Exploring creative thinking in graphically mediated synchronous dialogues

This paper reports on an aspect of the EC funded Argunaut project which researched and developed awareness tools for moderators of online dialogues. In this study we report on an investigation into the nature of creative thinking in online dialogues and whether or not this creative thinking can be coded for and recognized automatically such that moderators can be alerted when creative thinking is occurring or when it has not occurred after a period of time. We outline a dialogic theory of creativity, as the emergence of new perspectives from the interplay of voices, and the testing of this theory using a range of methods including a coding scheme which combined coding for creative thinking with more established codes for critical thinking, artificial intelligence pattern-matching techniques to see if our codes could be read automatically from maps and ‘key event recall’ interviews to explore the experience of participants. Our findings are that: (1) the emergence of new perspectives in a graphical dialogue map can be recognized by our coding scheme supported by a machine pattern-matching algorithm in a way that can be used to provide awareness indicators for moderators; (2) that the trigger events leading to the emergence of new perspectives in the online dialogues studied were most commonly disagreements and (3) the spatial representation of messages in a graphically mediated synchronous dialogue environment such as Digalo may offer more affordance for creativity than the much more common scrolling text chat environments. All these findings support the usefulness of our new account of creativity in online dialogues based on dialogic theory and demonstrate that this account can be operationalised through machine coding in a way that can be turned into alerts for moderators.     

Parallel Iterator for Parallelizing Object-Oriented Applications

With the advent of multi-core processors, desktop application developers must finally face parallel computing and its challenges. A large portion of the computational load in a program rests within iterative computations. In object-oriented languages these are commonly handled using iterators which are inadequate for parallel programming. This paper presents a powerful Parallel Iterator concept to be used in object-oriented programs for the parallel traversal of a collection of elements. The Parallel Iterator may be used with any collection type (even those inherently sequential) and it supports several scheduling schemes which may even be decided dynamically at run-time. Some additional features are provided to allow early termination of parallel loops, exception handling and a solution for performing reductions. With a slight contract modification, the Parallel Iterator interface imitates that of the Java-style sequential iterator. All these features combine together to promote minimal, if any, code restructuring. Along with the ease of use, the results reveal negligible overhead and the expected inherent speedup.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Stochastic flow-shop scheduling with minimizing the expected number of tardy jobs

In this research, minimizing the expected number of tardy jobs in a dynamic m machine flow-shop scheduling problem, i.e., $ {F_m}\left| {{r_j}\left| {{\text{E}}\left[ {\sum {{U_j}} } \right]} \right.} \right. $ is investigated. It is assumed that the jobs with deterministic processing times and stochastic due dates arrive randomly to the flow-shop cell. The due date of each job is assumed to be normally distributed with known mean and variance. A dynamic method is proposed for this problem by which the m machine stochastic flow-shop problem is decomposed into m stochastic single-machine sub-problems. Then, each sub-problem is solved as an independent stochastic single-machine scheduling problem by a mathematical programming model. Comparison of the proposed method with the most effective rule of thumb for the proposed problem, i.e., shortest processing time first rule shows that the proposed method performs 23.9 % better than the SPT rule on average for industry-size scheduling problems.     

Generation of sodium hypochlorite (NaOCl) from sodium chloride solution using C/PbO_2 and Pb/PbO_2 electrodes

Two modified electrodes (Pb/PbO2 and C/PbO2) were prepared by electrodepositing a lead oxide layer on lead and carbon sub-strates. These modified electrodes were used as anodes for the generation of sodium hypochlorite (NaOCl) from sodium chloride solution. Different operating conditions and factors affecting the treatment process of NaOCl generation, including current density, pH values, con-ductive electrolytes, and electrolysis time, were studied and optimized. By comparison the C/PbO2 electrode shows a higher efficiency than the Pb/PbO2 electrode for the generation of NaOCl.     

