How drivers decide whether to get a fuel cell vehicle: An ethnographic decision model

This article develops and tests an ethnographic decision model (EDM) of hydrogen fuel cell vehicle (FCV) adoption using interviews with California residents that either actually adopted an FCV or “seriously considered” doing so before deciding against it. We developed an initial model from 25 semi-structured interviews in which respondents self-described their decision-making processes. We iteratively tested and refined the model in a second round of 53 structured interviews. The final model consists of a first stage that assesses FCV adoption feasibility and a second stage that compares FCVs to other vehicle types. The model ultimately correctly predicts 86.8% of cases in the sample. In the first stage, respondents preferred to satisfy their need for a primary refueling station near home but a substantial number were willing to rely on a station near or on the way to work or other destination. Most drivers required a convenient backup station and a means of managing long-distance trips. Vehicle size options eliminated a few respondents. None rejected FCV adoption due to insufficient driving range. In the second stage, nearly all drivers engaged in some kind of cost comparison, though the factors considered varied greatly. Most opted for what they viewed as the less costly option, although a few FCV adopters and non-adopters were willing to pay more for their more preferred option. EDM is a promising qualitative research method for generating insights into how people navigate the decision whether or not to get an alternative-fuel vehicle.     

Chemical warfare and survival strategies in bacterial range expansions

Dispersal of species is a fundamental ecological process in the evolution and maintenance of biodiversity. Limited control over ecological parameters has hindered progress in understanding of what enables species to colonize new areas, as well as the importance of interspecies interactions. Such control is necessary to construct reliable mathematical models of ecosystems. In our work, we studied dispersal in the context of bacterial range expansions and identified the major determinants of species coexistence for a bacterial model system of three Escherichia coli strains (toxin-producing, sensitive and resistant). Genetic engineering allowed us to tune strain growth rates and to design different ecological scenarios (cyclic and hierarchical). We found that coexistence of all strains depended on three strongly interdependent factors: composition of inoculum, relative strain growth rates and effective toxin range. Robust agreement between our experiments and a thoroughly calibrated computational model enabled us to extrapolate these intricate interdependencies in terms of phenomenological biodiversity laws. Our mathematical analysis also suggested that cyclic dominance between strains is not a prerequisite for coexistence in competitive range expansions. Instead, robust three-strain coexistence required a balance between growth rates and either a reduced initial ratio of the toxin-producing strain, or a sufficiently short toxin range.     

A comparison between state-of-the-art and neural network modelling of solar collectors

The state-of-the-art modelling of solar collectors as described in the European Standard EN 12975-2 is based on equations describing the thermal behaviour of the collectors by characterising the physical phenomena, e.g. transmission of irradiance through transparent covers, absorption of irradiance by the absorber, temperature dependent heat losses and others. This approach leads to so called collector parameters that describe these phenomena, e.g. the zero-loss collector efficiency η₀ or the heat loss coefficients a₁ and a₂.Although the state-of-the-art approach in collector modelling and testing fits most of the collector types very well there are some collector designs (e.g. “Sydney” tubes using heat pipes and “water-in-glass” collectors) which cannot be modelled with the same accuracy than conventional collectors like flat plate or standard evacuated tubular collectors. The artificial neural network (ANN) approach could be an appropriate alternative to overcome this drawback.To compare the different approaches of modelling investigations for a conventional flat plate collector and an evacuated “Sydney” tubular collector have been carried out based on performance measurements according to the European Standard EN 12975-2. The investigations include the parameter identification (training), the comparisons between measured and modelled collector output and the simulated yearly collector yield for a solar domestic hot water system for both models.The obtained results show better agreement between measured and calculated collector output for the artificial neural network approach compared with the state-of-the-art modelling. The investigations also show that for the ANN approach special test sequences have to be designed and that the determination of the ANN that fits the thermal performance of the collector in the best way depends significantly on the expertise of the user.Nevertheless artificial neural networks have the potential to become an interesting alternative to the state-of-the-art collector models used today.     

Quantum transport including nonparabolicity and phonon scattering: application to silicon nanowires

In this work, the particular and combined influence of nonparabolicity and phonon scattering on the device characteristics of a triple-gate silicon nanowire is investigated. In addition, different approximations of the retarded self-energy for electron-phonon scattering are analyzed in terms of the electrostatics, current and computational cost.     

