Sustainable decisions on product upgrade confrontations with remanufacturing operations

In recent decades, remanufacturing is perceived to be an environmentally friendly option due to the reduced consumption of materials, energy etc. It should be noted that whether the remanufacturing operations are undertaken by the original equipment manufacturers (OEMs) or outsourced to the remanufacturers, given the size and the growth of remanufactured products, many OEMs intend to fend off the potential cannibalization of new products sales through differentiating their quality levels from those of remanufactured ones by launching upgraded versions. To understand whether and how the product upgrading strategy impacts on optimal outcomes in the context of the remanufacturing operations undertaken by OEMs or third-party remanufacturers (TPRs), in this paper, we develop two models that highlight the OEM’s product upgrading strategy under the scenarios where (1) the OEM owns its remanufacturing operations in-house (Model O) or (2) remanufacturing operations are undertaken by a TPR (Model T). Among other results, we find that, from an economic performance perspective, it is more beneficial for the OEM to perform remanufacturing operations in-house; however, from an environmental sustainability perspective, such behavior is not always good for our environment. In particular, when the level of product upgrading is pronounced, the remanufacturing operations undertaken by the OEM are always detrimental to our environment, due to indulging in remanufacturing, as seen in Model O

Genetic analysis of phytoene synthase 1 (Psy1) gene function and regulation in common wheat

Transcriptome details for three transgenic lines with the most significantly reduced YPC and non-transformed controls. (DOCX 18 kb

How Did Order-Flow Impact Bond Prices During the European Sovereign Debt Crisis?

The impact of trades on price dynamics in the European sovereign debt markets is of significant importance to policy makers and market participants. This paper uses high-frequency quote and transaction data from the MTS European sovereign bond inter-dealer platform to investigate price-order-flow dynamics from July 2005 until December 2011 for Germany, France, Portugal, Italy, Ireland, Spain and Greece. We find that order-flow had a larger impact on quote revision in a relatively low-intensity trading environment than in a relatively high-intensity trading environment implying that informed traders should only execute in low-intensity trading environments when they value immediacy over discretion. This analysis is consistent with the limited prior literature for European debt markets. Our analysis indicates that this relationship persists during turbulent market conditions. Also, we find that the impact of order-flow on subsequent trades was larger during periods of high-trading intensity implying that market participants use order splitting trading strategies

Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz

Simulation of quantum chemistry is one of the most promising applications of quantum computing. While recent experimental works have demonstrated the potential of solving electronic structures with variational quantum eigensolver (VQE), the implementations are either restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors for the more accurate unitary coupled cluster (UCC) ansatz. Here, integrating experimental and theoretical advancements of improved operations and dedicated algorithm optimisations, we demonstrate an implementation of VQE with UCC for H_2, LiH, F_2 from 4 to 12 qubits. Combining error mitigation, we produce high-precision results of the ground-state energy with error suppression by around two orders of magnitude. For the first time, we achieve chemical accuracy for H_2 at all bond distances and LiH at small bond distances in the experiment. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.Comment: 8 pages, 4 figures in the main text, and 29 pages supplementary materials with 16 figure

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.Comment: 7 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1 table in supplementary material

Price discovery in the dual-platform US Treasury market

Inter-dealer trading in US Treasury securities is almost equally divided between two electronic trading platforms that have only slight differences in terms of their relative liquidity and transparency. BrokerTec is more active in the trading of 2-, 5-, and 10-year T-notes while eSpeed has more active trading in the 30-year bond. Over the period studied, eSpeed provides a more pre-trade transparent platform than BrokerTec. We examine the contribution to ‘price discovery’ of activity in the two platforms using high frequency data. We find that price discovery does not derive equally from the two platforms and that the shares vary across term to maturity. This can be traced to differential trading activities and transparency of the two platforms