Supply Estimation Using Coevolutionary Genetic Algorithms in the Spanish Electrical Market

The price of electrical energy in Spain has not been regulated by the government since 1998, but determined by the supply from the generators in a competitive market, the so-called electrical pool. A genetic method for analyzing data from this new market is presented in this paper. The eventual objective is to determine the individual supply curves of the competitive agents. Adopting the point of view of the game theory, different genetic algorithm configurations using coevolutionary and non-coevolutionary strategies combined with scalar and multi-objective fitness are compared. The results obtained are the first step toward solving the induction of the optimal individual strategies into the Spanish electrical market from data in terms of perfect oligopolistic behavior.     

Temporal transformation of periodic incoherent ultrashort light pulses by chirped fiber gratings

The analogy between free-space propagation of optical beams and light-pulse reflection from linearly chirped fiber gratings is used to analyze the Lau effect in the temporal domain. The coherence conditions that are satisfied in the spatial domain for obtaining, at certain fixed locations, periodic fringes patterns are reformulated for guided light propagation. In this analogy, spatial periodic irradiance distributions are transformed in periodic sequences of light pulses. An optical setup is proposed to produce sharp pulse trains, with minimal distortion effects, that have repetition frequencies that are different from those associated with the input periodic optical signal. Some numerical results are given to illustrate this approach.     

New learning rules for the ASSOM network

The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonens method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one.     

AC-coupled front-end for biopotential measurements

AC coupling is essential in biopotential measurements. Electrode offset potentials can be several orders of magnitude larger than the amplitudes of the biological signals of interest, thus limiting the admissible gain of a dc-coupled front end to prevent amplifier saturation. A high-gain input stage needs ac input coupling. This can be achieved by series capacitors, but in order to provide a bias path, grounded resistors are usually included, which degrade the common mode rejection ratio (CMRR). This paper proposes a novel balanced input ac-coupling network that provides a bias path without any connection to ground, thus resulting in a high CMRR. The circuit being passive, it does not limit the differential dc input voltage. Furthermore, differential signals are ac coupled, whereas common-mode voltages are dc coupled, thus allowing the closed-loop control of the dc common mode voltage by means of a driven-right-leg circuit. This makes the circuit compatible with common-mode dc shifting strategies intended for single-supply biopotential amplifiers. The proposed circuit allows the implementation of high-gain biopotential amplifiers with a reduced number of parts, thus resulting in low power consumption. An electrocardiogram amplifier built according to the proposed design achieves a CMRR of 123 dB at 50 Hz.     

Identification of protein ligands in complex biological samples using intensity-fading MALDI-TOF mass spectrometry

The easy detection of biomolecular interactions in complex mixtures using a minimum amount of material is of prime interest in molecular and cellular biology research. In this work, a mass spectrometry MALDI-TOF based approach, which we call intensity-fading (IF MALDI-TOFMS), and which was designed for just such a purpose, is reported. This methodology is based on the use of the MALDI ion intensities to detect quickly the formation of complexes between nonimmobilized biomolecules in which a protein is one of the partners (protein-protein, protein-peptide, protein-organic molecule, and protein-nucleic acid complexes). The complex is detected through the decrease (fading) of the molecular ion intensities of the partners as directly compared to the MALDI mass spectrum of the mixture (problem and control molecules) following the addition of the target molecule. The potential of the approach is examined in several examples of model interactions, mainly involving small nonprotein and protein inhibitors of proteases, at both the qualitative and semiquantitative levels. Using this method, different protein ligands of proteolytic enzymes in total extracts of invertebrate organisms have been identified in a simple way. The proposed procedure should be easily applied to the high-throughput screening of biomolecules, opening a new experimental strategy in functional proteomics.     

Structures and stabilities of small DNA dumbbells with Watson-Crick and Hoogsteen base pairs

The structures and stabilities of cyclic DNA octamers of different sequences have been studied by NMR and CD spectroscopy and by restrained molecular dynamics. At low oligonucleotide concentrations, some of these molecules form stable monomeric structures consisting of a short stem of two base pairs connected by two mini-loops of two residues. To our knowledge, these dumbbell-like structures are the smallest observed to date. The relative stabilities of these cyclic dumbbells have been established by studying their melting transitions. Dumbbells made up purely of GC stems are more stable than those consisting purely of AT base pairs. The order of the base pairs closing the loops also has an important effect on the stabilities of these structures. The NMR data indicate that there are significant differences between the solution structures of dumbbells with G-C base pairs in the stem compared to those with A-T base pairs. In the case of dumbbells with G-C base pairs, the residues in the stem form a short segment of a BDNA helix stabilized by two Watson-Crick base pairs. In contrast, in the case of d, the stem is formed by two A-T base pairs with the glycosidic angles of the adenine bases in a syn conformation, most probably forming Hoogsteen base pairs. Although the conformations of the loop residues are not very well defined, the thymine residues at the first position of the loop are observed to fold back into the minor groove of the stem.     

