

Based on Hansen-Patrick method [E. Hansen, M. Patrick, A family of root finding methods, Numer. Math. 27 (1977) 257-269], we derive a two-parameter family of methods for solving nonlinear equations. All the methods of the family have third-order convergence, except one which has the fourth-order convergence. In terms of computational cost, all these methods require evaluations of one function, one first derivative and one second derivative per iteration. Numerical examples are given to support that the methods thus obtained are competitive with other robust methods of similar kind. Moreover, it is shown by way of illustration that the present methods, particularly fourth-order method, are very effective in high precision computations.

Based on Ostrowski fourth order multipoint method, we derive a one-parameter family of sixth order methods for solving equations. Each member of the family requires three evaluations of the given function and one evaluation of its derivative per iteration. Numerical examples are presented and performance is compared with Ostrowski method.

In this paper we present a new algorithm for solving nonlinear equations and this method requires the use of the first and second derivatives of the function as Halley’s, Laguerre’s, Chebyshev’s and other classical methods do. It has well known geometric interpretation and admits its geometric derivation from a circle. Further, the executed comparative numerical experiments show that this new method competes well with the known classical methods and has cubic convergence.

The forecasting of stock market problem from the available data is quite often of uncertain nature, hence the stock market prediction is a very challenging and difficult task. In this paper, we have investigated the predictability of stock market of Bombay Stock Exchange BSE30, Hang Sang China Stock Index HS, Japan Stock Index NIKKEI and Taiwan Weighted Index TWI with adaptive network-based fuzzy inference system ANFIS combined with subtractive clustering technique. In this process, we compared stock markets with variable numbers of data clusters. Optimised subtractive clustering is used to cluster the data and create fuzzy membership functions. Finally, a hybrid learning algorithm has been used to combine least square method and back propagation gradient-decent method for training the fuzzy inference system. This paper represents a state of the art for ANFIS application to forecast stock market index.

We present the first report of hexagonally ordered mesostructured ternary metal tin sulfide materials along with their structure and optical properties. The new mesophases designated as (CP)(x)MySn2S6 (M=Zn2+, Cd2+, Ga3+) where synthesized by linking [Sn2S6](4-) clusters with metal ions in the presence of cetylpyridinium (CP) surfactant molecules in formamide solution under basic conditions. The materials are semiconductors in the energy range 2.5 < E-g < 3.1 eV. The mesostructured (CP)(x)MySn2S6 show photoluminescence, when excited with light above the band gap. Nano Lett

If the full scientific and technological potential of mesostructured materials is to be achieved, systems with continuous domains in the form of single crystals or films must be prepared. Here we report a reliable and facile system for making large single-crystal particles of chalcogenido mesostructured materials with a highly organized cubic structure, accessible pore structure, and semiconducting properties. Building blocks with square planar bonding topology, Pt2+ and [Sn2Se6](4-), in combination with long-chain pyridinium surfactants (CnPyBr, n = 18, 20) favor faceted single-crystal particles with the highest possible space group symmetry la (3) over bard This is an important step toward developing large single-domain crystalline mesostructured semiconductors and usable natural self-assembled antidot array systems. The tendency toward cubic symmetry is so strong that the materials assemble readily under experimental conditions that can tolerate considerable variation and form micrometer-sized rhombic dodecahedral cubosome particles. The c-CnPyPtSnSe materials are the first to exhibit reversible ion-exchange properties. The surfactant molecules can be ion-exchanged reversibly and without loss of the cubic structure and particle morphology. The cubosomes possess a three-dimensional open Pt-Sn-Se framework with a low-energy band gap of similar to1.7 eV. J Am Chem Soc

The synthesis and properties of new hexagonal ordered mesostuctured phases of framework containing adamantane [Ge(4)Q(10)](4-) (Q = S, Se) clusters linked by Sb3+ and Sn4+ ions and templated with cetylpyridinium surfactant are reported. Chemical Communications

Open framework metal chalcogenide solids, with pore sizes in the nano- and mesoscale, are of potentially broad technological and fundamental interest in research areas ranging from optoelectronics to the physics of quantum confinement(1,2). Although there have been significant advances in the design and synthesis of mesostructured silicas(3,4), the construction of their non-oxidic analogues still remains a challenge. Here we describe a synthetic strategy that allows the preparation of a large class of mesoporous materials based on supramolecular assembly of tetrahedral Zintl anions [SnSe4](4-) with transition metals in the presence of cetylpyridinium (CP) surfactant molecules. These mesostructured semiconducting selenide materials are of the general formulae (CP)(4-2x)MxSnSe4 (where 1.0 < x < 1.3; M = Mn, Fe, Co, Zn, Cd, Hg). The resulting materials are open framework chalcogenides and form mesophases with uniform pore size (with spacings between 35 and 40 Angstrom). The pore arrangement depends on the synthetic conditions and metal used, and include disordered wormhole, hexagonal and even cubic phases. All compounds are medium bandgap semiconductors (varying between 1.4 and 2.5 eV). We expect that such semiconducting porous networks could be used for optoelectronic, photosynthetic and photocatalytic applications. Nature