Patterns of Holocene relative sea level change in the North of Britain and Ireland

Temporal and spatial patterns of relative sea level (RSL) change in the North of Britain and Ireland during the Holocene are examined. Four episodes, each defined by marked changes in the RSL trend, are identified. Each episode is marked by a rise to a culminating shoreline followed by a fall. Episode HRSL-1 dates from the Younger Dryas to early in the Holocene; HRSL-2 to HRSL-4 occurred later in the Holocene. There is extensive evidence for each episode, and on this basis the spatial distribution of the altitude data for three culminating shorelines and a shoreline formed at the time of the Holocene Storegga Slide tsunami (ca 8110 ± 100 cal. BP) is analysed. Ordinary Kriging is used to determine the general pattern, following which Gaussian Trend Surface Analysis is employed. Recognising that empirical measurements of RSL change can be unevenly distributed spatially, a new approach is introduced which enables the developing pattern to be identified. The patterns for the most widely occurring shorelines were analysed and found to be similar and common centre and axis models were developed for all shorelines. The analyses described provide models of the spatial pattern of Holocene RSL change in the area between ca 8100 cal. BP and ca 1000 cal. BP based on 2262 high resolution shoreline altitude measurements. These models fit the data closely, no shoreline altitude measurement lying more than −1.70 m or +1.82 m from the predicted value. The models disclose a similar pattern to a recently published Glacial Isostatic Adjustment model for present RSL change across the area, indicating that the overall spatial pattern of RSL change may not have varied greatly during the last ca 8000 years.

Three dimensional analysis of the lamina cribrosa in glaucoma

Background/aim: Structural changes in the lamina cribrosa have been implicated in the pathogenesis of glaucomatous optic atrophy. The aim of this study was to determine a measure the surface variability of the cup floor in normal subjects and patients with glaucoma. Methods: A sample of age matched normal subjects (NN), patients with low tension glaucoma (LTG), and primary open angle glaucoma (POAG) were included in the study. The glaucoma groups were matched for the severity of the visual field loss. Mean 10 degree topographic images of normal and glaucomatous eyes from the Heidelberg retina tomograph were imported into ERDAS image processing software where topographic analysis of the cup floor could be assessed. Each image was processed using customised spatial filters that calculated the surface depth variation in localised neighbourhood areas across each image. The local change in depth across the cup floor surface was determined and compared between the three clinical groups. Results: The depth variation in the cup floor was largest in normal subjects followed by LTG and POAG. Highly statistically significant differences in surface depth variability of the cup floor existed between normal and LTG (p=0.005), between normal and POAG (p

Relative role of deterministic and stochastic determinants of soil animal community: a spatially explicit analysis of oribatid mites

1. Ecologists are debating the relative role of deterministic and stochastic determinants of community structure. Although the high diversity and strong spatial structure of soil animal assemblages could provide ecologists with an ideal ecological scenario, surprisingly little information is available on these assemblages.
2. We studied species-rich soil oribatid mite assemblages from a Mediterranean beech forest and a grassland. We applied multivariate regression approaches and analysed spatial autocorrelation at multiple spatial scales using Moran's eigenvectors. Results were used to partition community variance in terms of the amount of variation uniquely accounted for by environmental correlates (e.g. organic matter) and geographical position. Estimated neutral diversity and immigration parameters were also applied to a soil animal group for the first time to simulate patterns of community dissimilarity expected under neutrality, thereby testing neutral predictions.
3. After accounting for spatial autocorrelation, the correlation between community structure and key environmental parameters disappeared: about 40% of community variation consisted of spatial patterns independent of measured environmental variables such as organic matter. Environmentally independent spatial patterns encompassed the entire range of scales accounted for by the sampling design (from tens of cm to 100 m). This spatial variation could be due to either unmeasured but spatially structured variables or stochastic drift mediated by dispersal. Observed levels of community dissimilarity were significantly different from those predicted by neutral models.
4. Oribatid mite assemblages are dominated by processes involving both deterministic and stochastic components and operating at multiple scales. Spatial patterns independent of the measured environmental variables are a prominent feature of the targeted assemblages, but patterns of community dissimilarity do not match neutral predictions. This suggests that either niche-mediated competition or environmental filtering or both are contributing to the core structure of the community. This study indicates new lines of investigation for understanding the mechanisms that determine the signature of the deterministic component of animal community assembly.

