Computing the homology of groups: The geometric way

In this paper, we present several algorithms related with the computation of the homology of groups, from a geometric perspective (that is to say, carrying out the calculations by means of simplicial sets and using techniques of Algebraic Topology). More concretely, we have developed some algorithms which, making use of the effective homology   method, construct the homology groups of Eilenberg–MacLane spaces K(G,1)$K(G,1)$ for different groups G$G$, allowing one in particular to determine the homology groups of G$G$.     

Acoustic evaluation and adjustment of an open-plan office through architectural design and noise control

Arranging office space into a single open room offers advantages in terms of easy exchange of information and interaction among coworkers, but reduces privacy and acoustic comfort. Thus, the purpose of this work was to evaluate the acoustic quality of a real open-plan office and to propose changes in the room to improve the acoustic conditioning of this office. The computational model of the office under study was calibrated based on RT and STI measurements. Predictions were made of the RT and STI, which generated the radius of distraction r(D), and the rate of spatial decay of sound pressure levels per distance doubling DL(2) in the real conditions of the office and after modifications of the room. The insertion of dividers between work stations and an increase in the ceiling's sound absorption improved the acoustic conditions in the office under study.     

SNR gap approximation for M-PSK-Based bit loading

Adaptive OFDM has the potential of providing bandwidth-efficient communications in hostile propagation environments. Currently, bit loading algorithms use M-ary quadrature amplitude modulation of the OFDM sub-carriers, where the number of bits per symbol modulating each of them is obtained in order to maximize the performance. SNR gap approximation for M-QAM signaling makes the algorithms simpler to implement. However, in some circumstances it may be preferable to use. M-ary phase shift keying. In this letter an approximation is derived for M-PSK similar to the SNR gap of M-QAM so that bit loading algorithms can be extended to this type of modulation. In addition, the performance obtained when using M-PSK is compared to that of M-QAM in a practical situation.     

Pollution Buildup on Road Surfaces

Samples of sediment found on an urban road in Aberdeen (Scotland) were collected by washing designated surfaces. This method, called the “wet” method, was capable of collecting sediment of the smallest particle size range that is normally left behind by traditional sampling techniques using dry vacuuming. Over 17 months (mainly on a weekly basis), 66 samples were collected and analyzed for sediment loading, particle size distribution, concentration of heavy metals (Zn, Cu, Pb, Cd) in four sediment size fractions, and several dissolved pollutants in the effluent collected from washing (NO3?,?SO42?,?PO43?,?Cl?,?F?,?NH4+, total organic carbon, total carbon). Standard statistical methods, including multiple regressions, were used to determine relationships amongst different sediment characteristics. It was found that sediment loading, as well as concentrations of Cl? and SO42?, were highest in the winter months, especially when snow was present on the road surface. It was observed that 66% of total road sediment loading was found within a 0.5 m strip next to the curb. The average sediment particle sizes found were smaller than those previously recorded in the literature. As expected, the concentrations of heavy metals were highest in the smallest particle size fraction analyzed (<63?μm), and this occurred during the summer months when less sediment was available on the street surface. The antecedent dry weather period had a very weak and negative influence on the loading rate of the smallest particle size fraction next to the curb.     

Influence diagnostics in regression with complex designs through conditional bias

One of the areas of Statistics in which the influence analysis has been widely studied is the multiple linear regression model. Nevertheless, the influence diagnostics proposed in this context cannot be applied to regression in complex survey, under randomized inference, since the i.i.d. case does not incorporate any probability weighting or population structure, such as clustering, stratification or measures of size into the analysis. In this paper we introduce some influence diagnostics in regression in complex survey. They are built on the conditional bias concept (Moreno-Rebollo et al., 1999). We emphasize the similarities and differences of the proposed measures with respect to the existing ones for the i.i.d. case. Research partly supported by the Spanish MCYT grant BFM2001-3844 and Spanish MEC grant MTM2004-0111433.     

ZRC – A Refinement Calculus for Z

The fact that Z is a specification language only, with no associated program development method, is a widely recognised problem. As an answer to that, we present ZRC, a refinement calculus based on Morgan's work that incorporates the Z notation and follows its style and conventions. This work builds upon existing refinement techniques for Z, but distinguishes itself mainly in that ZRC is completely formalised. In this paper, we explain how programs can be derived from Z specifications using ZRC. We present ZRC-L, the language of our calculus, and its conversion laws, which are concerned with the transformation of Z schemas into programs of this language. Moreover, we present the weakest precondition semantics of ZRC-L, which is the basis for the derivation of the laws of ZRC. More than a refinement calculus, ZRC is a theory of refinement for Z. Received July 1997 / Accepted in revised form October 1998     

Forecasting with unequally spaced data by a functional principal component approach

The Principal Component Regression model of multiple responses is extended to forccast a continuous-time stochastic process. Orthogonal projection on a subspace of trigonometric functions is applied in order to estimate the principal components using discrete-time observations from a sample of regular curves. The forecasts provided by this approach are compared with classical principal component regression on simulated data.     

Roles of the Unsaturated Fatty Acid Docosahexaenoic Acid in the Central Nervous System: Molecular and Cellular Insights

Fatty acids (FAs) are essential components of the central nervous system (CNS), where they exert multiple roles in health and disease. Among the FAs, docosahexaenoic acid (DHA) has been widely recognized as a key molecule for neuronal function and cell signaling. Despite its relevance, the molecular pathways underlying the beneficial effects of DHA on the cells of the CNS are still unclear. Here, we summarize and discuss the molecular mechanisms underlying the actions of DHA in neural cells with a special focus on processes of survival, morphological development, and synaptic maturation. In addition, we examine the evidence supporting a potential therapeutic role of DHA against CNS tumor diseases and tumorigenesis. The current results suggest that DHA exerts its actions on neural cells mainly through the modulation of signaling cascades involving the activation of diverse types of receptors. In addition, we found evidence connecting brain DHA and ω-3 PUFA levels with CNS diseases, such as depression, autism spectrum disorders, obesity, and neurodegenerative diseases. In the context of cancer, the existing data have shown that DHA exerts positive actions as a coadjuvant in antitumoral therapy. Although many questions in the field remain only partially resolved, we hope that future research may soon define specific pathways and receptor systems involved in the beneficial effects of DHA in cells of the CNS, opening new avenues for innovative therapeutic strategies for CNS diseases.     

An approach to accidents modeling based on compounds road environments

The most common approach to study the influence of certain road features on accidents has been the consideration of uniform road segments characterized by a unique feature. However, when an accident is related to the road infrastructure, its cause is usually not a single characteristic but rather a complex combination of several characteristics. The main objective of this paper is to describe a methodology developed in order to consider the road as a complete environment by using compound road environments, overcoming the limitations inherented in considering only uniform road segments. The methodology consists of: dividing a sample of roads into segments; grouping them into quite homogeneous road environments using cluster analysis; and identifying the influence of skid resistance and texture depth on road accidents in each environment by using generalized linear models. The application of this methodology is demonstrated for eight roads. Based on real data from accidents and road characteristics, three compound road environments were established where the pavement surface properties significantly influence the occurrence of accidents. Results have showed clearly that road environments where braking maneuvers are more common or those with small radii of curvature and high speeds require higher skid resistance and texture depth as an important contribution to the accident prevention.