Exchange-Rates Forecasting: A Hybrid Algorithm Based on Genetically Optimized Adaptive Neural Networks

The use of neural networks trained by a new hybrid algorithm is employed on forecasting the Greek Foreign Exchange-Rate Market. Four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DEM), the French Franc (FF) and the British Pound (GBP), versus the Greek Drachma, were used as experimental data. The proposed algorithm combines genetic algorithms and a training method based on the localized Extended Kalman Filter (EKF), in order to evolve the structure and train Multi-Layered Perceptron (MLP) neural networks. The goal of this effort is to predict, as accurately as possible, exchange-rates future behavior. Simulation results show that the method gives highly successful results, while the diversification of the structure between the four currencies has no effect on the performance.     

Copper(I) Thiocyanate (CuSCN) Hole‐Transport Layers Processed from Aqueous Precursor Solutions and Their Application in Thin‐Film Transistors and Highly Efficient Organic and Organometal Halide Perovskite Solar Cells

This study reports the development of copper(I) thiocyanate (CuSCN) hole‐transport layers (HTLs) processed from aqueous ammonia as a novel alternative to conventional n‐alkyl sulfide solvents. Wide bandgap (3.4–3.9 eV) and ultrathin (3–5 nm) layers of CuSCN are formed when the aqueous CuSCN–ammine complex solution is spin‐cast in air and annealed at 100 °C. X‐ray photoelectron spectroscopy confirms the high compositional purity of the formed CuSCN layers, while the high‐resolution valence band spectra agree with first‐principles calculations. Study of the hole‐transport properties using field‐effect transistor measurements reveals that the aqueous‐processed CuSCN layers exhibit a fivefold higher hole mobility than films processed from diethyl sulfide solutions with the maximum values approaching 0.1 cm² V^?1 s^?1. A further interesting characteristic is the low surface roughness of the resulting CuSCN layers, which in the case of solar cells helps to planarize the indium tin oxide anode. Organic bulk heterojunction and planar organometal halide perovskite solar cells based on aqueous‐processed CuSCN HTLs yield power conversion efficiency of 10.7% and 17.5%, respectively. Importantly, aqueous‐processed CuSCN‐based cells consistently outperform devices based on poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate HTLs. This is the first report on CuSCN films and devices processed via an aqueous‐based synthetic route that is compatible with high‐throughput manufacturing and paves the way for further developments.     

Determination of functional groups of coal-derived liquids by n.m.r. and elemental analysis

A new method of structural analysis was applied to a group of hydroliquefied coal samples. The method uses elemental analysis and n.m.r. data to estimate the concentrations of functional groups in the samples. The samples included oil and asphaltene fractions obtained in a series of hydroliquefaction experiments, and a set of nine fractions separated from a coal-derived oil. The structural characterization of these samples demonstrates that estimates of functional group concentrations can be used to provide detailed structural profiles of complex mixtures and to obtain limited information about reaction pathways.     

An Optimization Approach to Improving Collections of Shape Maps

Finding an informative, structure‐preserving map between two shapes has been a long‐standing problem in geometry processing, involving a variety of solution approaches and applications. However, in many cases, we are given not only two related shapes, but a collection of them, and considering each pairwise map independently does not take full advantage of all existing information. For example, a notorious problem with computing shape maps is the ambiguity introduced by the symmetry problem — for two similar shapes which have reflectional symmetry there exist two maps which are equally favorable, and no intrinsic mapping algorithm can distinguish between them based on these two shapes alone. Another prominent issue with shape mapping algorithms is their relative sensitivity to how “similar” two shapes are — good maps are much easier to obtain when shapes are very similar. Given the context of additional shape maps connecting our collection, we propose to add the constraint of global map consistency, requiring that any composition of maps between two shapes should be independent of the path chosen in the network. This requirement can help us choose among the equally good symmetric alternatives, or help us replace a “bad” pairwise map with the composition of a few “good” maps between shapes that in some sense interpolate the original ones. We show how, given a collection of pairwise shape maps, to define an optimization problem whose output is a set of alternative maps, compositions of those given, which are consistent, and individually at times much better than the original. Our method is general, and can work on any collection of shapes, as long as a seed set of good pairwise maps is provided. We demonstrate the effectiveness of our method for improving maps generated by state‐of‐the‐art mapping methods on various shape databases.     

