Linear State Models for Volatility Estimation and Prediction

This report covers the important topic of stochastic volatility modelling with an emphasis on linear state models. The approach taken focuses on comparing models based on their ability to fit the data and their forecasting performance. To this end several parsimonious stochastic volatility models are estimated using realised volatility, a volatility proxy from high frequency stock price data. The results indicate that a hidden state space model performs the best among the realised volatility-based models under consideration. For the state space model different sampling intervals are compared based on in-sample prediction performance. The comparisons are partly based on the multi-period prediction results that are derived in this report

Hydrogen and fuel cell technologies for heating: A review

The debate on low-carbon heat in Europe has become focused on a narrow range of technological options and has largely neglected hydrogen and fuel cell technologies, despite these receiving strong support towards commercialisation in Asia. This review examines the potential benefits of these technologies across different markets, particularly the current state of development and performance of fuel cell micro-CHP. Fuel cells offer some important benefits over other low-carbon heating technologies, and steady cost reductions through innovation are bringing fuel cells close to commercialisation in several countries. Moreover, fuel cells offer wider energy system benefits for high-latitude countries with peak electricity demands in winter. Hydrogen is a zero-carbon alternative to natural gas, which could be particularly valuable for those countries with extensive natural gas distribution networks, but many national energy system models examine neither hydrogen nor fuel cells for heating. There is a need to include hydrogen and fuel cell heating technologies in future scenario analyses, and for policymakers to take into account the full value of the potential contribution of hydrogen and fuel cells to low-carbon energy systems

Signal and Image Manipulation in Microanalysis

Scanning electron microscopes, and transmission instruments equipped for EELS, generate a host of signals and hence of images from each pixel of the specimen. Numerous ingenious ways of coping with this multiplicity of information, which may be very different in character, have been devised, but no detailed study has yet been made of the appropriate mathematical structure, with the aid of which all this information could be manipulated reasonably easily. One such structure falls within the subject that has come to be known as Image Algebra, the principal attraction of which is that we deal directly with entire images and not with individual pixels; the operations involved do of course ultimately take effect at pixel level. Despite its forbidding name, image algebra is intrinsically very simple and has the merit that the notion of image is very general. Images can in particular be multi-valued, that is, a set of values can be associated with every pixel. Indeed, a whole image is associated with each pixel, in the case of the very important class of images known as templates. Image algebra has proved to be an extremely fertile subject, generating many new ideas and especially, revealing several unsuspected relationships between different branches of image and signal processing. The value of this approach will be examined, after a very simple introduction to the basic ideas. The application to image-spectra will be considered as a tangible example. We conclude with some speculations concerning the future of this rich new way of picturing images

Habitat utilization of fish species on the Ohio River: preliminary development of a multi-metric habitat index

Development of a habitat index requires an understanding of the longitudinal distribution of habitat, fish assemblages, and how the two interact. Because of the complexity and size of the Ohio River, this understanding has not been reached. Habitat analysis has long been considered, and is essential, in assigning impaired and reference condition of habitat quality. The Ohio River is diverse in the distribution of its habitat within pool and river-wide. An Analysis of Variance (ANOVA) was used to analyze these distributions. Within pool assessment of % habitat composition revealed woody cover and vegetation types were significantly greater in the lowest quarter of each pool (p whereas river-wide, fine sediment types dominate Distribution of fish species is often Many studies have been 0.05), downstream. dependent on habitat types present, performed to develop a better understanding of the relationship between habitat and fish assemblages in smaller streams, but not in such a dynamic system like that of the Ohio River. In this study, a multi-metric fish assemblage index for large rivers was used to determine the relationship of habitat and fish composition on the Ohio River. Habitat types (sediment, depth, and woody/ vegetation cover) were found to weakly describe fish community variability as much as 19.58% individually (Pearson\u27s correlation analysis) and 25.42% as a composite (stepwise multiple regression) for particular metrics. It was found through analysis this variability was strongly explained by sediment types and depth. The influence of woody cover was minimal as a result of its location in zones assessed. Although the relationships observed were found to be weak, a better understanding of this diverse system\u27s ecology has been made. These discoveries will be useful in the future to develop a predictive model of fish community response to habitat in optimal and degraded conditions

Image Algebra and Restoration

The arrival of image algebras has made it possible to express a vast amount of the heterogeneous material in the image processing literature in a convenient and consistent manner. Furthermore, this type of formulation has proved very fertile and many new ideas have emerged. Image restoration has, however, been studied very much less than enhancement and analysis. We explain briefly the use of one such algebra in this field and show that such tasks as reconstruction from focal series and three-dimensional reconstruction can easily be incorporated

Transmission of epi-alleles with MET1-dependent dense methylation in Arabidopsis thaliana.

