Deep weathering of calcareous sedimentary rock and the redistribution of iron and manganese in soil and saprolite

Iron and Mn redistribute in soil and saprolite during weathering. The geological weathering fronts ofcalcareous sedimentary rock were investigated by examining the bulk density, porosity, and distribution ofCa, Fe, and Mn. Core samples were taken ofsoil, saprolite, and bedrock material from both summit (HHMS-4B) and sideslope (HHMS-5A) positions on an interbedded Nolichucky shale and Maryville limestone landform in Solid Waste Storage Area 6 (SWSA-6). This is a low-level radioactive solids waste disposal site on the Dept. ofEnergy (DOE) Oak Ridge Reservation in Roane County Tennessee. This work was initiated because data about the properties of highly weathered sedimentary rock on this site were limited. The core samples were analyzed for pH, calcium carbonate equivalence (CCE), hydroxylamine-extractable (HA) Mn, and dithionite-citrate (CBD)-extractable Fe and Mn. Low pH values occurred from the soil surface down to the depth of the oxidized and leached saprolite in both cores. The CCE and HA-extractable Mn results were also influenced by the weathering that has occurred in these zones. Extractable Mn oxide was higher at a lower depth in the oxidized and leached saprolite compared with the Fe oxide, which was higher in the overlying soil solum. Amounts of Mn oxides were higher in the sideslope core (HHMS-5A) than in the summit core (HHMS-4B). Iron was more abundant in the deeper weathered summit core, but the highest value, 39.4 g kg-1, was found at 1.8 to 2.4 m in the sideslope core. The zone encompassing the oxidized and partially leached saprolite down to the unoxidized and unleached bedrock had higher densities and larger quantities of CaCO3 than the soil solum and oxidized and leached saprolite. The overlying soil and oxidized and leached saprolite had lower pH and CCE values and were higher in Fe and Mn oxides than the oxidized and unleached saprolite. The distribution of Fe and Mn is important when evaluating soil and saprolite for hazardous waste disposal site assessment.

Larval Transport Modeling of Deep-Sea Invertebrates Can Aid the Search for Undiscovered Populations

Background: Many deep-sea benthic animals occur in patchy distributions separated by thousands of kilometres, yet because deep-sea habitats are remote, little is known about their larval dispersal. Our novel method simulates dispersal by combining data from the Argo array of autonomous oceanographic probes, deep-sea ecological surveys, and comparative invertebrate physiology. The predicted particle tracks allow quantitative, testable predictions about the dispersal of benthic invertebrate larvae in the south-west Pacific. Principal Findings: In a test case presented here, using non-feeding, non-swimming (lecithotrophic trochophore) larvae of polyplacophoran molluscs (chitons), we show that the likely dispersal pathways in a single generation are significantly shorter than the distances between the three known population centres in our study region. The large-scale density of chiton populations throughout our study region is potentially much greater than present survey data suggest, with intermediate ‘stepping stone’ populations yet to be discovered. Conclusions/Significance: We present a new method that is broadly applicable to studies of the dispersal of deep-sea organisms. This test case demonstrates the power and potential applications of our new method, in generating quantitative, testable hypotheses at multiple levels to solve the mismatch between observed and expected distributions: probabilistic predictions of locations of intermediate populations, potential alternative dispersal mechanisms, and expected population genetic structure. The global Argo data have never previously been used to address benthic biology, and our method can be applied to any non-swimming larvae of the deep-sea, giving information upon dispersal corridors and population densities in habitats that remain intrinsically difficult to assess.

