Using high-frequency water quality data to assess sampling strategies for the EU Water Framework Directive

The EU Water Framework Directive (WFD) requires that the ecological and chemical status of water bodies in Europe should be assessed, and action taken where possible to ensure that at least "good" quality is attained in each case by 2015. This paper is concerned with the accuracy and precision with which chemical status in rivers can be measured given certain sampling strategies, and how this can be improved. High-frequency (hourly) chemical data from four rivers in southern England were subsampled to simulate different sampling strategies for four parameters used for WFD classification: dissolved phosphorus, dissolved oxygen, pH and water temperature. These data sub-sets were then used to calculate the WFD classification for each site. Monthly sampling was less precise than weekly sampling, but the effect on WFD classification depended on the closeness of the range of concentrations to the class boundaries. In some cases, monthly sampling for a year could result in the same water body being assigned to three or four of the WFD classes with 95% confidence, due to random sampling effects, whereas with weekly sampling this was one or two classes for the same cases. In the most extreme case, the same water body could have been assigned to any of the five WFD quality classes. Weekly sampling considerably reduces the uncertainties compared to monthly sampling. The width of the weekly sampled confidence intervals was about 33% that of the monthly for P species and pH, about 50% for dissolved oxygen, and about 67% for water temperature. For water temperature, which is assessed as the 98th percentile in the UK, monthly sampling biases the mean downwards by about 1 °C compared to the true value, due to problems of assessing high percentiles with limited data. Low-frequency measurements will generally be unsuitable for assessing standards expressed as high percentiles. Confining sampling to the working week compared to all 7 days made little difference, but a modest improvement in precision could be obtained by sampling at the same time of day within a 3 h time window, and this is recommended. For parameters with a strong diel variation, such as dissolved oxygen, the value obtained, and thus possibly the WFD classification, can depend markedly on when in the cycle the sample was taken. Specifying this in the sampling regime would be a straightforward way to improve precision, but there needs to be agreement about how best to characterise risk in different types of river. These results suggest that in some cases it will be difficult to assign accurate WFD chemical classes or to detect likely trends using current sampling regimes, even for these largely groundwater-fed rivers. A more critical approach to sampling is needed to ensure that management actions are appropriate and supported by data.

Nitrogen dynamics under Lolium perenne after a single application of different sewage sludge types from the same treatment stream

Three sludge types from the same treatment stream (undigested liquid, anaerobically digested liquid and dewatered, anaerobically digested cake) were used in a field based tub study. Amendments (4, 8, and 16 Mg dry solid (ds)ha(-1)) were incorporated into the upper 15 cm of a sandy loam soil prior to sowing with rye-grass (Lolium perenne L.). Nitrogen transformations in the soil were determined for the 80 d period following incorporation. Nitrogen uptake and crop yield were measured in the cut sward 35 and 70 d after sowing. The study showed that application of sewage sludge at rates as low as 4 Mgha(-1) can have a nutritional benefit to rye-grass over the two harvests. Differences in N transformation, and hence crop nutritional benefit, between sludge types were evident throughout the experiment. In particular, the dewatering process changed the mineral N characteristics of the anaerobically digested sludge, which, when not dewatered, outperformed the other sludges in terms of yield and mineralisation rate at both harvests. The dewatered sludge produced the lowest yield of rye-grass. The undigested liquid sludge had the lowest foliar N and soil NO(3)-N concentrations, possibly immobilised as the large oxidisable C component of this sludge was metabolised by the microbial biomass. Correlation data support the concept of preferential uptake of NH(4)-N over NO(3)-N in Lolium perenne. Results are discussed in the context of managing sludge type and application for a plant nutrient source and NO(3)-N release.

Joint use of attribute importance rankings and non-attendance data in choice experiments

The joint and alternative uses of attribute non-attendance and importance ranking data within discrete choice experiments are investigated using data from Lebanon examining consumers’ preferences for safety certification in food. We find that both types of information; attribute non-attendance and importance rankings, improve estimates of respondent utility. We introduce a method of integrating both types of information simultaneously and find that this outperforms models where either importance ranking or non-attendance data are used alone. As in previous studies, stated non-attendance of attributes was not found to be consistent with respondents having zero marginal utility for those attributes

Cross layer based protocols for energy aware and critical data delivery related applications using wireless sensor networks