An Examination of Transformation Techniques to Investigate and Interpret Multivariate Geochemical Data Analysis: Tellus Case Study

This research aims to use the multivariate geochemical dataset, generated by the Tellus project, to investigate the appropriate use of transformation methods to maintain the integrity of geochemical data and inherent constrained behaviour in multivariate relationships. The widely used normal score transform is compared with the use of a stepwise conditional transform technique. The Tellus Project, managed by GSNI and funded by the Department of Enterprise Trade and Development and the EU’s Building Sustainable Prosperity Fund, involves the most comprehensive geological mapping project ever undertaken in Northern Ireland. Previous study has demonstrated spatial variability in the Tellus data but geostatistical analysis and interpretation of the datasets requires use of an appropriate methodology that reproduces the inherently complex multivariate relations. Previous investigation of the Tellus geochemical data has included use of Gaussian-based techniques. However, earth science variables are rarely Gaussian, hence transformation of data is integral to the approach. The multivariate geochemical dataset generated by the Tellus project provides an opportunity to investigate the appropriate use of transformation methods, as required for Gaussian-based geostatistical analysis. In particular, the stepwise conditional transform is investigated and developed for the geochemical datasets obtained as part of the Tellus project. The transform is applied to four variables in a bivariate nested fashion due to the limited availability of data. Simulation of these transformed variables is then carried out, along with a corresponding back transformation to original units. Results show that the stepwise transform is successful in reproducing both univariate statistics and the complex bivariate relations exhibited by the data. Greater fidelity to multivariate relationships will improve uncertainty models, which are required for consequent geological, environmental and economic inferences.

Soil Moisture Deficit as a predictor of the trend in soil water status of grass fields

Nutrient loss from agricultural land following organic fertilizer spreading can lead to eutrophication and poor water quality. The risk of pollution is partly related to the soil water status during and after spreading. In response to these issues, a decision support system (DSS) for nutrient management has been developed to predict when soil and weather conditions are suitable for slurry spreading. At the core of the DSS, the Hybrid Soil Moisture Deficit (HSMD) model estimates soil water status relative to field capacity (FC) for three soil classes (well, moderately and poorly drained) and has potential to predict the occurrence of a transport vector when the soil is wetter than FC. Three years of field observation of volumetric water content was used to validate HSMD model predictions of water status and to ensure correct use and interpretation of the drainage classes. Point HSMD model predictions were validated with respect to the temporal and spatial variations in volumetric water content and soil strength properties. It was found that the HSMD model predictions were well related to topsoil water content through time, but a new class intermediate between poor and moderate, perhaps ‘imperfectly drained’, was needed. With correct allocations of a field into a drainage class, the HSMD model predictions reflect field scale trends in water status and therefore the model is suitable for use at the core of a DSS.

Application of factor analysis and geostatistics to identify processes controlling the distribution and bioaccessibility of trace elements in Northern Ireland soils

Geologic and environmental factors acting over varying spatial scales can control
trace element distribution and mobility in soils. In turn, the mobility of an element in soil will affect its oral bioaccessibility. Geostatistics, kriging and principal component analysis (PCA) were used to explore factors and spatial ranges of influence over a suite of 8 element oxides, soil organic carbon (SOC), pH, and the trace elements nickel (Ni), vanadium (V) and zinc (Zn). Bioaccessibility testing was carried out previously using the Unified BARGE Method on a sub-set of 91 soil samples from the Northern Ireland Tellus1 soil archive. Initial spatial mapping of total Ni, V and Zn concentrations shows their distributions are correlated spatially with local geologic formations, and prior correlation analyses showed that statistically significant controls were exerted over trace element bioaccessibility by the 8 oxides, SOC and pH. PCA applied to the geochemistry parameters of the bioaccessibility sample set yielded three principal components accounting for 77% of cumulative variance in the data
set. Geostatistical analysis of oxide, trace element, SOC and pH distributions using 6862 sample locations also identified distinct spatial ranges of influence for these variables, concluded to arise from geologic forming processes, weathering processes, and localised soil chemistry factors. Kriging was used to conduct a spatial PCA of Ni, V and Zn distributions which identified two factors comprising the majority of distribution variance. This was spatially accounted for firstly by basalt rock types, with the second component associated with sandstone and limestone in the region. The results suggest trace element bioaccessibility and distribution is controlled by chemical and geologic processes which occur over variable spatial ranges of influence.

