A Ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool.

Image segmentation based on multi-scan constraint satisfaction neural network

A novel image segmentation method based on a constraint satisfaction neural network (CSNN) is presented. The new method uses CSNN-based relaxation but with a modified scanning scheme of the image. The pixels are visited with more distant intervals and wider neighborhoods in the first level of the algorithm. The intervals between pixels and their neighborhoods are reduced in the following stages of the algorithm. This method contributes to the formation of more regular segments rapidly and consistently. A cluster validity index to determine the number of segments is also added to complete the proposed method into a fully automatic unsupervised segmentation scheme. The results are compared quantitatively by means of a novel segmentation evaluation criterion. The results are promising.

Constraint rule-based programming of norms for electronic institutions

Norms constitute a powerful coordination mechanism among heterogeneous agents. In this paper, we propose a rule language to specify and explicitly manage the normative positions of agents (permissions, prohibitions and obligations), with which distinct deontic notions and their relationships can be captured. Our rule-based formalism includes constraints for more expressiveness and precision and allows to supplement (and implement) electronic institutions with norms. We also show how some normative aspects are given computational interpretation. © 2008 Springer Science+Business Media, LLC.

TLM2.0 based timing accurate modeling method for complex NoC systems

Scalability and efficiency of on-chip communication of emerging Multiprocessor System-on-Chip (MPSoC) are critical design considerations. Conventional bus based interconnection schemes no longer fit for MPSoC with a large number of cores. Networks-on-Chip (NoC) is widely accepted as the next generation interconnection scheme for large scale MPSoC. The increase of MPSoC complexity requires fast and accurate system-level modeling techniques for rapid modeling and veri-fication of emerging MPSoCs. However, the existing modeling methods are limited in delivering the essentials of timing accuracy and simulation speed. This paper proposes a novel system-level Networks-on-Chip (NoC) modeling method, which is based on SystemC and TLM2.0 and capable of delivering timing accuracy close to cycle accurate modeling techniques at a significantly lower simulation cost. Experimental results are presented to demonstrate the proposed method. ©2010 IEEE.

Structure and thermal properties of salicylate-based-protic ionic liquids as new heat storage media. COSMO-RS structure characterization and modeling of heat capacities

During this research, we present a study on the thermal properties, such as the melting, cold crystallization, and glass transition temperatures as well as heat capacities from 293.15 K to 323.15 K of nine in-house synthesized protic ionic liquids based on the 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate ([H-Im-C₁OC_n][Sal]) with n = 3–11. The 3D structures, surface charge distributions and COSMO volumes of all investigated ions are obtained by combining DFT calculations and the COSMO-RS methodology. The heat capacity data sets as a function of temperature of the 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate are then predicted using the methodology originally proposed in the case of ionic liquids by Ge et al. 3-(Alkoxymethyl)-1H-imidazol-3-ium salicylate based ionic liquids present specific heat capacities higher in many cases than other ionic liquids that make them suitable as heat storage media and in heat transfer processes. It was found experimentally that the heat capacity increases linearly with increasing alkyl chain length of the alkoxymethyl group of 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate as was expected and predicted using the Ge et al. method with an overall relative absolute deviation close to 3.2% for temperatures up to 323.15 K.

Building information modeling based building design optimization for sustainability

Environmental problems, especially climate change, have become a serious global issue waiting for people to solve. In the construction industry, the concept of sustainable building is developing to reduce greenhouse gas emissions. In this study, a building information modeling (BIM) based building design optimization method is proposed to facilitate designers to optimize their designs and improve buildings’ sustainability. A revised particle swarm optimization (PSO) algorithm is applied to search for the trade-off between life cycle costs (LCC) and life cycle carbon emissions (LCCE) of building designs. In order tovalidate the effectiveness and efficiency of this method, a case study of an office building is conducted in Hong Kong. The result of the case study shows that this method can enlarge the searching space for optimal design solutions and shorten the processing time for optimal design results, which is really helpful for designers to deliver an economic and environmental friendly design scheme.

