Footprints in the sand: a persistent spatial impression of fishing in a mobile groundfish assemblage

Fishing is well known to curtail the size distribution of fish populations. This paper reports the discovery of small-scale spatial patterns in length appearing in several exploited species of Celtic Sea demersal 'groundfish'. These patterns match well with spatial distributions of fishing activity, estimated from vessel monitoring records taken over a period of 6 years, suggesting that this 'mobile' fish community retains a persistent impression of local-scale fishing pressure. An individual random-walk model of fish movement best matched these exploitation 'footprints' with individual movement rates set to <35 km per year. We propose that Celtic Sea groundfish may have surprisingly low movement rates for much of the year, such that fishing impact is spatially heterogeneous and related to local fishing intensity.

The application of the ecological footprint in two Irish urban areas: Limerick and Belfast

The ecological footprint is now a widely accepted indicator of sustainable
development. Footprinting translates resource consumption into the land area
required to sustain it, and allows for an average per capita footprint for a region
or nation to be compared with the global average. This paper reports on a project
in which footprints were calculated for two Irish cities, namely Belfast in
Northern Ireland and Limerick in the Republic of Ireland for the year 2001. As
is frequently the case at sub-national scale, data quality and availability were
often problematic, and in general data gaps were filled by means of population
proxies or national averages. A range of methods was applied to convert
resource flows to land areas. Both footprints suggest that the lifestyles of citizens
of the cities use several times more land than their global share, as has been
found for other cities.

Inference and Declaration of Independence in Task-Parallel Programs

The inherent difficulty of thread-based shared-memory programming has recently motivated research in high-level, task-parallel programming models. Recent advances of Task-Parallel models add implicit synchronization, where the system automatically detects and satisfies data dependencies among spawned tasks. However, dynamic dependence analysis incurs significant runtime overheads, because the runtime must track task resources and use this information to schedule tasks while avoiding conflicts and races.
We present SCOOP, a compiler that effectively integrates static and dynamic analysis in code generation. SCOOP combines context-sensitive points-to, control-flow, escape, and effect analyses to remove redundant dependence checks at runtime. Our static analysis can work in combination with existing dynamic analyses and task-parallel runtimes that use annotations to specify tasks and their memory footprints. We use our static dependence analysis to detect non-conflicting tasks and an existing dynamic analysis to handle the remaining dependencies. We evaluate the resulting hybrid dependence analysis on a set of task-parallel programs.

BDDT: Block-level Dynamic Dependence Analysis for Task-Based Parallelism

We present BDDT, a task-parallel runtime system that dynamically discovers and resolves dependencies among parallel tasks. BDDT allows the programmer to specify detailed task footprints on any memory address range, multidimensional array tile or dynamic region. BDDT uses a block-based dependence analysis with arbitrary granularity. The analysis is applicable to existing C programs without having to restructure object or array allocation, and provides flexibility in array layouts and tile dimensions.
We evaluate BDDT using a representative set of benchmarks, and we compare it to SMPSs (the equivalent runtime system in StarSs) and OpenMP. BDDT performs comparable to or better than SMPSs and is able to cope with task granularity as much as one order of magnitude finer than SMPSs. Compared to OpenMP, BDDT performs up to 3.9× better for benchmarks that benefit from dynamic dependence analysis. BDDT provides additional data annotations to bypass dependence analysis. Using these annotations, BDDT outperforms OpenMP also in benchmarks where dependence analysis does not discover additional parallelism, thanks to a more efficient implementation of the runtime system.

Planning for Renewable Energy: Lessons from the UK’s Devolved Administrations

The planning system has been put forward as a key element in facilitating the low carbon transition (Bulkeley 2006, While 2008), by reducing carbon footprints through initiatives such as encouraging less-energy intensive development, reducing the need to travel or promoting sustainable forms of transport. It has also played a key role on encouraging a shift to more renewable sources of energy, through establishing the spatial ‘rules’ for its regulation, consenting of specific projects and acting as the key arena for mediating a range of social concerns over the resulting socio-technical shift. Despite having this key facilitative role, planning is also regularly seen as a key impediment to renewables, particularly on-shore wind (Ellis et al 2009). There is however, little known about what makes the ‘best’ approach to planning for renewables and indeed little discussion on how to judge the effectiveness of a planning regime for this issue – is it one that maximises generating capacity, protects or landscapes or biodiversity, or perhaps one that maximises social acceptance of renewable developments?