Seasonal variation in month of diagnosis in children with type 1 diabetes registered in 23 EURODIAB centres during 1989-2008

Objectives: To investigate seasonal variation in month of diagnosis in children with type 1 diabetes registered in EURODIAB centres during 1989-2008.
Methods: 23 population-based registers recorded date of diagnosis in new cases of clinically diagnosed type 1 diabetes in children aged under 15 years. Completeness of ascertainment was assessed through capture-recapture methodology and was high in most centres. A general test for seasonal variation (11df) and Edward's test for sinusoidal (sine wave) variation (2df) were employed. Time series methods were also used to investigate if meteorological data were predictive of monthly counts after taking account of seasonality and long term trends.
Results: Significant seasonal variation was apparent in all but two small centres, with an excess of cases apparent in the winter quarter. Significant sinusoidal pattern was also evident in all but two small centres with peaks in December (14 centres), January (5 centres) or February (2 centres). Relative amplitude varied from ±11% to ±39% (median ±18%). There was no relationship across the centres between relative amplitude and incidence level. However there was evidence of significant deviation from the sinusoidal pattern in the majority of centres. Pooling results over centres, there was significant seasonal variation in each age-group at diagnosis, but with significantly less variation in those aged under 5 years. Boys showed marginally greater seasonal variation than girls. There were no differences in seasonal pattern between four sub-periods of the 20 year period. In most centres monthly counts of cases were not associated with deviations from normal monthly average temperature or sunshine hours; short term meteorological variations do not explain numbers of cases diagnosed.
Conclusions: Seasonality with a winter excess is apparent in all age-groups and both sexes, but girls and the under 5s show less marked variation. The seasonal pattern changed little in the 20 year period.

An Evidential Approach to Query Interface Matching on the Deep Web

Matching query interfaces is a crucial step in data integration across multiple Web databases. The problem is closely related to schema matching that typically exploits different features of schemas. Relying on a particular feature of schemas is not suffcient. We propose an evidential approach to combining multiple matchers using Dempster-Shafer theory of evidence. First, our approach views the match results of an individual matcher as a source of evidence that provides a level of confidence on the validity of each candidate attribute correspondence. Second, it combines multiple sources of evidence to get a combined mass function that represents the overall level of confidence, taking into account the match results of different matchers. Our combination mechanism does not require use of weighing parameters, hence no setting and tuning of them is needed. Third, it selects the top k attribute correspondences of each source attribute from the target schema based on the combined mass function. Finally it uses some heuristics to resolve any conflicts between the attribute correspondences of different source attributes. Our experimental results show that our approach is highly accurate and effective.

Intellectual Property and the EU’s Deep Trade Agenda

The EU is considered to be one of the main proponents of what has been called the deep trade agenda—that is, the push for further trade liberalization with an emphasis on the removal of domestic non-tariff regulatory measures affecting trade, as opposed to the traditional focus on the removal of trade barriers at borders. As negotiations on the Doha Development Round have stalled, the EU has attempted to achieve these aims by entering into comprehensive free trade agreements (FTAs) that are not only limited exclusively to tariffs but also extend to non-tariff barriers, including services, intellectual property rights (IPRs), competition, and investment. These FTAs place great emphasis on regulatory convergence as a means to secure greater market openings. The paper examines the EU's current external trade policy in the area of IP, particularly its attempts to promote its own regulatory model for the protection of IP rights through trade agreements. By looking at the IP enforcement provisions of such agreements, the article also examines how the divisive issues that are currently hindering the progress of negotiations at WTO level, including the demands from developing countries to maintain a degree of autonomy in the area of IP regulation as well as the need to balance IP protection with human rights protection, are being dealt with in recent EU FTAs.

Sea-level changes in Iceland and the influence of the North Atlantic Oscillation during the last half millennium

We present a new, diatom-based sea-level reconstruction for Iceland spanning the last -500 years, and investigate the possible mechanisms driving the sea-level changes. A sea-level reconstruction from near the Icelandic low pressure system is important as it can improve understanding of ocean-atmosphere forcing on North Atlantic sea-level variability over multi-decadal to centennial timescales. Our reconstruction is from Viarhólmi salt marsh in Snæfellsnes in western Iceland, a site from where we previously obtained a 2000-yr record based upon less precise sea-level indicators (salt-marsh foraminifera). The 20th century part of our record is corroborated by tide-gauge data from Reykjavik. Overall, the new reconstruction shows ca0.6m rise of relative sea level during the last four centuries, of which ca0.2m occurred during the 20th century. Low-amplitude and high-frequency sea-level variability is super-imposed on the pre-industrial long-term rising trend of 0.65m per 1000 years. Most of the relative sea-level rise occurred in three distinct periods: AD 1620-1650, AD 1780-1850 and AD 1950-2000, with maximum rates of ~3±2mm/yr during the latter two of these periods. Maximum rates were achieved at the end of large shifts (from negative to positive) of the winter North Atlantic Oscillation (NAO) Index as reconstructed from proxy data. Instrumental data demonstrate that a strong and sustained positive NAO (a deep Icelandic Low) generates setup on the west coast of Iceland resulting in rising sea levels. There is no strong evidence that the periods of rapid sea-level rise were caused by ocean mass changes, glacial isostatic adjustment or regional steric change. We suggest that wind forcing plays an important role in causing regional-scale coastal sea-level variability in the North Atlantic, not only on (multi-)annual timescales, but also on multi-decadal to centennial timescales.

