Formal verification of a generic framework to synthesize SAT-provers

We present in this paper an application of the ACL2 system to generate and reason about propositional satisfiability provers. For that purpose, we develop a framework in which we define a generic S AT-prover based on transformation rules, and we formalize this generic framework in the ACL2 logic, carrying out a formal proof of its termination, soundness, and completeness. This generic framework can be instantiated to obtain a number of verified and executable SAT-provers in ACL2, and this instantiation can be done in an automated way. Three instantiations of the generic framework are considered: semantic tableaux, sequent calculus, and Davis-Putnam-Logeman-Loveland methods.     

A new model for time-series forecasting using radial basis functions and exogenous data

In this paper, we present a new model for time-series forecasting using radial basis functions (RBFs) as a unit of artificial neural networks (ANNs), which allows the inclusion of exogenous information (EI) without additional pre-processing. We begin by summarizing the most well-known EI techniques used ad hoc, i.e., principal component analysis (PCA) and independent component analysis (ICA). We analyze the advantages and disadvantages of these techniques in time-series forecasting using Spanish bank and company stocks. Then, we describe a new hybrid model for time-series forecasting which combines ANNs with genetic algorithms (GAs). We also describe the possibilities when implementing the model on parallel processing systems.

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DJ el Niño: expressing synthetic emotions with music

The purpose of this work is twofold: (1) to present an artistic experiment on how to use artificial intelligence to develop a different kind of DJ, and (2) to test a cognitive model on how music expresses emotions. Based on a former model conceived by the author, electronic music loops were tagged according to the type and intensity of the expressed emotion. Then, using a feedback model, an artificial personality was arranged, which was affected by the music and played the loops depending on the emotional state the artificial personality was in. The efficiency and credibility of the model was tested in a live event during the Z2000 art festival in Berlin, with encouraging results.

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Atmospheric dry deposition of persistent organic pollutants to the Atlantic and inferences for the global oceans

Atmospheric deposition to the oceans is a key process affecting the global dynamics and sinks of persistent organic pollutants (POPs). A new methodology that combines aerosol remote sensing measurements with measured POP aerosol-phase concentrations is presented to derive dry particulate depositional fluxes of POPs to the oceans. These fluxes are compared with those due to diffusive air-water exchange. For all polychlorinated biphenyl (PCB) congeners and lower chlorinated dibenzo-p-dioxins and furans (PCDD/Fs), air-water exchange dominates the dry deposition mechanism. However, this tendency reverses in some areas, such as in marine aerosol influenced areas and dust outflow regions, consistent with the important variability encountered for the depositional fluxes. Seasonal variability is mainly found in mid-high latitudes, due to the important influence of wind speed enhancing dry deposition fluxes and temperature as a driver of the gas-particle partitioning of POPs. The average dry aerosol deposition flux of sigmaPCBs and sigmaPCDD/Fs to the Atlantic Ocean is calculated to be in the order of 66 ng m(-2) yr(-1) and 9 ng m(-2)yr(-1) respectively. The total dry aerosol deposition of sigmaPCBs and sigmaPCDD/Fs to the Atlantic Ocean is estimated to be 2200 kg yr(-1) and 500 kg yr(-1), respectively, while the net air-water exchange is higher, 22000 kg sigmaPCBs yr(-1) for PCBs and 1300 kg sigmaPCDD/Fs yr(-1). Furthermore, it is suggested that marine aerosol plays an important role in scavenging atmospheric contaminants.     