The UK offers a useful context for exploring these issues, with its four main territories (England, Northern Ireland, Scotland and Wales) having broadly similar institutional arrangements, but autonomy over spatial planning during the period in which renewables expanded across the landscape. Each of these jurisdictions has sought to use their planning system to encourage renewables with subtlety different discourses, regulations and spatial strategies. Such an ‘experiment’ offers some important insight into what ‘works’.

This paper will draw on a two year study funded by the UK’s Economic and Social Research Council (RES-062-23-2526), which has charted the effects of devolved administrations on policy and delivery of renewable energy from 1990 to 2012. Drawing on more than 80 interviews, documentary analysis and secondary data sources it describes the growth of renewable capacity in each jurisdiction, explores the spatial strategies adopted and analyses the way in which the broader institutional frameworks in which planning for renewables has emerged. The paper uses this analysis to consider the lessons that can be drawn from the comparable experience of the devolved administrations in the UK and points to the ways in which we should evaluate the effectiveness of planning regimes for renewable energy.

Personalized medicine: Has it started yet? A reconstruction of the early history

Within the last few years the field personalized medicine entered the stage. Accompanied with great hopes and expectations it is believed that this field may have the potential to revolutionize medical and clinical care by utilizing genomics information about the individual patients themselves. In this paper, we reconstruct the early footprints of personalized medicine as reflected by information retrieved from PubMed and Google Scholar. That means we are providing a data-driven perspective of this field to estimate its current status and potential problems.

Young Stellar Object VARiability (YSOVAR): Long Timescale Variations in the Mid-infrared

The YSOVAR (Young Stellar Object VARiability) Spitzer Space Telescope observing program obtained the first extensive mid-infrared (3.6 and 4.5 μm) time series photometry of the Orion Nebula Cluster plus smaller footprints in 11 other star-forming cores (AFGL 490, NGC 1333, Mon R2, GGD 12-15, NGC 2264, L1688, Serpens Main, Serpens South, IRAS 20050+2720, IC 1396A, and Ceph C). There are ~29,000 unique objects with light curves in either or both IRAC channels in the YSOVAR data set. We present the data collection and reduction for the Spitzer and ancillary data, and define the "standard sample" on which we calculate statistics, consisting of fast cadence data, with epochs roughly twice per day for ~40 days. We also define a "standard sample of members" consisting of all the IR-selected members and X-ray-selected members. We characterize the standard sample in terms of other properties, such as spectral energy distribution shape. We use three mechanisms to identify variables in the fast cadence data—the Stetson index, a χ2 fit to a flat light curve, and significant periodicity. We also identified variables on the longest timescales possible of six to seven years by comparing measurements taken early in the Spitzer mission with the mean from our YSOVAR campaign. The fraction of members in each cluster that are variable on these longest timescales is a function of the ratio of Class I/total members in each cluster, such that clusters with a higher fraction of Class I objects also have a higher fraction of long-term variables. For objects with a YSOVAR-determined period and a [3.6]-[8] color, we find that a star with a longer period is more likely than those with shorter periods to have an IR excess. We do not find any evidence for variability that causes [3.6]-[4.5] excesses to appear or vanish within our data set; out of members and field objects combined, at most 0.02% may have transient IR excesses.

Essential scientific mapping of the value chain of thermochemical converted second-generation bio-fuels

As the largest contributor to renewable energy, biomass (especially lignocellulosic biomass) has significant potential to address atmospheric emission and energy shortage issues. The bio-fuels derived from lignocellulosic biomass are popularly referred to as second-generation bio-fuels. To date, several thermochemical conversion pathways for the production of second-generation bio-fuels have shown commercial promise; however, most of these remain at various pre-commercial stages. In view of their imminent commercialization, it is important to conduct a profound and comprehensive comparison of these production techniques. Accordingly, the scope of this review is to fill this essential knowledge gap by mapping the entire value chain of second-generation bio-fuels, from technical, economic, and environmental perspectives. This value chain covers i) the thermochemical technologies used to convert solid biomass feedstock into easier-to-handle intermediates, such as bio-oil, syngas, methanol, and Fischer-Tropsch fuel; and ii) the upgrading technologies used to convert intermediates into end products, including diesel, gasoline, renewable jet fuels, hydrogen, char, olefins, and oxygenated compounds. This review also provides an economic and commercial assessment of these technologies, with the aim of identifying the most adaptable technology for the production of bio-fuels, fuel additives, and bio-chemicals. A detailed mapping of the carbon footprints of the various thermochemical routes to second-generation bio-fuels is also carried out. The review concludes by identifying key challenges and future trends for second-generation petroleum substitute bio-fuels.