A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading

The purpose of this article is to present a novel genetic programming trading technique in the task of forecasting the next day returns when trading the EUR/USD exchange rate based on the exchange rates of historical data. Aiming at testing its effectiveness, we benchmark the forecasting performance of our genetic programming implementation with three traditional strategies (naive strategy, MACD, and a buy & hold strategy) plus a hybrid evolutionary artificial neural network approach. The proposed genetic programming technique was found to demonstrate the highest trading performance in terms of annualized return and information ratio when compared to all other strategies which have been used. When more elaborate trading techniques, such as leverage, were combined with the examined models, the genetic programming approach still presented the highest trading performance. To the best of our knowledge, this is the first time that genetic programming is applied in the problem of effectively modeling and trading with the EUR/USD exchange rate. Our application now offers practitioners with an effective and extremely promising set of results when forecasting in the foreign exchange market. The developed genetic programming environment is implemented using the C++ programming language and includes a variation of the genetic programming algorithm with tournament selection.     

Linear-time algorithms for visibility and shortest path problems inside triangulated simple polygons

Given a triangulation of a simple polygonP, we present linear-time algorithms for solving a collection of problems concerning shortest paths and visibility withinP. These problems include calculation of the collection of all shortest paths insideP from a given source vertexS to all the other vertices ofP, calculation of the subpolygon ofP consisting of points that are visible from a given segment withinP, preprocessingP for fast "ray shooting" queries, and several related problems.     

Biofuel support policies in Europe: Lessons learnt for the long way ahead

Biofuel consumption in the EU is growing rapidly but major efforts will need to be undertaken if the EU's objectives for 2010 and beyond are to be achieved. This article analyses the strengths and weaknesses of different biofuel support policies based on the experiences gained in pioneering countries and explores scenarios for their possible impacts in the long-term. It comes to the conclusion that important pre-conditions such as fuel standards and compatibility with engines are in place or being introduced on an EU-wide basis. Current and future policy support therefore focuses on creating favourable economic or legal frameworks to accelerate the market penetration of biofuels. The ambitious targets endorsed in terms of biofuel market shares require the implementation of efficient policy instruments. At the same time, large consumption volumes and the advent of innovative production technologies make it possible for Member States to promote specific types of biofuels, depending on their main objectives and natural potentials. This will require complementary instruments such as subsidies for production facilities, user incentives or feedstock subsidies.     

Investigation into the Use of Hybrid Solutions for ΣΔ A/D Converter Testing

ΣΔ ADCs are now extensively used in electronic system applications requiring high resolution analogue to digital interfaces. Many of these applications require low cost solutions that imply the need for efficient production test strategies for verifying performance specifications. Industrial state-of-the-art is based on DSP testing to extract dynamic performance such as THD and SNR from an FFT on a sampled bit-stream from the decimator output. This method is computationally expensive and as resolution increases, the total number of samples required also increases thus pushing total test time beyond acceptable limits. This paper proposes an alternative hybrid solution based on an initial low-cost wafer level screening test followed by a DSP based technique on marginal devices based on alternative DSP transforms. The screening test is applied to the high-frequency bit-stream output of the ΣΔ modulator and has potential for on-chip implementation. Relatively simple algorithms and cross-correlation techniques are used that can associate specific changes in the bit-stream pattern to key failure modes affecting dynamic performance parameters. A simplified supplementary DSP test for marginal devices is proposed that is less computationally intensive than FFT analysis.     

Community detection by modularity maximization using GRASP with path relinking

Complex systems in diverse areas such as biology, sociology and physics are frequently being modelled as graphs, that provide the mathematical framework upon which small scale dynamics between the fundamental elements of the system can reveal large scale system behavior. Community structure in a graph is an important large scale characteristic, and can be described as a natural division of the vertices into densely connected groups, or clusters. Detection of community structure remains up to this date a computationally challenging problem despite the efforts of many researchers from various scientific fields in the past few years. The modularity value of a set of vertex clusters in a graph is a widely used quality measure for community structure, and the relating problem of finding a partition of the vertices into clusters such that the corresponding modularity is maximized is an NP-Hard problem.     

Nanopatterning of ultrananocrystalline diamond nanowires

We report the fabrication of horizontally aligned ultrananocrystalline diamond (UNCD) nanowires (NWs) via two different approaches. First, with the top-down approach by using electron beam lithography (EBL) and reactive ion etching (RIE) with a photo resist layer as an etch mask. Using this approach, we demonstrate fabrication of 50?μm long UNCD NWs with widths as narrow as 40?nm. We further present an alternative approach to grow UNCD NWs at pre-defined positions through a selective seeding process. No RIE was needed either to etch the NWs or to remove the mask. In this case, we achieved UNCD NWs with lengths of 50?μm and smallest width of 90?nm respectively. Characterization of these nanowires by using scanning electron microscopy (SEM) and atomic force microscopy (AFM) shows that the UNCD NWs are well defined and fully released, with no indication of residual stress. Characterization using visible and ultraviolet (UV) Raman spectroscopy indicates that in both fabrication approaches, UNCD NWs maintain their intrinsic diamond structure.