Evidence and Spatial Planning for Sustainable Development: A Model for Spatially Allocating Material Flows

The aspiration the spatial planning should act as the main coordinating function for the transition to a sustainable society is grounded on the assumption that it is capable of incorporating both a strong evidence base of environmental accounting for policy, coupled with opportunities for open, deliberative decision-making. While there are a number of increasingly sophisticated methods (such as material flow analysis and ecological footprinting) that can be used to longitudinally determine the impact of policy, there are fewer that can provide a robust spatial assessment of sustainability policy. In this paper, we introduce the Spatial Allocation of Material Flow Analysis (SAMFA) model, which uses the concept of socio-economic metabolism to extrapolate the impact of local consumption patterns that may occur as a result of the local spatial planning process at multiple spatial levels. The initial application the SAMFA model is based on County Kildare in the Republic of Ireland, through spatial temporal simulation and visualisation of construction material flows and associated energy use in the housing sector. Thus, while we focus on an Ireland case study, the model is applicable to spatial planning and sustainability research more generally. Through the development and evaluation of alternative scenarios, the model appears to be successful in its prediction of the cumulative resource and energy impacts arising from consumption and development patterns. This leads to some important insights in relation to the differential spatial distribution of disaggregated allocation of material balance and energy use, for example that rural areas have greater resource accumulation (and are therefore in a sense “less sustainable”) than urban areas, confirming that rural housing in Ireland is both more material and energy intensive. This therefore has the potential to identify hotspots of higher material and energy use, which can be addressed through targeted planning initiatives or focussed community engagement. Furthermore, due to the ability of the model to allow manipulation of different policy criteria (increased density, urban conservation etc), it can also act as an effective basis for multi-stakeholder engagement.

Threshold-based substream selection for closed-loop spatial multiplexing

We propose a low-complexity closed-loop spatial multiplexing method with limited feedback over multi-input-multi-output (MIMO) fading channels. The transmit adaptation is simply performed by selecting transmit antennas (or substreams) by comparing their signal-to-noise ratios to a given threshold with a fixed nonadaptive constellation and fixed transmit power per substream. We analyze the performance of the proposed system by deriving closed-form expressions for spectral efficiency, average transmit power, and bit error rate (BER). Depending on practical system design constraints, the threshold is chosen to maximize the spectral efficiency (or minimize the average BER) subject to average transmit power and average BER (or spectral efficiency) constraints, respectively. We present numerical and Monte Carlo simulation results that validate our analysis. Compared to open-loop spatial multiplexing and other approaches that select the best antenna subset in spatial multiplexing, the numerical results illustrate that the proposed technique obtains significant power gains for the same BER and spectral efficiency. We also provide numerical results that show improvement over rate-adaptive orthogonal space-time block coding, which requires highly complex constellation adaptation. We analyze the impact of feedback delay using analytical and Monte Carlo approaches. The proposed approach is arguably the simplest possible adaptive spatial multiplexing system from an implementation point of view. However, our approach and analysis can be extended to other systems using multiple constellations and power levels.

An analysis of the masking of speech by competing speech using self-report data (L)

Many of the items in the “Speech, Spatial, and Qualities of Hearing” scale questionnaire [S. Gatehouse and W. Noble, Int. J. Audiol.43, 85–99 (2004)] are concerned with speech understanding in a variety of backgrounds, both speech and nonspeech. To study if this self-report data reflected informational masking, previously collected data on 414 people were analyzed. The lowest scores (greatest difficulties) were found for the two items in which there were two speech targets, with successively higher scores for competing speech (six items), energetic masking (one item), and no masking (three items). The results suggest significant masking by competing speech in everyday listening situations.