Language Simplification through Error-Correcting and Grammatical Inference Techniques

In many language processing tasks, most of the sentences generally convey rather simple meanings. Moreover, these tasks have a limited semantic domain that can be properly covered with a simple lexicon and a restricted syntax. Nevertheless, casual users are by no means expected to comply with any kind of formal syntactic restrictions due to the inherent spontaneous nature of human language. In this work, the use of error-correcting-based learning techniques is proposed to cope with the complex syntactic variability which is generally exhibited by natural language. In our approach, a complex task is modeled in terms of a basic finite state model, F, and a stochastic error model, E. F should account for the basic (syntactic) structures underlying this task, which would convey the meaning. E should account for general vocabulary variations, word disappearance, superfluous words, and so on. Each natural user sentence is thus considered as a corrupted version (according to E) of some simple sentence of L(F). Adequate bootstrapping procedures are presented that incrementally improve the structure of F while estimating the probabilities for the operations of E. These techniques have been applied to a practical task of moderately high syntactic variability, and the results which show the potential of the proposed approach are presented.     

MR imaging of baker cysts—prevalence and relation to internal derangements of the knee

Rationale and objectives: To evaluate the prevalence of popliteal cysts in patients studied with magnetic resonance (MR) imaging. Cyst presence and volume will be related to other internal derangement of the knee.Materials and methods: Three hundred and eighty-two consecutive patients with a MR study of the knee were included. Images were obtained in the three spatial orthogonal planes and evaluated through consensus. Four categories were established for the Baker cyst and synovial fluid (absence, minimum, moderate and massive), and the presence of meniscal, cruciate ligaments and cartilage lesions were recorded. Statistical analysis was carried out with bivariate analysis (χ² of Pearson and Gamma tests).Results: From the 382 patients, 145 had Baker cysts (38.0%). Cyst content was minimum in 99, moderate in 34 and massive in 12. Joint effusion was observed in 269 patients (70.4%), being minimal in 140 patients, moderate in 119 and massive in ten. Meniscal lesions were observed in 195 patients (51%), while 58 patients (15%) had a cruciate ligament lesion. Baker cyst has a statistically significant direct relationship with the presence and quantity of synovial fluid (P=0.002) and with the presence and type of meniscal lesion (P=0.01) but not with cruciate ligaments or cartilage lesions.Conclusions: The prevalence of Baker cysts in MR studies of the knee is high. Its presence and volume are related to the quantity of synovial fluid, and to the presence and severity of meniscal lesions.     

Learning finite-state models for machine translation

In formal language theory, finite-state transducers are well-know models for simple “input-output” mappings between two languages. Even if more powerful, recursive models can be used to account for more complex mappings, it has been argued that the input-output relations underlying most usual natural language pairs can essentially be modeled by finite-state devices. Moreover, the relative simplicity of these mappings has recently led to the development of techniques for learning finite-state transducers from a training set of input-output sentence pairs of the languages considered. In the last years, these techniques have lead to the development of a number of machine translation systems. Under the statistical statement of machine translation, we overview here how modeling, learning and search problems can be solved by using stochastic finite-state transducers. We also review the results achieved by the systems we have developed under this paradigm. As a main conclusion of this review we argue that, as task complexity and training data scarcity increase, those systems which rely more on statistical techniques tend produce the best results. This work was partially supported by the European Union project TT2 (IST-2001-32091) and by the Spanish project ITEFTE (TIC 2003-08681-C02-02). Editor: Georgios Paliouras and Yasubumi Sakakibara     

Group decision-making model with incomplete fuzzy preference relations based on additive consistency.

In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, i.e., some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations. As part of this decision model, we propose an iterative procedure to estimate the missing information in an expert's incomplete fuzzy preference relation. This procedure is guided by the additive-consistency (AC) property and only uses the preference values the expert provides. The AC property is also used to measure the level of consistency of the information provided by the experts and also to propose a new induced ordered weighted averaging (IOWA) operator, the AC-IOWA operator, which permits the aggregation of the experts' preferences in such a way that more importance is given to the most consistent ones. Finally, the selection of the solution set of alternatives according to the fuzzy majority of the experts is based on two quantifier-guided choice degrees: the dominance and the nondominance degree.