PubMed · 13993529
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E P TAXAY, H H AIKEN. 1963. Regional computer centers.. https://doi.org/10.1016/0002-9343(63)90115-3
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A programmable quantum computer is experimentally demonstrated by nuclear magnetic resonance using one qubit for the program and two qubits for data. A non-separable two-qubit operation is performed in a programmable way to show the successful demonstration. Projective measurements required in the programmable quantum computer are simulated by averaging the results of experiments just like when producing an effective pure state.
Millions of end-of-life electronic components are retired annually due to the proliferation of new models and their rapid obsolescence. The recovery of resources such as plastics from these goods requires their disassembly. The time required for each disassembly and its associated cost is defined by the operator's familiarity with the product design and its complexity. Since model proliferation serves to complicate an operator's learning curve, it is worthwhile to investigate the benefits to be gained in a disassembly operator's preplanning process. Effective disassembly process design demands the application of green engineering principles, such as those developed by Anastas and Zimmerman (Environ. Sci. Technol. 2003, 37, 94A-101A), which include regard for product complexity, structural commonality, separation energy, material value, and waste prevention. This paper introduces the concept of design symbolsto help the operator more efficiently survey product complexity with respect to location and number of fasteners to remove a structure that is common to all electronics: the housing. With a sample of 71 different computers, printers, and monitors, we demonstrate that appropriate symbols reduce the total disassembly planning time by 13.2 min. Such an improvement could well make efficient the separation of plastic that would otherwise be destined for waste-to-energy or landfill. The symbolic methodology presented may also improve Design for Recycling and Design for Maintenance and Support.
Green engineering requires the designer to consider a very extensive set of environmental impacts. To minimize these impacts, the designer must significantly expand his or her "toolset" of product design concepts, alternative materials, manufacturing systems, and analytic methods for addressing life cycle impacts. This can overwhelm a designer, who then resorts to overly simplistic rules or checklists out of necessity. The central issue is how to identify all "pollution prevention pays" opportunities and then how to deal with the unavoidable tradeoffs that arise after all these opportunities have been exhausted. This paper presents a framework for employing mathematical decision modeling toward this end. A domain-independent constrained optimization formulation is presented. A multiattribute utility function reflects the willingness to pay for environmental improvement and is the basis of the objective function. The feasibility constraints reflect the unavoidable tradeoffs. Several case studies are presented, including power systems, floor tile manufacturing, and computer systems.