Analysis of Dependence Tracking Algorithms for Task Dataflow Execution

Processor architectures has taken a turn towards many-core processors, which integrate multiple processing cores on a single chip to increase overall performance, and there are no signs that this trend will stop in the near future. Many-core processors are harder to program than multi-core and single-core processors due to the need of writing parallel or concurrent programs with high degrees of parallelism. Moreover, many-cores have to operate in a mode of strong scaling because of memory bandwidth constraints. In strong scaling increasingly finer-grain parallelism must be extracted in order to keep all processing cores busy.

Task dataflow programming models have a high potential to simplify parallel program- ming because they alleviate the programmer from identifying precisely all inter-task de- pendences when writing programs. Instead, the task dataflow runtime system detects and enforces inter-task dependences during execution based on the description of memory each task accesses. The runtime constructs a task dataflow graph that captures all tasks and their dependences. Tasks are scheduled to execute in parallel taking into account dependences specified in the task graph.

Several papers report important overheads for task dataflow systems, which severely limits the scalability and usability of such systems. In this paper we study efficient schemes to manage task graphs and analyze their scalability. We assume a programming model that supports input, output and in/out annotations on task arguments, as well as commutative in/out and reductions. We analyze the structure of task graphs and identify versions and generations as key concepts for efficient management of task graphs. Then, we present three schemes to manage task graphs building on graph representations, hypergraphs and lists. We also consider a fourth edge-less scheme that synchronizes tasks using integers. Analysis using micro-benchmarks shows that the graph representation is not always scalable and that the edge-less scheme introduces least overhead in nearly all situations.

Vitamin D and mortality: meta-analysis of individual participant data from a large consortium of cohort studies from Europe and the United States

Objective: To investigate the association between serum 25-hydroxyvitamin D concentrations (25(OH)D) and mortality in a large consortium of cohort studies paying particular attention to potential age, sex, season, and country differences.

Design: Meta-analysis of individual participant data of eight prospective cohort studies from Europe and the US.

Setting: General population.

Participants: 26 018 men and women aged 50-79 years

Main outcome measures: All-cause, cardiovascular, and cancer mortality.

Results: 25(OH)D concentrations varied strongly by season (higher in summer), country (higher in US and northern Europe) and sex (higher in men), but no consistent trend with age was observed. During follow-up, 6695 study participants died, among whom 2624 died of cardiovascular diseases and 2227 died of cancer. For each cohort and analysis, 25(OH)D quintiles were defined with cohort and subgroup specific cut-off values. Comparing bottom versus top quintiles resulted in a pooled risk ratio of 1.57 (95% CI 1.36 to 1.81) for all-cause mortality. Risk ratios for cardiovascular mortality were similar in magnitude to that for all-cause mortality in subjects both with and without a history of cardiovascular disease at baseline. With respect to cancer mortality, an association was only observed among subjects with a history of cancer (risk ratio, 1.70 (1.00 to 2.88)). Analyses using all quintiles suggest curvilinear, inverse, dose-response curves for the aforementioned relationships. No strong age, sex, season, or country specific differences were detected. Heterogeneity was low in most meta-analyses.

Conclusions: Despite levels of 25(OH)D strongly varying with country, sex, and season, the association between 25(OH)D level and all-cause and cause-specific mortality was remarkably consistent. Results from a long term randomised controlled trial addressing longevity are being awaited before vitamin D supplementation can be recommended in most individuals with low 25(OH)D levels.

