An approach to scenario analysis of the sustainability of an industrial sector applied to clothing and textiles in the UK

Companies aiming to be 'sustainability leaders' in their sector and governments wanting to support their ambitions need a means to assess the changes required to make a significant difference in the impact of their whole sector. Previous work on scenario analysis/scenario planning demonstrates extensive developments and applications, but as yet few attempts to integrate the 'triple bottom line' concerns of sustainability into scenario planning exercises. This paper, therefore, presents a methodology for scenario analysis of large change to an entire sector. The approach includes calculation of a 'triple bottom line graphic equaliser' to allow exploration and evaluation of the trade-offs between economic, environmental and social impacts. The methodology is applied to the UK's clothing and textiles sector, and results from the study of the sector are summarised. In reflecting on the specific study, some suggestions are made about future application of a similar methodology, including a template of candidate solutions that may lead to significant reduction in impacts. © 2007 Elsevier Ltd. All rights reserved.

Encoding of mixtures in a simple olfactory system.

Natural odors are usually mixtures; yet, humans and animals can experience them as unitary percepts. Olfaction also enables stimulus categorization and generalization. We studied how these computations are performed with the responses of 168 locust antennal lobe projection neurons (PNs) to varying mixtures of two monomolecular odors, and of 174 PNs and 209 mushroom body Kenyon cells (KCs) to mixtures of up to eight monomolecular odors. Single-PN responses showed strong hypoadditivity and population trajectories clustered by odor concentration and mixture similarity. KC responses were much sparser on average than those of PNs and often signaled the presence of single components in mixtures. Linear classifiers could read out the responses of both populations in single time bins to perform odor identification, categorization, and generalization. Our results suggest that odor representations in the mushroom body may result from competing optimization constraints to facilitate memorization (sparseness) while enabling identification, classification, and generalization.

Encoding of mixtures in a simple olfactory system

Natural odors are usually mixtures; yet, humans and animals can experience them as unitary percepts. Olfaction also enables stimulus categorization and generalization. We studied how these computations are performed with the responses of 168 locust antennal lobe projection neurons (PNs) to varying mixtures of two monomolecular odors, and of 174 PNs and 209 mushroom body Kenyon cells (KCs) to mixtures of up to eight monomolecular odors. Single-PN responses showed strong hypoadditivity and population trajectories clustered by odor concentration and mixture similarity. KC responses were much sparser on average than those of PNs and often signaled the presence of single components in mixtures. Linear classifiers could read out the responses of both populations in single time bins to perform odor identification, categorization, and generalization. Our results suggest that odor representations in the mushroom body may result from competing optimization constraints to facilitate memorization (sparseness) while enabling identification, classification, and generalization