Wireless Sensor Networks (WSNs) have been an exciting topic in recent years. The services offered by a WSN can be classified into three major categories: monitoring, alerting, and information on demand. WSNs have been used for a variety of applications related to the environment (agriculture, water and forest fire detection), the military, buildings, health (elderly people and home monitoring), disaster relief, and area or industrial monitoring. In most WSNs tasks like processing the sensed data, making decisions and generating emergency messages are carried out by a remote server, hence the need for efficient means of transferring data across the network. Because of the range of applications and types of WSN there is a need for different kinds of MAC and routing protocols in order to guarantee delivery of data from the source nodes to the server (or sink). In order to minimize energy consumption and increase performance in areas such as reliability of data delivery, extensive research has been conducted and documented in the literature on designing energy efficient protocols for each individual layer. The most common way to conserve energy in WSNs involves using the MAC layer to put the transceiver and the processor of the sensor node into a low power, sleep state when they are not being used. Hence the energy wasted due to collisions, overhearing and idle listening is reduced. As a result of this strategy for saving energy, the routing protocols need new solutions that take into account the sleep state of some nodes, and which also enable the lifetime of the entire network to be increased by distributing energy usage between nodes over time. This could mean that a combined MAC and routing protocol could significantly improve WSNs because the interaction between the MAC and network layers lets nodes be active at the same time in order to deal with data transmission. In the research presented in this thesis, a cross-layer protocol based on MAC and routing protocols was designed in order to improve the capability of WSNs for a range of different applications. Simulation results, based on a range of realistic scenarios, show that these new protocols improve WSNs by reducing their energy consumption as well as enabling them to support mobile nodes, where necessary. A number of conference and journal papers have been published to disseminate these results for a range of applications.

Effect of repeated torque/mechanical loading cycles on two different abutment types in implants with internal tapered connections: an in vitro study

Internal tapered connections were developed to improve biomechanical properties and to reduce mechanical problems found in other implant connection systems. The purpose of this study was to evaluate the effects of mechanical loading and repeated insertion/removal cycles on the torque loss of abutments with internal tapered connections. Sixty-eight conical implants and 68 abutments of two types were used. They were divided into four groups: groups 1 and 3 received solid abutments, and groups 2 and 4 received two-piece abutments. In groups 1 and 2, abutments were simply installed and uninstalled; torque-in and torque-out values were measured. In groups 3 and 4, abutments were installed, mechanically loaded and uninstalled; torque-in and torque-out values were measured. Under mechanical loading, two-piece abutments were frictionally locked into the implant; thus, data of group 4 were catalogued under two subgroups (4a: torque-out value necessary to loosen the fixation screw; 4b: torque-out value necessary to remove the abutment from the implant). Ten insertion/removal cycles were performed for every implant/abutment assembly. Data were analyzed with a mixed linear model (P <= 0.05). Torque loss was higher in groups 4a and 2 (over 30% loss), followed by group 1 (10.5% loss), group 3 (5.4% loss) and group 4b (39% torque gain). All the results were significantly different. As the number of insertion/removal cycles increased, removal torques tended to be lower. It was concluded that mechanical loading increased removal torque of loaded abutments in comparison with unloaded abutments, and removal torque values tended to decrease as the number of insertion/removal cycles increased. To cite this article:Ricciardi Coppede A, de Mattos MdaGC, Rodrigues RCS, Ribeiro RF. Effect of repeated torque/mechanical loading cycles on two different abutment types in implants with internal tapered connections: an in vitro study.Clin. Oral Impl. Res. 20, 2009; 624-632.doi: 10.1111/j.1600-0501.2008.01690.x.

The South American Plasmodium falciparum var gene repertoire is limited, highly shared and possibly lacks several antigenic types

