A Supply Function Competition Model for the Spanish Wholesale Electricity Market

We model the Spanish wholesale market as a multiplant linear supply function competition model. According to the theory, the larger generators should have supply curves for each plant which are to the left of the supply curves of plants owned by smaller generators. We test this prediction for fuel plants using data from the Spanish Market Operator (OMEL) from May 2001 to December 2003. Our results indicate that the prediction of the model holds.

Does the Term Spread play a role in the FED's reaction function? An Empirical Investigation

Using US data for the period 1967:5-2002:4, this paper empirically investigates the performance of a Fed’s reaction function (FRF) that (i) allows for the presence of switching regimes, (ii) considers the long-short term spread in addition to the typical variables, (iii) uses an alternative monthly indicator of general economic activity suggested by Stock and Watson (1999), and (iv) considers interest rate smoothing. The estimation results show the existence of three switching regimes, two characterized by low volatility and the remaining regime by high volatility. Moreover, the scale of the responses of the Federal funds rate to movements in the rate of inflation and the economic activity index depends on the regime. The estimation results also show robust empirical evidence that the importance of the term spread in the FRF has increased over the sample period and the FRF has been more stable during the term of office of Chairman Greenspan than in the pre-Greenspan period.

The role of the term spread in an augmented Taylor rule: An empirical investigation

Using US data for the period 1967:5-2002:4, this paper empirically investigates the performance of an augmented version of the Taylor rule (ATR) that (i) allows for the presence of switching regimes, (ii) considers the long-short term spread in addition to the typical variables, (iii) uses an alternative monthly indicator of general economic activity suggested by Stock and Watson (1999), and (iv) considers interest rate smoothing. The estimation results show the existence of switching regimes, one characterized by low volatility and the other by high volatility. Moreover, the scale of the responses of the Federal funds rate to movements in the term spread, inflation and the economic activity index depend on the regime. The estimation results also show robust empirical evidence that the ATR has been more stable during the term of office of Chairman Greenspan than in the pre-Greenspan period. However, a closer look at the Greenspan period shows the existence of two alternative regimes and that the response of the Fed funds rate to inflation has not been significant during this period once the term spread is considered.

Scenario Cluster Lagrangian Decomposition in two stage stochastic mixed 0-1 optimization

In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem.

Kv7 Channels Can Function without Constitutive Calmodulin Tethering

9 p.

Regulation of Energy Metabolism by the Extracytoplasmic Function (ECF) σ Factors of Arcobacter butzleri

11 p.

Role of G protein coupled inwardly rectifier K+ channels (GIRK) on the neurobiology depression

353 págs.

Augmented Peter Pan: un cuento multilingüe con Realidad Aumentada

[SPA] El objetivo de este Trabajo de Fin de Grado ha sido diseñar un cuento multilingüe que incorpora una innovación tecnológica, la Realidad Aumentada, para favorecer el proceso de enseñanza-aprendizaje lingüístico en la escuela. Para ello, hemos analizado algunos cuentos con Realidad Aumentada que se pueden encontrar en el mercado. Al no encontrar ningún cuento que cumpliese nuestras expectativas, hemos creado un cuento multilingüe con Realidad Aumentada adaptando el cuento clásico “Peter Pan”, que cumple los objetivos de fomentar el aprendizaje de diferentes idiomas y el gusto por la lectura, valorar las lenguas inmigrantes en las aulas y promover el uso de las TIC.

Climate change impacts on the water services in Costa Rica: a production function for the hydroenergy sector

23 p.

A novel brain partition highlights the modular skeleton shared by structure and function

Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects. Our novel approach allows us to find a common skeleton shared by structure and function from which a new, optimal, brain partition can be extracted. We describe the emerging common structure-function modules (SFMs) in detail and compare them with commonly employed anatomical or functional parcellations. Our results underline the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.

Structure-function analysis of USP1: insights into the role of Ser313 phosphorylation site and the effect of cancer-associated mutations on autocleavage

Background: In complex with its cofactor UAF1, the USP1 deubiquitinase plays an important role in cellular processes related to cancer, including the response to DNA damage. The USP1/UAF1 complex is emerging as a novel target in cancer therapy, but several aspects of its function and regulation remain to be further clarified. These include the role of the serine 313 phosphorylation site, the relative contribution of different USP1 sequence motifs to UAF1 binding, and the potential effect of cancer-associated mutations on USP1 regulation by autocleavage. Methods: We have generated a large set of USP1 structural variants, including a catalytically inactive form (C90S), non-phosphorylatable (S313A) and phosphomimetic (S313D) mutants, deletion mutants lacking potential UAF1 binding sites, a mutant (GG/AA) unable to undergo autocleavage at the well-characterized G670/G671 diglycine motif, and four USP1 mutants identified in tumor samples that cluster around this cleavage site (G667A, L669P, K673T and A676T). Using cell-based assays, we have determined the ability of these mutants to bind UAF1, to reverse DNA damage-induced monoubiquitination of PCNA, and to undergo autocleavage. Results: A non-phosphorylatable S313A mutant of USP1 retained the ability to bind UAF1 and to reverse PCNA ubiquitination in cell-based assays. Regardless of the presence of a phosphomimetic S313D mutation, deletion of USP1 fragment 420-520 disrupted UAF1 binding, as determined using a nuclear relocation assay. The UAF1 binding site in a second UAF1-interacting DUB, USP46, was mapped to a region homologous to USP1(420-520). Regarding USP1 autocleavage, co-expression of the C90S and GG/AA mutants did not result in cleavage, while the cancer-associated mutation L669P was found to reduce cleavage efficiency. Conclusions: USP1 phosphorylation at S313 is not critical for PCNA deubiquitination, neither for binding to UAF1 in a cellular environment. In this context, USP1 amino acid motif 420-520 is necessary and sufficient for UAF1 binding. This motif, and a homologous amino acid segment that mediates USP46 binding to UAF1, map to the Fingers sub-domain of these DUBs. On the other hand, our results support the view that USP1 autocleavage may occur in cis, and can be altered by a cancer-associated mutation.