Understanding the metabolic burden of recombinant antibody production in Saccharomyces cerevisiae using a quantitative metabolomics approach

The cellular changes induced by heterologous protein expression in the yeast Saccharomyces cerevisiae have been analysed on many levels and found to be significant. However, even though high‐level protein production poses a metabolic burden, evaluation of the expression host at the level of the metabolome has often been neglected. We present a comparison of metabolite profiles of a wild‐type strain with those of three strains producing recombinant antibody variants of increasing size and complexity: an scFv fragment, an scFv–Fc fusion protein and a full‐length IgG molecule. Under producing conditions, all three recombinant strains showed a clear decrease in growth rate compared with the wild‐type strain and the severity of the growth phenotype increased with size of the protein. The levels of 76 intracellular metabolites were determined using a targeted (semi) quantitative mass spectrometry based approach. Based on unsupervised and supervised multivariate analysis of metabolite profiles, together with pathway activity profiling, the recombinant strains were found to be significantly different from each other and from the wild‐type strain. We observed the most prominent changes in metabolite levels for metabolites involved in amino acid and redox metabolism. Induction of the unfolded protein response was detected in all producing strains and is considered to be a contributing factor to the overall metabolic burden on the cells.     

Application of classification and regression trees for sensitivity analysis of the Escherichia coli O157:H7 food safety process risk model

Microbial food safety process risk models are simplifications of the real world that help risk managers in their efforts to mitigate food safety risks. An important tool in these risk assessment endeavors is sensitivity analysis, a systematic method used to quantify the effect of changes in input variables on model outputs. In this study, a novel sensitivity analysis method called classification and regression trees was applied to safety risk assessment with the use of portions of the Slaughter Module and Preparation Module of the E. coli O157:H7 microbial food safety process risk as an example. Specifically, the classification and regression trees sensitivity analysis method was evaluated on the basis of its ability to address typical characteristics of microbial food safety process risk models such as nonlinearities, interaction, thresholds, and categorical inputs. Moreover, this method was evaluated with respect to identification of high exposure scenarios and corresponding key inputs and critical limits. The results from the classification and regression trees analysis applied to the Slaughter Module confirmed that the process of chilling carcasses is a critical control point. The method identified a cutoff value of a 2.2-log increase in the number of organisms during chilling as a critical value above which high levels of contamination would be expected. When classification and regression trees analysis was applied to the cooking effects part of the Preparation Module, cooking temperature was found to be the most sensitive input, with precooking treatment (i.e., raw product storage conditions) ranked second in importance. This case study demonstrates the capabilities of classification and regression trees analysis as an alternative to other statistically based sensitivity analysis methods, and one that can readily address specific characteristics that are common in microbial food safety process risk models.     

Risk of Vibrio transmission linked to the consumption of crustaceans in coastal towns of Côte d'Ivoire

The purpose of this study was to assess the risk of Vibrio spp. transmission from crustaceans to humans in two coastal towns of C?te d'Ivoire. Bacteriologic analysis was performed on 322 crustacean samples obtained from six markets in Abidjan and one in Dabou. Suspected Vibrio colonies were identified by morphological, cultural, biochemical, and molecular tests and matrix-assisted laser desorption ionization-time-of-flight mass spectrometry. PCR assays were used to further characterize Vibrio strains. A survey on consumption of crustaceans was conducted among 120 randomly selected households in Abidjan. Overall, Vibrio spp. were isolated from 7.8% of the crustacean samples studied, at levels as high as 6.3 log CFU/g. Of the Vibrio strains identified, 40% were V. alginolyticus, 36% were V. parahaemolyticus, and 24% were nontoxigenic V. cholerae; the latter two species can cause mild to severe forms of seafood-associated gastroenteritis. Among interviewed households, 11.7% reported daily consumption of crustaceans, confirming the high probability of exposure of human population to Vibrio spp., and 7.5% reported symptoms of food poisoning after consumption of crustaceans. The absence of genes encoding major virulence factors in the studied strains, i.e., cholera toxin (ctxA and ctxB) in V. cholerae and thermostable direct hemolysin (tdh) and thermostable direct hemolysin-related hemolysin (trh) in V. parahaemolyticus, does not exclude the possibility of exposure to pathogenic strains. However, human infections are not common because most households (96.7%) boil crustaceans, usually for at least 45 min (85.9% of households) before consumption.     

Towards Zero Carbon Homes in England? From Inception to Partial Implementation

In 2007, the UK Government announced an ambitious zero-carbon target for all new housing in England. This paper shows how the definition and its associated policies emerged from discourses of environmental policy innovation; how the problem subsequently became framed as one of mainstreaming, consequent upon the apparent success of experimental schemes and defined in more detail through the interaction between pressure group politics and the technical analyses that accompanied the government's consultation exercises. Finally, it shows how regional and local variations in housing and property markets are likely to influence the ease of zero carbon development. The analysis uses concepts drawn from both the science and technology literature and the literature on policy implementation.