The GPU on the simulation of cellular computing models

Membrane Computing is a discipline aiming to abstract formal computing models, called membrane systems or P systems, from the structure and functioning of the living cells as well as from the cooperation of cells in tissues, organs, and other higher order structures. This framework provides polynomial time solutions to NP-complete problems by trading space for time, and whose efficient simulation poses challenges in three different aspects: an intrinsic massively parallelism of P systems, an exponential computational workspace, and a non-intensive floating point nature. In this paper, we analyze the simulation of a family of recognizer P systems with active membranes that solves the Satisfiability problem in linear time on different instances of Graphics Processing Units (GPUs). For an efficient handling of the exponential workspace created by the P systems computation, we enable different data policies to increase memory bandwidth and exploit data locality through tiling and dynamic queues. Parallelism inherent to the target P system is also managed to demonstrate that GPUs offer a valid alternative for high-performance computing at a considerably lower cost. Furthermore, scalability is demonstrated on the way to the largest problem size we were able to run, and considering the new hardware generation from Nvidia, Fermi, for a total speed-up exceeding four orders of magnitude when running our simulations on the Tesla S2050 server.     

Luminescent Amphiphilic Aminoglycoside Probes to Study Transfection

We report the characterization of amphiphilic aminoglycoside conjugates containing luminophores with aggregation-induced emission properties as transfection reagents. These inherently luminescent transfection vectors are capable of binding plasmid DNA through electrostatic interactions; this binding results in an emission “on” signal due to restriction of intramolecular motion of the luminophore core. The luminescent cationic amphiphiles effectively transferred plasmid DNA into mammalian cells (HeLa, HEK 293T), as proven by expression of a red fluorescent protein marker. The morphologies of the aggregates were investigated by microscopy as well as ζ-potential and dynamic light-scattering measurements. The transfection efficiencies using luminescent cationic amphiphiles were similar to that of the gold-standard transfection reagent Lipofectamine® 2000.     

Comparative evaluation of platforms for parallel Ant Colony Optimization

The rapidly growing field of nature-inspired computing concerns the development and application of algorithms and methods based on biological or physical principles. This approach is particularly compelling for practitioners in high-performance computing, as natural algorithms are often inherently parallel in nature (for example, they may be based on a “swarm”-like model that uses a population of agents to optimize a function). Coupled with rising interest in nature-based algorithms is the growth in heterogenous computing; systems that use more than one kind of processor. We are therefore interested in the performance characteristics of nature-inspired algorithms on a number of different platforms. To this end, we present a new OpenCL-based implementation of the Ant Colony Optimization algorithm, and use it as the basis of extensive experimental tests. We benchmark the algorithm against existing implementations, on a wide variety of hardware platforms, and offer extensive analysis. This work provides rigorous foundations for future investigations of Ant Colony Optimization on high-performance platforms.     

Solubility of gases in a hydroformylation solvent

The solubility of reactants used in homogeneously catalyzed hydroformylation of alkene was studied in a pressurized, semi-batch autoclave. The solubilities of alkene, carbon monoxide and hydrogen in the 2,2,4-trimethyl-1,3-pentanediol mono(2-methylpropanoate) solvent (NX 795) were determined by precise pressure and weight measurements. The measured solubilities were tested against empirical and theoretical models. Logarithmic empirical models gave a very good fit to experimental solubility data. To obtain nonempirical equations for the gas solubility, thermodynamic models based on activity coefficients and equations of state were used. The activity coefficient models based on regular solution theory (RST) predicted the solubilities well, but the selection of the model is the critical issue. For 1-butene, the best prediction was provided by the Yen and McKetta modification of the RST, whereas for carbon monoxide and hydrogen it was necessary to include a mixing entropy term in the RST. The Soave-Redlich-Kwong equation of state gave a fairly good prediction of the solubilities, but not as good as the RST-based models.     

A study about the effect of the temperature of hydrogen treatment on the properties of Ru/Al2O3 and Ru/C and their catalytic behavior during 1-heptyne semi-hydrogenation

The 1-heptyne selective hydrogenation carried out at 150 kPa, and at 283 and 303 K using Ru/Al₂O₃ and Ru/C as catalysts, was studied. Catalysts were prepared by the incipient wetness impregnation technique using RuCl₃ as precursor. Ru/Al₂O₃ was treated in hydrogen at 373 or 573 K and Ru/C only at the last temperature. Catalysts were characterized by hydrogen chemisorption, TPR and XPS. Ru dispersion after treatment in hydrogen at the highest temperature is similar for both catalysts. Ru is present as Ru⁰ in Ru/C, while Ru⁰ and Ru electron-deficient species are present on the catalysts surface after hydrogen treatment at the two temperatures using Al₂O₃ as support. The best catalytic behavior was observed for the highest temperature of hydrogen treatment and for 303 K reaction temperature. As a consequence of a shape selectivity effect of the C support, the best conversion is obtained with the alumina supported catalyst.     

A bio-inspired predictive sensory-motor coordination scheme for robot reaching and preshaping

This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image. The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.     

Cyclic Nanoindentation for Local High Cycle Fatigue Investigations: A Methodological Approach Accounting for Thermal Drift

Cyclic nanoindentation allows characterizing the influence of single phases and their interactions on fatigue mechanisms. Herein, a method for high cycle fatigue testing by nanoindentation is presented. By combining high- and low-frequency indentation modes, high cycle numbers are achieved while obtaining sufficient data points to reconstruct force–displacement hysteresis loops. A challenge is the stochastic course of thermal drift which is addressed by measuring drift rate in regular low-force holding segments. Drift rates are used to correct the displacement values, yielding reproducible cyclic deformation data as it is shown for two very different materials, a ductile metal and a brittle ceramic.     

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement.     

Input-output properties of the Page-Hinkley detector

We consider the stochastic input-output properties of a simple non-linear dynamical system, the so-called Page-Hinkley detector, playing a key role in change detection, and also in queuing theory. We show that for L-mixing inputs with negative expectation the output process of this system is L-mixing. The result is applied to get an upper bound for the false alarm rate. The proof is then adapted to get a similar result for the case of random i.i.d. inputs. Possible extensions and open problems are given in the discussion.