Differential Scanning Calorimetry Study and Computer Modeling of b a Phase Transformation in Ti-6Al-4V Alloy

The relationship between heat-treatment parameters and microstructure in titanium alloys has so far been mainly studied empirically, using characterization techniques such as microscopy. Calculation and modeling of the kinetics of phase transformation have not yet been widely used for these alloys. Differential scanning calorimetry (DSC) has been widely used for the study of a variety of phase transformations. There has been much work done on the calculation and modeling of the kinetics of phase transformations for different systems based on the results from DSC study. In the present work, the kinetics of the transformation in a Ti-6Al-4V titanium alloy were studied using DSC, at continuous cooling conditions with constant cooling rates of 5 °C, 10 °C, 20 °C, 30 °C, 40 °C, and 50 °C/min. The results from calorimetry were then used to trace and model the transformation kinetics in continuous cooling conditions. Based on suitably interpreted DSC results, continuous cooling–transformation (CCT) diagrams were calculated with lines of isotransformed fraction. The kinetics of transformation were modeled using the Johnson–Mehl–Avrami (JMA) theory and by applying the "concept of additivity." The JMA kinetic parameters were derived. Good agreement between the calculated and experimental transformed fractions is demonstrated. Using the derived kinetic parameters, the transformation in a Ti-6Al-4V alloy can be described for any cooling path and condition. An interpretation of the results from the point of view of activation energy for nucleation is also presented.

Distribution-based adaptive bounding genetic algorithm for continuous optimisation problems

A new search-space-updating technique for genetic algorithms is proposed for continuous optimisation problems. Other than gradually reducing the search space during the evolution process with a fixed reduction rate set ‘a priori’, the upper and the lower boundaries for each variable in the objective function are dynamically adjusted based on its distribution statistics. To test the effectiveness, the technique is applied to a number of benchmark optimisation problems in comparison with three other techniques, namely the genetic algorithms with parameter space size adjustment (GAPSSA) technique [A.B. Djurišic, Elite genetic algorithms with adaptive mutations for solving continuous optimization problems – application to modeling of the optical constants of solids, Optics Communications 151 (1998) 147–159], successive zooming genetic algorithm (SZGA) [Y. Kwon, S. Kwon, S. Jin, J. Kim, Convergence enhanced genetic algorithm with successive zooming method for solving continuous optimization problems, Computers and Structures 81 (2003) 1715–1725] and a simple GA. The tests show that for well-posed problems, existing search space updating techniques perform well in terms of convergence speed and solution precision however, for some ill-posed problems these techniques are statistically inferior to a simple GA. All the tests show that the proposed new search space update technique is statistically superior to its counterparts.

Reactivity of the 4d transition metals toward N hydrogenation and NH dissociation: A DFT-based HSAB analysis

The density functional theory (DFT) based hard-soft acid-base (HSAB) reactivity indices, including the electrophilicity index, have been successfully applied to many areas of molecular chemistry. In this work we test the applicability of such an approach to fundamental surface chemistry. We have considered, as prototypical surface reactions, both the hydrogenation of atomic nitrogen and the dissociative adsorption of the NH molecular radical. By use of a DFT methodology, the minimum energy reaction pathways, and corresponding reaction barriers, of the above reactions over Zr(001), Nb(110), Mo(110), Tc(001), Ru(001), Rh(111), and Pd(111) have been determined. By consideration of the chemical potential and chemical hardness of the surface metal atoms, and the principle of electronegativity equalization, it is found that the charge transferred to the NH radical during the process of dissociative adsorption correlates very well with that determined by Mulliken population analysis. Furthermore, it is found that the stability of the NH/surface transition state complex relates directly to this charge transfer and that the trend in transition state stability predicted by a HSAB; treatment correlates very strongly with that determined by DFT calculations. With regards to N hydrogenation, we find that during the course of the reaction, H loses cohesion to the surface, as it must migrate from a 3-fold hollow site to either a bridge or top site, to react with N. Partial density of states (PDOS) and Mulliken population analysis reveal that this loss of bonding is accompanied by charge transfer from H to the surface metal atoms. Moreover, by simple modeling, we show that the reaction barriers are directly proportional to this mandatory charge transfer. Indeed, it is found that the reaction barriers correlate very well with the electrophilicity index of the metal atoms.