A long-term analysis of pumped hydro storage to firm wind power

Although pumped hydro storage is seen as a strategic key asset by grid operators, financing it is complicated in new liberalised markets. It could be argued that the optimum generation portfolio is now determined by the economic viability of generators based on a short to medium term return on investment. This has meant that capital intensive projects such as pumped hydro storage are less attractive for wholesale electricity companies because the payback periods are too long. In tandem a significant amount of wind power has entered the generation mix, which has resulted in operating and planning integration issues due to wind's inherent uncertain, varying spatial and temporal nature. These integration issues can be overcome using fast acting gas peaking plant or energy storage. Most analysis of wind power integration using storage to date has used stochastic optimisation for power system balancing or arbitrage modelling to examine techno-economic viability. In this research a deterministic dynamic programming long term generation expansion model is employed to optimise the generation mix, total system costs and total carbon dioxide emissions, and unlike other studies calculates reserve to firm wind power. The key finding of this study is that the incentive to build capital-intensive pumped hydro storage to firm wind power is limited unless exogenous market costs come very strongly into play. Furthermore it was demonstrated that reserve increases with increasing wind power showing the importance of ancillary services in future power systems. © 2014 Elsevier Ltd. All rights reserved.

Kinetostatic-model-based Stiffness Analysis of Exechon PKM

As a comparative newly-invented PKM with over-constraints in kinematic chains, the Exechon has attracted extensive attention from the research society. Different from the well-recognized kinematics analysis, the research on the stiffness characteristics of the Exechon still remains as a challenge due to the structural complexity. In order to achieve a thorough understanding of the stiffness characteristics of the Exechon PKM, this paper proposed an analytical kinetostatic model by using the substructure synthesis technique. The whole PKM system is decomposed into a moving platform subsystem, three limb subsystems and a fixed base subsystem, which are connected to each other sequentially through corresponding joints. Each limb body is modeled as a spatial beam with a uniform cross-section constrained by two sets of lumped springs. The equilibrium equation of each individual limb assemblage is derived through finite element formulation and combined with that of the moving platform derived with Newtonian method to construct the governing kinetostatic equations of the system after introducing the deformation compatibility conditions between the moving platform and the limbs. By extracting the 6 x 6 block matrix from the inversion of the governing compliance matrix, the stiffness of the moving platform is formulated. The computation for the stiffness of the Exechon PKM at a typical configuration as well as throughout the workspace is carried out in a quick manner with a piece-by-piece partition algorithm. The numerical simulations reveal a strong position-dependency of the PKM's stiffness in that it is symmetric relative to a work plane due to structural features. At the last stage, the effects of some design variables such as structural, dimensional and stiffness parameters on system rigidity are investigated with the purpose of providing useful information for the structural optimization and performance enhancement of the Exechon PKM. It is worthy mentioning that the proposed methodology of stiffness modeling in this paper can also be applied to other overconstrained PKMs and can evaluate the global rigidity over workplace efficiently with minor revisions.

Evaluation of an in situ spatial resolution instrument for fixed beds through the assessment of the invasiveness of probes and a comparison with a micro-kinetic model

This paper reports the detailed description and validation of a fully automated, computer controlled analytical method to spatially probe the gas composition and thermal characteristics in packed bed systems. This method has been designed to limit the invasiveness of the probe, a characteristic assessed using CFD. The thermocouple is aligned with the sampling holes to enable simultaneous recording of the gas composition and temperature profiles. This analysis technique has been validated by studying CO oxidation over a 1% Pt/Al2O3 catalyst. The resultant profiles have been compared with a micro-kinetic model, to further assess the strength of the technique. 

A statistical analysis of person–to–vehicle communications channels in an urban environment at 5.8 GHz

In recent years, the embracement of smart devices carried or worn by people have transformed how society interact with one another. This trend has also been observed in the advancement of vehicular networks. Here, developments in wireless technologies for vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communications are leading to a new generation of vehicular networks. A natural extension of both types of networks will be their eventual wireless integration. Both people and vehicles will undoubtedly form integral parts of future mobile networks of people and things. Central to this will be the person-to-vehicle (P2V) communications channel. As the P2V channel will be subject to different signal propagation characteristics than either type of communication system considered in isolation, it is imperative the characteristics of the wireless channel must first be fully understood. To the best of the author's knowledge, this is a topic which has not yet been addressed in the open literature. In this paper we will present our most recent research on the statistical characterization of the 5.8 GHz person-to-vehicle channel in an urban environment.