Fate and transport of testosterone in agricultural soils

Hormones excreted in animal waste have been measured in surface and groundwater associated with manure that is applied to the land surface. Limited studies have been done on the fate and transport of androgenic hormones in soils. In this study, batch and column experiments were used to identify the fate and transport of radiolabeled [14C] testosterone in agricultural soils. The batch results indicated that aqueous-phase concentrations decreased for the first 5 h and then appeared to increase through time. The first-order sorption kinetics ranged from 0.08 to 0.640 h(-1) for the first 5 h. Beyond 5 h the increase in aqueous 14C could have been caused by desorption of testosterone back into the aqueous phase. However, metabolites were also produced beyond 5 h and would have likely resulted in the increase in aqueous 14C by sorption site competition and/or by lower sorption affinity. There were weak correlations of sorption with soil particle size, organic matter, and specific surface area. Testosterone was the dominant compound present in the soil column effluents, and a fully kinetic-sorption, chemical nonequilibrium model was used to describe the data. Column experiment sorption estimates were lower than the batch, which resulted from rate-limiting sorption due to the advective transport. The column degradation coefficients (0.404-0.600 h(-1)) were generally higher than values reported in the literature for 17beta-estradiol. Although it was found that testosterone degraded more readily than 17beta-estradiol, it appeared to have a greater potential to migrate in the soil because it was not as strongly sorbed. This study underlined the importance of the simultaneous transformation and sorption processes in the transport of hormones through soils.     

A CAVE-like environment as a tool for full-size train design

Simulations of models, in all different areas, is an expanding, attractive line of work. More and more applications are taking advantage of the improvements in technology and knowledge in this field, thus achieving results that would have been impossible to achieve with a real model, or foreseeing facts that otherwise would have been encountered too late in the production process. The rail industry is one possible beneficiary of this approach. Usually, before commencing the fabrication process of a new train, the construction of a full-size model is mandatory. Instead of building this full-size real model, which leaves little room for later, last-minute modifications, a virtual model can be built in the digital realm, thus offering a new platform for easier interaction with it. In this article, a simulation of a train is presented in order to tackle visual, aesthetic and ergonomic issues. The simulation runs on a PC-based CAVE-like architecture, offering a certain degree of interaction to the user, and combines static and dynamic computer-generated imagery, both with and without stereoscopy for 3D visualisation, as well as augmented virtuality techniques for the integration of the train with its environment.     

Physico-chemical studies of polymeric carriers

Summary Fractionation procedure on copolymer of N-vinylpyrrolidone and maleic anhydride has been elaborated using water/acetone solvent/nonsolvent system. Viscosity and molecular weight measurements of the fractions were carried out in acidic aqueous medium at 25 °C. The relationship between the intrinsic viscosity and the molecular mass []_c = K·M was obtained at pH = 2.1.     

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.     

A regression model describing the effect of pH,NaCl and temperature on<Emphasis Type="Italic"> D</Emphasis> values of<Emphasis Type="Italic"> Bacillus stearothermophilus</Emphasis> spores

A new microbiological predictive model was developed, relating pH, NaCl concentration and temperature (T) with the decimal reduction coefficient D as dependent variable, using data previously reported by [1]. The model was obtained by multiple linear regression using only three predictor variables (T ², pH² and NaCl), easily computed from the basic variables pH, NaCl concentration and temperature (T). The fitted model is robust, stable and satisfies all the basic hypotheses of the regression models. It also provides simplicity when compared with previously published models based on multiple linear regression tools.     

Microbiological and biochemical changes in minimally processed fresh-cut Conference pears

Fresh-cut Conference pears were packaged under different modified atmosphere packaging (MAP) conditions and stored in refrigeration. The effects of packaging atmospheres on the microbial viability as well as on the changes in acidity, sugars and ascorbic acid (AA) were studied throughout storage. The use of plastic bags of a permeability of 15 cm ³ O ₂ m ^-2bar ^-1 24 h ^-1 and initial atmospheres of 0 kPa O ₂ extended the microbiological shelf life of pear cubes for at least 3 weeks of storage. Under these conditions, AA levels were kept almost unvaried throughout storage due to the restrictions in the O ₂ availability inside the bags' headspaces. However, these conditions also triggered physiological and biochemical changes that caused greater changes in the product acidity. The sugar profile was also affected by storage, but MAP did not show any significant influence on the reported changes.