Analysis and manipulation of the structure of odor plumes from a piezo-electric release system and measurements of upwind flight of male Almond Moths, Cadra cautella, to pheromone plumes

We investigated the plume structure of a piezo-electric sprayer system, set up to release ethanol in a wind tunnel, using a fast response mini-photoionizaton detector. We recorded the plume structure of four different piezo-sprayer configurations: the sprayer alone; with a 1.6-mm steel mesh shield; with a 3.2-mm steel mesh shield; and with a 5 cm circular upwind baffle. We measured a 12 × 12-mm core at the center of the plume, and both a horizontal and vertical cross-section of the plume, all at 100-, 200-, and 400-mm downwind of the odor source. Significant differences in plume structure were found among all configurations in terms of conditional relative mean concentration, intermittency, ratio of peak concentration to conditional mean concentration, and cross-sectional area of the plume. We then measured the flight responses of the almond moth, Cadra cautella, to odor plumes generated with the sprayer alone, and with the upwind baffle piezo-sprayer configuration, releasing a 13:1 ratio of (9Z,12E)-tetradecadienyl acetate and (Z)-9-tetradecenyl acetate diluted in ethanol at release rates of 1, 10, 100, and 1,000 pg/min. For each configuration, differences in pheromone release rate resulted in significant differences in the proportions of moths performing oriented flight and landing behaviors. Additionally, there were apparent differences in the moths’ behaviors between the two sprayer configurations, although this requires confirmation with further experiments. This study provides evidence that both pheromone concentration and plume structure affect moth orientation behavior and demonstrates that care is needed when setting up experiments that use a piezo-electric release system to ensure the optimal conditions for behavioral observations.

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles

Background: The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. New method: We propose a complete pipeline for the cluster analysis of ERP data. To increase the signalto-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA)to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). Results: After validating the pipeline on simulated data, we tested it on data from two experiments – a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership.

Analysis and modeling of Diacamma workers’ behavior

It is well-known that social insects such as ants show interesting collective behaviors. How do they organize such behaviors? To expand understanding of collective behaviors of social insects, we focused on ants, Diacamma, and analyzed the behavior of a few individuals. In an experimental set-up, ants are placed in hemisphere without a nest and food and the trajectory of ants is recorded. From this bottom-up approach, we found following characteristics: 1. Activity of individuals increases and decreases periodically. 2. Spontaneous meeting process is observed between two ants and meeting spot of two ants is localized in the experimental field.

Global transcriptome analysis and identification of differentially expressed genes after infection of Fragaria vesca with powdery mildew (Podosphaera aphanis)

Background: Podosphaera aphanis, the causal agent of strawberry powdery mildew causes significant economic loss worldwide. Methods: We used the diploid strawberry species Fragaria vesca as a model to study plant pathogen interactions. RNA-seq was employed to generate a transcriptome dataset from two accessions, F. vesca ssp. vesca Hawaii 4 (HW) and F. vesca f. semperflorens Yellow Wonder 5AF7 (YW) at 1 d (1 DAI) and 8 d (8 DAI) after infection. Results: Of the total reads identified about 999 million (92%) mapped to the F. vesca genome. These transcripts were derived from a total of 23,470 and 23,464 genes in HW and YW, respectively from the three time points (control, 1 and 8 DAI). Analysis identified 1,567, 1,846 and 1,145 up-regulated genes between control and 1 DAI, control and 8 DAI, and 1 and 8 DAI, respectively in HW. Similarly, 1,336, 1,619 and 968 genes were up-regulated in YW. Also 646, 1,098 and 624 down-regulated genes were identified in HW, while 571, 754 and 627 genes were down-regulated in YW between all three time points, respectively. Conclusion: Investigation of differentially expressed genes (log2 fold changes �5) between control and 1 DAI in both HW and YW identified a large number of genes related to secondary metabolism, signal transduction; transcriptional regulation and disease resistance were highly expressed. These included flavonoid 3´-monooxygenase, peroxidase 15, glucan endo-1,3-β-glucosidase 2, receptor-like kinases, transcription factors, germin-like proteins, F-box proteins, NB-ARC and NBS-LRR proteins. This is the first application of RNA-seq to any pathogen interaction in strawberry

Multi-criteria analysis and the resolution of sustainable development dilemmas: a stakeholder management approach

We demonstrate that stakeholder-oriented multi-criteria analysis (MCA) can adequately address a variety of sustainable development dilemmas in decision-making, especially when applied to complex project evaluations involving multiple objectives and multiple stakeholder groups. Such evaluations are typically geared towards satisfying simultaneously private economic goals, broader social objectives and environmental targets. We show that, under specific conditions, a variety of stakeholder-oriented MCA approaches may be able to contribute substantively to the resolution or improved governance of societal conflicts and the pursuit of the public good in the form of sustainable development. We contrast the potential usefulness of these stakeholder-oriented approaches – in terms of their ability to contribute to sustainable development – with more conventional MCA approaches and social cost–benefit analysis.