Determining number of clusters and prototype locations via multi-scale clustering

In clustering algorithms, it is usually assumed that the number of clusters is known or given. In the absence of such a priori information, a procedure is needed to find an appropriate number of clusters. This paper presents a clustering algorithm that incorporates a mechanism for finding the appropriate number of clusters as well as the locations of cluster prototypes. This algorithm, called multi-scale clustering, is based on scale-space theory by considering that any prominent data structure ought to survive over many scales. The number of clusters as well as the locations of cluster prototypes are found in an objective manner by defining and using lifetime and drift speed clustering criteria. The outcome of this algorithm does not depend on the initial prototype locations that affect the outcome of many clustering algorithms. As an application of this algorithm, it is used to enhance the Hough transform technique.     

Axial offset control of PWR nuclear reactor core using intelligent techniques

Improved load following capability is one of the main technical performances of advanced PWR (APWR). Controlling the nuclear reactor core during load following operation encounters some difficulties. These difficulties mainly arise from nuclear reactor core limitations in local power peaking, while the core is subject to large and sharp variation of local power density during transients. Axial offset (AO) is the parameter usually used to represent of core power peaking, in form of a practical parameter. This paper, proposes a new intelligent approach to AO control of PWR nuclear reactors core during load following operation. This method uses a neural network model of the core to predict the dynamic behavior of the core and a fuzzy critic based on the operator knowledge and experience for the purpose of decision-making during load following operations. Simulation results show that this method can use optimum control rod groups maneuver with variable overlapping and may improve the reactor load following capability.     

Graphene network enabled high speed electrical actuation of shape memory nanocomposite based on poly(vinyl acetate)

Here strong electroactive shape memory nanocomposites were prepared by incorporating graphene nanoplatelets into poly(vinyl acetate) (PVAc ) through the simple solvent mixing method. TEM and XRD revealed that well exfoliated graphene nanoplatelets formed a continuous network throughout the matrix with a large amount of interconnectedness. Dynamic mechanical analysis showed that the inclusion of graphene significantly improves both glassy and rubbery moduli of the matrix. Furthermore, the prepared nanocomposites demonstrated a marked electrical conductivity up to 24.7 S m^?1 and thereby surprisingly rapid electrical actuation behaviour exhibiting a 100% recovery ratio in 2.5 s. Moreover, PVAc and its nanocomposites displayed scratch self‐healing capability. This work demonstrates that the PVAc /graphene nanocomposites with high modulus and excellent electroactive shape memory performance can be a promising material in many applications such as sensors and fast deployable and actuating devices. © 2016 Society of Chemical Industry     

Comparative performance analysis of solar powered supercritical-transcritical CO2 based systems for hydrogen production and multigeneration

CO₂ based power and refrigeration cycles have been developed and analyzed in different existing studies. However, the development of a CO₂ based comprehensive energy system and its performance analysis have not been considered. In this study, the integration of a CO₂ based solar parabolic trough collector system, a supercritical CO₂ power cycle, a transcritical CO₂ power cycle, and a CO₂ based cascade refrigeration system for hydrogen production and multigeneration purpose is analyzed thermodynamically. This study aims to analyze and compare the difference in the thermodynamic performance of comprehensive energy systems when CO₂ is used as the working fluid in all the cycles with a system that uses other working fluids. Therefore, two comprehensive energy systems with the same number of subsystems are designed to justify the comparison. The second comprehensive energy system uses liquid potassium instead of CO₂ as a working fluid in the solar parabolic trough collector and a steam cycle is used to replace the transcritical CO₂ power cycle. Results of the energy and exergy performance analysis of two comprehensive energy systems showed that the two systems can be used for the multigeneration purpose. However, the use of a steam cycle and potassium-based solar parabolic trough collector increases the comprehensive energy systems’ overall energy and exergy efficiency by 41.9% and 26.7% respectively. Also, the use of liquid potassium as working fluid in the parabolic trough collectors increases the absorbed solar energy input by 419 kW and 2100 kW thereby resulting in a 23% and 90.7% increase in energetic and exergetic efficiency respectively. The carbon emission reduction potential of the two comprehensive energy systems modelled in this study is also analyzed.