PEM-West trajectory climatology and photochemical model sensitivity study prepared using retrospective meteorological data

Trajectory and photochemical model calculations based on retrospective meteorological data for the operations areas of the NASA Pacific Exploratory Mission (PEM)-West mission are summarized. The trajectory climatology discussed here is intended to provide guidance for flight planning and initial data interpretation during the field phase of the expedition by indicating the most probable path air parcels are likely to take to reach various points in the area. The photochemical model calculations which are discussed indicate the sensitivity of the chemical environment to various initial chemical concentrations and to conditions along the trajectory. In the post-expedition analysis these calculations will be used to provide a climatological context for the meteorological conditions which are encountered in the field

Characterisation of the mechanical and thermal degradation behaviour of natural fibres for lightweight automotive applications

It is well established that light-weighting of automotive parts leads to reduced carbon emissions over vehicle lifetime. Mineral fibres and fillers have a relatively high density and may require high levels of energy in their production, resulting in a large carbon footprint. Natural fibres have been identified as a potential candidate to substitute mineral fillers in automotive application of thermoplastic matrix composites. This paper focuses on the characterisation of the mechanical and thermal degradation of two types of natural fibres (date palm and coir fibres) as part of an evaluation of their potential for the substitution of high density mineral fillers with more environmentally friendly lower density natural fibre reinforcements

Paving the Way for Development: The Impact of Road Infrastructure on Agricultural Production and Household Wealth in the Democratic Republic of Congo

Given its vast land resources and favorable water supply, the Democratic Republic of Congo's (DRC) natural agricultural potential is immense. However, the economic potential of the sector is handicapped by one of the most dilapidated transport systems in the developing world (World Bank, 2006). Road investments are therefore a high priority in the government's investment plans, and those of its major donors. Whilst these are encouraging signs, very little is known about how the existing road network constrains agricultural and rural development, and how these new road investments would address these constraints. To inform this issue the present paper primarily employs GIS-based data to assess the impact of market access on agricultural and rural development (ARD). Compared to existing work, however, the paper makes a number of innovations to improve and extend the generic techniques used to estimate the importance of market access for ARD. First, the DRC road network data is augmented with survey-based data from Minten and Kyle (1999) on agricultural transport times to calculate improved “market access” measures for the DRC. Second, we follow Dorosh et al (2009) in estimating the long run relationship between market access and agricultural production, although we also investigate the relationship with household wealth. Finally, we run simulations of how proposed infrastructure investments would affect market access, and how market access would in turn affect agricultural production and household wealth.Infrastructure, market access, road and river transport, agricultural production, poverty., Agricultural and Food Policy, Crop Production/Industries, Food Security and Poverty, Production Economics,

A four-lidar view of Cirrus from the FIRE IFO: 27-28 October 1986

The four ground-based lidar systems that participated in the 1986 FIRE IFO were configured in a diamond-shaped array across central and southern Wisconsin. Data were generally collected in the zenith pointing mode, except for the Doppler lidar, which regularly operated in a scanning mode with intermittent zenith observations. As a component of the cirrus case study of 27 and 28 October 1986 selected for initial analysis, data collected by the remote sensor ensemble from 1600 (on the 27th) to 2400 UTC (on the 28th) is described and compared. In general, the cirrus studied on the 27th consisted of intermittent layers of thin and subvisual cirrus clouds. Particularly at Wausau, subvisual cirrus was detected from 11.0 to 11.5 km MSL, just below the tropopause. At lower levels, occasional cirrus clouds between approx. 8.0 to 9.5 km were detected from all ground sites. Preliminary analysis of the four-lidar dataset reveals the passage of surprisingly consistent cloud features across the experiment area. A variety of types and amounts of middle and high level clouds occurred, ranging from subvisual cirrus to deep cloud bands. It is expected that the ground-based lidar measurements from this case study, as well as the airborne observations, will provide an excellent data base for comparison to satellite observations

Bachia heteropa

Number of Pages: 10Integrative BiologyGeological Science

Graph Signal Processing: A Signal Representation Approach to Convolution and Sampling

The paper presents sampling in GSP as 1) linear operations (change of bases) between signal representations and 2) downsampling as linear shift invariant filtering and reconstruction (interpolation) as filtering, both in the spectral domain. To achieve this, it considers a spectral shift $M$ that leads to a spectral graph signal processing theory, $\text{GSP}_{\textrm{sp}}$, dual to GSP but that starts from the spectral domain and $M$. The paper introduces alternative signal representations, convolution of graph signals for these alternative representations, presenting a $\textit{fast}$ GSP convolution that uses the DSP FFT algorithm, and sampling as solutions of algebraic linear systems of equations.Comment: Added missing space in arXiv titl

A Statistical Description of Neural Ensemble Dynamics

The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connections is often intractable because the set of possible interactions grows exponentially with ensemble size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing changes in the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility