Improving productivity - Opening the black box

Hourly productivity levels in the UK still remain behind those in some competitor countries. The government devotes much policy attention to enhancing productivity and continues to emphasise its five drivers - investment, innovation, skills, enterprise, and competition. This article argues that it is investment broadly defined that is the key to sustained productivity improvement. The emphasis should be on improving productivity simultaneously with improving the quality of production. Only thus will the gains be widely shared. In achieving these aims there are two prerequisites for policy-makers. The first is to ensure better coordination of policy than appears to be currently achieved by the present departmental structures in Whitehall. The second is to recognize fully the long and complex chain of causation that can be triggered by pulling on one policy lever. Such complexity can only be fully understood by more research on what actually goes on inside the black box of the organization. © 2006 Oxford University Press.

Global civil aviation black carbon emissions.

Aircraft black carbon (BC) emissions contribute to climate forcing, but few estimates of BC emitted by aircraft at cruise exist. For the majority of aircraft engines the only BC-related measurement available is smoke number (SN)-a filter based optical method designed to measure near-ground plume visibility, not mass. While the first order approximation (FOA3) technique has been developed to estimate BC mass emissions normalized by fuel burn [EI(BC)] from SN, it is shown that it underestimates EI(BC) by >90% in 35% of directly measured cases (R(2) = -0.10). As there are no plans to measure BC emissions from all existing certified engines-which will be in service for several decades-it is necessary to estimate EI(BC) for existing aircraft on the ground and at cruise. An alternative method, called FOX, that is independent of the SN is developed to estimate BC emissions. Estimates of EI(BC) at ground level are significantly improved (R(2) = 0.68), whereas estimates at cruise are within 30% of measurements. Implementing this approach for global civil aviation estimated aircraft BC emissions are revised upward by a factor of ~3. Direct radiative forcing (RF) due to aviation BC emissions is estimated to be ~9.5 mW/m(2), equivalent to ~1/3 of the current RF due to aviation CO2 emissions.

Updated correlation between aircraft smoke number and black carbon concentration

Aircraft emissions of black carbon (BC) contribute to anthropogenic climate forcing and degrade air quality. The smoke number (SN) is the current regulatory measure of aircraft particulate matter emissions and quantifies exhaust plume visibility. Several correlations between SN and the exhaust mass concentration of BC (CBC) have been developed, based on measurements relevant to older aircraft engines. These form the basis of the current standard method used to estimate aircraft BC emissions (First Order Approximation version 3 [FOA3]) for the purposes of environmental impact analyses. In this study, BC with a geometric mean diameter (GMD) of 20, 30, and 60 nm and filter diameters of 19 and 35 mm are used to investigate the effect of particle size and sampling variability on SN measurements. For BC with 20 and 30 nm GMD, corresponding to BC emitted by modern aircraft engines, a smaller SN results from a given CBC than is the case for BC with 60 nm GMD, which is more typical of older engines. An updated correlation between CBC and SNthat accounts for typical size of BC emitted by modern aircraft is proposed. An uncertainty of ±25% accounts for variation in GMD in the range 20-30 nm and for the range of filter diameters. The SN-CBC correlation currently used in FOA3 underestimates by a factor of 2.5-3 for SN <15, implying that current estimates of aircraft BC emissions derived from SN are underestimated by the same factor. Copyright © American Association for Aerosol Research.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.