Towards a meta-analysis and toolkit for port-related socio-economic impacts: a review of socio-economic impact studies conducted for seaports

Port authorities increasingly need to communicate with a variety of external stakeholders in order to maintain and strengthen the societal acceptance of seaport activities. The availability of socio-economic impact studies on port authority and regional development agency websites has often made this information accessible to the public at large. However, the differences in methodologies adopted, in terms of selecting, defining and measuring various types of socio-economic impacts, sometimes lead to misconceptions as well as misleading comparisons across ports within and between regions. In this paper, we suggest guidelines for the design and application of a potential best practice from an interregional perspective (UK, France and Belgium), based on research in the framework of a European Commission co-funded project, ‘IMPACTE’. The paper also aims to develop guidelines for comparing the socio-economic impacts of ports across regional and national borders and discusses the development of a European port economic impact measurement toolkit. We analyse a sample of 33 recent socio-economic impact assessment reports in terms of methodologies adopted and types of impacts measured. The review shows a great diversity among these studies, leading to important differences between the impacts of port activity communicated to stakeholders.

Spectral analysis and the Aharonov-Bohm\~ effect on certain almost-Riemannian manifold

We study spectral properties of the Laplace-Beltrami operator on two relevant almost-Riemannian manifolds, namely the Grushin structures on the cylinder and on the sphere. This operator contains first order diverging terms caused by the divergence of the volume. We get explicit descriptions of the spectrum and the eigenfunctions. In particular in both cases we get a Weyl's law with leading term Elog E. We then study the drastic effect of Aharonov-Bohm magnetic potentials on the spectral properties. Other generalised Riemannian structures including conic and anti-conic type manifolds are also studied. In this case, the Aharonov-Bohm magnetic potential may affect the self-adjointness of the Laplace-Beltrami operator.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

Impact of cyclones on sea ice in the Fram Strait: numerical simulations and observations

The sea ice export from the Arctic is of global importance due to its fresh water which influences the oceanic stratification and, thus, the global thermohaline circulation. This study deals with the effect of cyclones on sea ice and sea ice transport in particular on the basis of observations from two field experiments FRAMZY 1999 and FRAMZY 2002 in April 1999 and March 2002 as well as on the basis of simulations with a numerical sea ice model. The simulations realised by a dynamic-thermodynamic sea ice model are forced with 6-hourly atmospheric ECMWF- analyses (European Centre for Medium-Range Weather Forecasts) and 6-hourly oceanic data of a MPI-OM-simulation (Max-Planck-Institute Ocean Model). Comparing the observed and simulated variability of the sea ice drift and of the position of the ice edge shows that the chosen configuration of the model is appropriate for the performed studies. The seven observed cyclones change the position of the ice edge up to 100 km and cause an extensive decrease of sea ice coverage by 2 % up to more than 10 %. The decrease is only simulated by the model if the ocean current is strongly divergent in the centre of the cyclone. The impact is remarkable of the ocean current on divergence and shear deformation of the ice drift. As shown by sensitivity studies the ocean current at a depth of 6 m – the sea ice model is forced with – is mainly responsible for the ascertained differences between simulation and observation. The simulated sea ice transport shows a strong variability on a time scale from hours to days. Local minima occur in the time series of the ice transport during periods with Fram Strait cyclones. These minima are not caused by the local effect of the cyclone’s wind field, but mainly by the large-scale pattern of surface pressure. A displacement of the areas of strongest cyclone activity in the Nordic Seas would considerably influence the ice transport.

Kinetics analysis and automated online screening of aminocarbonylation of aryl halides in flow

Temperature, pressure, gas stoichiometry, and residence time were varied to control the yield and product distribution of the palladium-catalyzed aminocarbonylation of aromatic bromides in both a silicon microreactor and a packed-bed tubular reactor. Automation of the system set points and product sampling enabled facile and repeatable reaction analysis with minimal operator supervision. It was observed that the reaction was divided into two temperature regimes. An automated system was used to screen steady-state conditions for offline analysis by gas chromatography to fit a reaction rate model. Additionally, a transient temperature ramp method utilizing online infrared analysis was used, leading to more rapid determination of the reaction activation energy of the lower temperature regimes. The entire reaction spanning both regimes was modeled in good agreement with the experimental data.