DNA methylation in plants targets cytosines in three sequence contexts, CG, CHG and CHH (H representing A, C or T). Each of these patterns has traditionally been associated with distinct DNA methylation pathways with CHH methylation being controlled by the RNA dependent DNA methylation (RdDM) pathway employing small RNAs as a guide for the de novo DOMAINS REARRANGED METHYLTRANSFERASE (DRM2), and maintenance DNA METHYLTRANSFERASE1 (MET1) being responsible for faithful propagation of CG methylation. Here we report an unusual 'dense methylation' pattern under the control of MET1, with methylation in all three sequence contexts. We identified epi-alleles of dense methylation at a non coding RNA locus (At4g15242) in Arabidopsis ecotypes, with distinct dense methylation and expression characteristics, which are stably maintained and transmitted in genetic crosses and which can be heritably altered by depletion of MET1. This suggests that, in addition to its classical CG maintenance function, at certain loci MET1 plays a role in creating transcriptional diversity based on the generation of independent epi-alleles. Database inspection identified several other loci with MET1-dependent dense methylation patterns. Arabidopsis ecotypes contain distinct epi-alleles of these loci with expression patterns that inversely correlate with methylation density, predominantly within the transcribed region. In Arabidopsis, dense methylation appears to be an exception as it is only found at a small number of loci. Its presence does, however, highlight the potential for MET1 as a contributor to epigenetic diversity, and it will be interesting to investigate the representation of dense methylation in other plant species

Temperature dependence of surface stress across an order-disorder transition: p(1x2)O/W(110)

Strain relaxations of a p(1x2) ordered oxygen layer on W(110) are measured as a function of temperature across the disordering transition using low-energy electron diffraction. The measured strains approach values of 0.027 in the [1-10] and -0.053 in the [001] direction. On the basis of the measured strain relaxations, we give quantitative information on temperature-dependent surface stress using the results of ab initio calculations. From the surface formation energy for different strains, determined by first-principles calculations, we estimate that surface stress changes from -1.1 for the ordered phase to -0.2N/m for the disordered one along [1-10], and from 5.1 to 3.4 N/m along [001]. Moreover, our observation that the strains scale inversely with domain size confirms that the strain relaxation takes place at the domain boundaries.Comment: 8 pages, 5 figure

Life cycle environmental impacts of natural gas drivetrains used in UK road freighting and impacts to UK emission targets

Using natural gas as a fuel in the road freight sector instead of diesel could cut greenhouse gas and air quality emissions but the switch alone is not enough to meet UK climate targets. A life cycle assessment (LCA) has been conducted comparing natural gas trucks to diesel, biodiesel, dimethyl ether and electric trucks on impacts to climate change, land use change, air quality, human health and resource depletion. This is the first LCA to consider a full suite of environmental impacts and is the first study to estimate what impact natural gas could have on reducing emissions form the UK freight sector. If LNG is used, climate change impacts could be up to 33% lower per km and up to 12% lower per kWh engine output. However, methane emissions will eliminate any benefits if they exceed 1.5–3.5% of throughput for typical fuel consumption. For non-climate impacts, natural gas exhibits lower emissions (11–66%) than diesel for all indicators. Thus, for natural gas climate benefits are modest. However, emissions of CO, methane and particulate matter are over air quality limits set for UK trucks. Of the other options, electric and biodiesel trucks perform best in climate change, but are the worst with respect to land use change (which could have significant impacts on overall climate change benefits), air quality, human toxicity and metals depletion indicators. Natural gas could help reduce the sector's emissions but deeper decarbonization options are required to meet 2030 climate targets, thus the window for beneficial utilisation is short

Characterising the distribution of methane and carbon dioxide emissions from the natural gas supply chain

Methane and CO2 emissions from the natural gas supply chain have been shown to vary widely but there is little understanding about the distribution of emissions across supply chain routes, processes, regions and operational practises. This study defines the distribution of total methane and CO2 emissions from the natural gas supply chain, identifying the contribution from each stage and quantifying the effect of key parameters on emissions. The study uses recent high-resolution emissions measurements with estimates of parameter distributions to build a probabilistic emissions model for a variety of technological supply chain scenarios. The distribution of emissions resembles a log-log-logistic distribution for most supply chain scenarios, indicating an extremely heavy tailed skew: median estimates which represent typical facilities are modest at 18 – 24 g CO2 eq./ MJ HHV, but mean estimates which account for the heavy tail are 22 – 107 g CO2 eq./ MJ HHV. To place these values into context, emissions associated with natural gas combustion (e.g. for heat) are approximately 55 g CO2/ MJ HHV. Thus, some supply chain scenarios are major contributors to total greenhouse gas emissions from natural gas. For methane-only emissions, median estimates are 0.8 – 2.2% of total methane production, with mean emissions of 1.6 - 5.5%. The heavy tail distribution is the signature of the disproportionately large emitting equipment known as super-emitters, which appear at all stages of the supply chain. The study analyses the impact of different technological options and identifies a set of best technological option (BTO) scenarios. This suggests that emissions-minimising technology can reduce supply chain emissions significantly, with this study estimating median emissions of 0.9% of production. However, even with the emissions-minimising technologies, evidence suggests that the influence of the super-emitters remains. Therefore, emissions-minimising technology is only part of the solution: reducing the impact of super emitters requires more effective detection and rectification, as well as pre-emptive maintenance processes