Designing a Collaborative Visual Analytics Tool for Social and Technological Change Prediction

This paper discussed about GreenOracle which an interdisciplinary team designed and developed. GreenOracle is a collaborative visual analytics tool for predicting global climate change's impact on US power grids and its implications for society and national security. These future scenarios provide critical assessments and information to help policymakers and stakeholders formulate a coherent and unified strategy toward shaping a safe and secure society.     

Inter-organisational knowledge transfer in social networks: A definition of intermediate ties

A social network perspective helps identify and analyse informal knowledge transfer among people and organisations with the aim to recommend organisational interventions and improvements, for example in the form of Information and Communication Technology (ICT) support. This paper particularly focuses on a key concept of social network analysis (SNA), the concept of tie strength, in an inter-organisational knowledge transfer context. Tie strength describes the strength of a social relationship. In the past, SNA literature with a knowledge management context has often emphasized the importance of strong and/or weak ties rather than of intermediate (medium strong) ties in a social network. Nevertheless, in this study, intermediate ties are identified as the dominant links among key groups of organisation in a social network. Intermediate ties also help connect otherwise weakly linked organisations. Moreover, a definition of intermediate ties in the context of knowledge management is introduced. With the help of cluster analysis and an investigation into the levels of reciprocity, intermediate ties can be clearly defined in a social network. Due to their importance for knowledge transfer in a social network, intermediate ties should be primarily supported, for example by appropriate ICT.     

Comprehensive evaluation of a CO2‐capturing high‐efficiency power generation system for utilizing waste heat from factories

It is becoming more important to realize CO₂‐capturing power generation systems (PGSs) for drastically decreasing an amount of CO₂ emission into the atmosphere. However, net power generation efficiency (NPGE) of a CO₂‐capturing system has been considered to be greatly deteriorated, since capturing CO₂ requires extra energy. This paper proposes a new CO₂‐capturing PGS that has a high‐efficient NPGE by utilizing waste heat from factories. As an example of a waste heat, exhaust gas with temperature 200°C from refuse incinerator plants is adopted. In the proposed system, the temperature of saturated steam produced by utilizing the waste heat is raised by combusting fuel with the use of pure oxygen in a combustor, and is used as the main working fluid of a gas turbine PGS. It is estimated that the proposed system has a fuel‐to‐electricity NPGE of 59.3%, when turbine inlet temperature (TIT) is assumed to be 1000°C. The economics of the proposed system is also evaluated and the CO₂ reduction cost is estimated to be small; 4.16 U.S. $ t⁻¹ CO₂ compared with 32.1 U.S. $ t⁻¹ CO₂ for a conventional steam turbine PGS. It is shown that CO₂‐capturing is not cost consuming but becomes to be profitable owing to improved power generation characteristics, when its TIT is increased from 1000 to 1200°C. Copyright © 2009 John Wiley & Sons, Ltd.     

Role of Learning Algorithm in Neural Network-Based Backcalculation of Flexible Pavements

Nondestructive testing (NDT) methods are widely used for the performance evaluation of flexible pavements. Falling weight deflectometer (FWD), which measures time-domain deflections resulting from applied impulse loads, is the most popular technique among all NDT methods. The evaluation of the FWD data requires the inversion of mechanical pavement properties using a backcalculation tool that includes both a forward pavement response model and an optimization algorithm. Neural networks (NNs) have also emerged as alternative tools that can be employed for pavement backcalculation problems relative to their real-time processing abilities. However, there have been no comprehensive analyses in previous studies that focus on the learning algorithm and the architecture of a NN model, which considerably affect backcalculation results. In this study, 284 different NN models were developed using synthetic training and testing databases obtained by layered elastic theory. Results indicated that both the learning algorithm and network architecture play important roles in the performance of the NN based backcalculation process.     

An application of polarized domains in ferroelectric thin films using scanning probe microscope

The feasibility of utilizing PZT films as future data storage media was investigated using a modified AFM. Applying voltages between a conductive AFM tip and the PZT films causes the switching of ferroelectric domains. The domains are observed using an EFM imaging technique. The experimental results and calculations revealed that the electrostatic force generated between the polarized area and the tip is a main contributor for the imaging of the polarized domains. The written features on ferroelectric films were less than 100 nm in diameter, implying the possibility of realizing data storage devices with ultra-high area density. The disappearance of the polarized images without any applied voltage was observed, which is a drawback in this application of PZT thin films.     

Nitrogen solubility in liquid manganese and ferromanganese alloys

The nitrogen solubilities in liquid manganese, manganese-iron, manganese-carbon, and manganese-iron-carbon alloys have been measured by the gas-liquid metal equilibration technique in the temperature range of 1623 to 1823 K. The equilibrium nitrogen content in pure liquid manganese at an atmospheric nitrogen pressure is high, and it does not follow Sievert’s law, i.e., f _N is not unity. The reduced nitrogen partial pressures by dilution with argon enabled us to obtain more reliable information on the thermodynamics of nitrogen in liquid manganese. The nitrogen dissolution follows Sievert’s law at nitrogen contents below 1 wt pct. The standard free-energy change for the dissolution of nitrogen in pure liquid manganese has been determined as −67,222+30.32T J/g atom, with the standard state of nitrogen taken as a 1 wt pct solution. Carbon and iron in manganese-rich melts decrease the nitrogen solubility significantly. The first- and second-order interaction parameters between nitrogen and other elements in manganese alloy melts have been determined. The activity coefficient of nitrogen in a ferromanganese alloy melt can be expressed as