Development of an app to support learning in A-level maths

This project involved producing an app for smart devices to enable modernised learning for A-level maths students. Research in a stakeholder school showed that 94% of pupils surveyed within the upper-secondary level owned a smartphone and most owned a tablet also, emphasising the opportunity for using apps to support learning. The app was developed using iBuildApp, an online app-creation programme which requires no programming. Past exam questions and solutions, notes and video tutorials were included and the topic was vectors, identified by teachers as problematic. Pupils generally found the app easy to use and wanted further development. The videos were popular despite this not ranking highly as a preferred method of revision previously. Teachers were happy for pupils to use the app to supplement their learning, both in the classroom and outside.

A Lightweight Tool for Anomaly Detection in Cloud Data Centres

Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribu- tion, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient corre- lation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomaly is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly.

Analysis of groundwater-level response to rainfall and recharge estimates in fractured hard rock aquifers, NW Ireland

Despite fractured hard rock aquifers underlying over 65% of Ireland, knowledge of key processes controlling groundwater recharge in these bedrock systems is inadequately constrained. In this study, we examined 19 groundwater-level hydrographs from two Irish hillslope sites underlain by hard rock aquifers. Water-level time-series in clustered monitoring wells completed at the subsoil, soil/bedrock interface, shallow and deep bedrocks were continuously monitored hourly over two hydrological years. Correlation methods were applied to investigate groundwater-level response to rainfall, as well as its seasonal variations. The results reveal that the direct groundwater recharge to the shallow and deep bedrocks on hillslope is very limited. Water-level variations within these geological units are likely dominated by slow flow rock matrix storage. The rapid responses to rainfall (⩽2 h) with little seasonal variations were observed to the monitoring wells installed at the subsoil and soil/bedrock interface, as well as those in the shallow or deep bedrocks at the base of the hillslope. This suggests that the direct recharge takes place within these units. An automated time-series procedure using the water-table fluctuation method was developed to estimate groundwater recharge from the water-level and rainfall data. Results show the annual recharge rates of 42–197 mm/yr in the subsoil and soil/bedrock interface, which represent 4–19% of the annual rainfall. Statistical analysis of the relationship between the rainfall intensity and water-table rise reveal that the low rainfall intensity group (⩽1 mm/h) has greater impact on the groundwater recharge rate than other groups (>1 mm/h). This study shows that the combination of the time-series analysis and the water-table fluctuation method could be an useful approach to investigate groundwater recharge in fractured hard rock aquifers in Ireland.

The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.

Deep Head Pose: Gaze-Direction Estimation in Multimodal Video

In this paper we present a convolutional neuralnetwork (CNN)-based model for human head pose estimation inlow-resolution multi-modal RGB-D data. We pose the problemas one of classification of human gazing direction. We furtherfine-tune a regressor based on the learned deep classifier. Next wecombine the two models (classification and regression) to estimateapproximate regression confidence. We present state-of-the-artresults in datasets that span the range of high-resolution humanrobot interaction (close up faces plus depth information) data tochallenging low resolution outdoor surveillance data. We buildupon our robust head-pose estimation and further introduce anew visual attention model to recover interaction with theenvironment. Using this probabilistic model, we show thatmany higher level scene understanding like human-human/sceneinteraction detection can be achieved. Our solution runs inreal-time on commercial hardware