Dynamical entanglement transfer for quantum-information networks

A key element in the architecture of a quantum-information processing network is a reliable physical interface between fields and qubits. We study a process of entanglement transfer engineering, where two remote qubits respectively interact with an entangled two-mode continuous-variable (CV) field. We quantify the entanglement induced in the qubit state at the expenses of the loss of entanglement in the CV system. We discuss the range of mixed entangled states which can be obtained with this setup. Furthermore, we suggest a protocol to determine the residual entangling power of the light fields inferring, thus, the entanglement left in the field modes which, after the interaction, are no longer in a Gaussian state. Two different setups are proposed: a cavity-QED system and an interface between superconducting qubits and field modes. We address in detail the practical difficulties inherent in these two proposals, showing that the latter is promising in many aspects.

Energy efficiency investments in the home: Swiss homeowners and expectations about future energy prices

Using conjoint choice experiments, we surveyed 473 Swiss homeowners about their preferences for energy efficiency home renovations.We find that homeowners are responsive to the upfront costs of the renovation projects, governmentoffered rebates, savings in energy expenses, time horizon over which such savings would be realized, and thermal comfort improvement. The implicit discount rate is low, ranging from 1.5 to 3%, depending on model specification. This is consistent with Hassett and Metcalf (1993) and Metcalf and Rosenthal (1995), and with the fact that our scenarios contain no uncertainty. Respondents who feel completely uncertain about future energy prices are more likely to select the status quo (no renovations) in any given choice task and weight the costs of the investments more heavily than the financial gains (subsidies and savings on the energy bills). Renovations are more likely when respondents believe that climate change considerations are important determinants of home renovations. Copyright © 2013 by the IAEE. All rights reserved.

Modelling the Performance of Irish Credit Unions, 2002 to 2010

This study undertakes a modeling based performance assessment of all Irish credit unions between 2002 and 2010, a particularly turbulent period in their history. The analysis explicitly addresses the current challenges faced by credit unions in that the modeling approach used rewards credit unions for reducing undesirable outputs (impaired loans and investments) as well as for increasing desirable outputs (loans, earning assets and members’ funds) and decreasing inputs (labour expenditure, capital expenditure and fund expenses). The main findings are: credit unions are subject to increasing returns to scale; technical regression occurred in the years after 2007; there is significant scope for an improvement in efficiency through expansion of desirable outputs and contraction of undesirable outputs and inputs; and that larger credit unions, that are better capitalised and pay a higher dividend to members are more efficient than their smaller, less capitalised, and lower dividend paying counterparts.

Environmental Aspects of Underground Construction and Exploration of Underground Structure – State of the Art

An underground work (such as a tunnel or a cavern) has many, well known, environmental qualities such as: no physical barriers crossing the land, less maintenance costs than an analogous surface structure, less expenses for heating and conditioning; a localized emission of noise, gas, dust during operation and, finally, a better protection against seismic actions.
It cannot be forgotten, anyway, that some negative environmental features are present such as, for example, : perturbation, pollution and drainage of the groundwater; settlements; disposal of waste rock.
In the paper the above mentioned concepts are discussed and analysed to give a global overview of all this aspects.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

Improving fuel economy application of the organic Rankine Cycle on a hybrid bus

Globally vehicle operators are experiencing rising fuel costs and increased
running expenses as governments around the world attempt to decrease carbon dioxide emissions and fossil fuel consumption, due to global warming and the drive to reduce dependency on fossil fuels. Recent advances in hybrid vehicle design have made great strides towards more efficient operation, with regenerative braking being widely used to capture otherwise lost energy. In this paper a hybrid series bus is developed a step further, by installing another method of energy capture on the vehicle. In this case, it is in the form of the Organic Rankine Cycle (ORC). The waste heat expelled to the exhaust and coolant streams is recovered and converted to electrical energy which is then stored in the hybrid vehicles batteries. The electrical energy can then be used for the auxiliary power circuit or to assist in vehicle propulsion, thus reducing the load on the engine, thereby improving the overall fuel economy of the vehicle and reducing carbon dioxide emissions.

A novel forward gene selection algorithm for microarray data

This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches