Ethical Dimensions in Clothing Purchase

It is widely reported that consumer interest in environmental and ethical issues is growing. Evidence suggests that ethical considerations are now impacting on a broad range of consumption decisions. The focus of this paper is the impact such concerns may hold in clothing purchase decision making. Through an inductive qualitative approach, clothing purchase decision making has been explored before discussing consumers’ knowledge and concern of ethical issues within the supply chain. Any impact that these concerns may exert has been discussed. It is identified that although there is widespread knowledge and understanding of the ethical issues that may be present in the manufacture of clothing, these concerns do not play a primary role in consumers’ selection of items. Product attributes such as colour, style and fit dominate the decision making process in most cases. Despite this, ethical considerations can be seen to impact on the consumer in three key ways: initial boycott of particular products or brands; influencing final purchase decisions if items are similar on other criteria and, impacting on post-purchase satisfaction with the product

Automated, objective texture segmentation of multibeam echosounder data - Seafloor survey and substrate maps from James Island to Ozette Lake, Washington Outer Coast

Without knowledge of basic seafloor characteristics, the ability to address any number of critical marine and/or coastal management issues is diminished. For example, management and conservation of essential fish habitat (EFH), a requirement mandated by federally guided fishery management plans (FMPs), requires among other things a description of habitats for federally managed species. Although the list of attributes important to habitat are numerous, the ability to efficiently and effectively describe many, and especially at the scales required, does not exist with the tools currently available. However, several characteristics of seafloor morphology are readily obtainable at multiple scales and can serve as useful descriptors of habitat. Recent advancements in acoustic technology, such as multibeam echosounding (MBES), can provide remote indication of surficial sediment properties such as texture, hardness, or roughness, and further permit highly detailed renderings of seafloor morphology. With acoustic-based surveys providing a relatively efficient method for data acquisition, there exists a need for efficient and reproducible automated segmentation routines to process the data. Using MBES data collected by the Olympic Coast National Marine Sanctuary (OCNMS), and through a contracted seafloor survey, we expanded on the techniques of Cutter et al. (2003) to describe an objective repeatable process that uses parameterized local Fourier histogram (LFH) texture features to automate segmentation of surficial sediments from acoustic imagery using a maximum likelihood decision rule. Sonar signatures and classification performance were evaluated using video imagery obtained from a towed camera sled. Segmented raster images were converted to polygon features and attributed using a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999) for use in a geographical information system (GIS). (PDF contains 41 pages.

Soft-bottom fishes and spatial protection: findings from a temperate marine protected area

Numerous studies over the last decades have focused on marine protected areas (MPAs) and their effects on fish communities. However, there is a knowledge gap regarding how species that live associated with soft-substrates (e.g., sand, mud) respond to spatial protection. We analyzed abundance, biomass and total lengths of the soft-bottom fishes in a multiple-use MPA in the north-eastern Atlantic, the Luiz Saldanha Marine Park (Portugal), during and after the implementation of its management plan. Data were collected by experimental fishing in areas with three different levels of protection, during the implementation period and for three years after full implementation of the MPA. Univariate analysis detected significant biomass increases between the two periods. Fish assemblages were mainly structured by depth and substrate, followed by protection level. Community composition analyses revealed significant differences between protection levels and between the two periods. Species exhibited a broad variation in their response to protection, and we hypothesize that factors such as species habitat preferences, body size and late maturity might be underlying determinants. Overall, this study provides some evidence of protection effectiveness in soft-bottom fish communities, supported by the significant increase in biomass in the protected areas and the positive trends of some species.project LIFE-BIOMARES [LIFE06 NAT/P/000192]; project BUFFER (ERA-Net BiodivERsA); company SECIL-Companhia Geral de Cal e Cimento S.A.; FCT-Foundation for Science and Technology [CCMAR/Multi/04326/2013, SFRH/BD/80771/2011]; Foundation for Science and Technology [SFRH/BD/80771/2011]; 2012 Sesimbra Scientific Priz

Characterization of 30 76^{76}Ge enriched Broad Energy Ge detectors for GERDA Phase II

The GERmanium Detector Array (GERDA) is a low background experiment located at the Laboratori Nazionali del Gran Sasso in Italy, which searches for neutrinoless double beta decay of $^{76}$Ge into $^{76}$Se+2e$^-$. GERDA has been conceived in two phases. Phase II, which started in December 2015, features several novelties including 30 new Ge detectors. These were manufactured according to the Broad Energy Germanium (BEGe) detector design that has a better background discrimination capability and energy resolution compared to formerly widely-used types. Prior to their installation, the new BEGe detectors were mounted in vacuum cryostats and characterized in detail in the HADES underground laboratory in Belgium. This paper describes the properties and the overall performance of these detectors during operation in vacuum. The characterization campaign provided not only direct input for GERDA Phase II data collection and analyses, but also allowed to study detector phenomena, detector correlations as well as to test the strength of pulse shape simulation codes.Comment: 29 pages, 18 figure

Hedonic price models and indices based on boosting applied to the Dutch housing market

We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction. Boosting enables capturing of complex nonlinear relationships and interaction effects between input variables.We report mean relative errors and mean absolute error for all regions and compare our models with a standard linear regression approach. Our model improves prediction performance with up to 40% compared with Linear Regression. Next, we interpret the boosted models: we determine the most influential characteristics and graphically depict the relationship between the most important input variables and the house price. We find the size of the house to be the most important input for all but one region, and find some interesting nonlinear relationships between inputs and price.Finally, we construct hedonic price indices and compare these to the mean and median index and find that these indices differ notably in the urban regions of Amsterdam and Rotterdam.data mining;machine learning;gradient boosting;housing;hedonic price models;hedonic price index