The Plasmodium falciparum var gene family encodes large variant antigens, which are important virulence factors, and also targets of the humoral host response. The frequently observed mild outcomes of falciparum malaria in many places of the Amazon area prompted us to ask whether a globally restricted variant (var) gene repertoire is present in currently circulating and older isolates of this area. By exhaustive analysis of var gene tags from 89 isolates and clones taken during many years from all over the Brazilian Amazon, we estimate that there are probably no more than 350-430 distinct sequence types, less than for any similar sized area studied so far. Detailed analysis of the var tags from genetically distinct clones obtained from single isolates revealed restricted and redundant repertoires suggesting either a low incidence of infective bites or restricted variant gene diversity in inoculated parasites. Additionally, we found a structuring of var gene repertoires observed as a higher pairwise typing sharing in isolates from the same microregion compared to isolates from different regions. Fine analysis of translated var tags revealed that certain Distinct Sequence Identifiers (DSIDs) were differently represented in Brazilian/South American isolates when compared to datasets from other continents. By global alignment of worldwide var DBL alpha sequences and sorting in groups with more than 76% identity, 125 clusters were formed and more than half of all genes were found in nine clusters with 50 or more sequences. While Brazilian/South American sequences were represented only in 64 groups, African sequences were found in the majority of clusters. DSID type 1 related sequences accumulated almost completely in one single cluster, indicating that limited recombination occurs in these specific var gene types. These data demonstrate the so far highest pairwise type sharing values for the var gene family in isolates from all over an entire subcontinent. The apparent lack of specific sequences types suggests that the P. falciparum transmission dynamics in the whole Amazon are probably different from any other endemic region studied and possibly interfere with the parasite`s ability to efficiently diversify its variant gene repertoires. (C) 2010 Elsevier B.V. All rights reserved.

A Framework for Exploring Multidimensional Data with 3D Projections

Visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e. g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least-Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework`s applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

On the efficiency of data representation on the modeling and characterization of complex networks

Specific choices about how to represent complex networks can have a substantial impact on the execution time required for the respective construction and analysis of those structures. In this work we report a comparison of the effects of representing complex networks statically by adjacency matrices or dynamically by adjacency lists. Three theoretical models of complex networks are considered: two types of Erdos-Renyi as well as the Barabasi-Albert model. We investigated the effect of the different representations with respect to the construction and measurement of several topological properties (i.e. degree, clustering coefficient, shortest path length, and betweenness centrality). We found that different forms of representation generally have a substantial effect on the execution time, with the sparse representation frequently resulting in remarkably superior performance. (C) 2011 Elsevier B.V. All rights reserved.

Application of chemometric methods in the evaluation of chemical and spectroscopic data on organic matter from Oxisols in sewage sludge applications

Chemometric methods can contribute to soil research by permitting the extraction of more information from the data. The aim of this work was to use Principal Component Analysis to evaluate data obtained through chemical and spectroscopic methods on the changes in the humification process of soil organic matter from two tropical soils after sewage sludge application. In this case, humic acids extracted from Typic Eutrorthox and Typic Haplorthox soils with and without sewage sludge application for 7 consecutive years were studied. The results obtained for all of the samples and methods showed two clusters: samples extracted from the two soil types. These expected results indicated the textural difference between the two soils was more significant than the differences between treatments (control and sewage sludge application) or between depths. In this case, an individual chemometric treatment was made for each type of soil. It was noted that the characterization of the humic acids extracted from soils with and without sewage sludge application after 7 consecutive years using several methods supplies important results about changes in the humification degree of soil organic matter, These important result obtained by Principal Component Analysis justify further research using these methods to characterize the changes in the humic acids extracted from sewage sludge-amended soils. (C) 2009 Elsevier B.V. All rights reserved.

Reliability of GPS based traffic data: an experimental evaluation

GPS tracking of mobile objects provides spatial and temporal data for a broad range of applications including traffic management and control, transportation routing and planning. Previous transport research has focused on GPS tracking data as an appealing alternative to travel diaries. Moreover, the GPS based data are gradually becoming a cornerstone for real-time traffic management. Tracking data of vehicles from GPS devices are however susceptible to measurement errors – a neglected issue in transport research. By conducting a randomized experiment, we assess the reliability of GPS based traffic data on geographical position, velocity, and altitude for three types of vehicles; bike, car, and bus. We find the geographical positioning reliable, but with an error greater than postulated by the manufacturer and a non-negligible risk for aberrant positioning. Velocity is slightly underestimated, whereas altitude measurements are unreliable.

A data driven approach for automating vehicle activated signs

Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated. Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.

Combining Satellite And Gauge Precipitation Data With Co-Kriging Method For Jakarta Region

Jakarta is vulnerable to flooding mainly caused by prolonged and heavy rainfall and thus a robust hydrological modeling is called for. A good quality of spatial precipitation data is therefore desired so that a good hydrological model could be achieved. Two types of rainfall sources are available: satellite and gauge station observations. At-site rainfall is considered to be a reliable and accurate source of rainfall. However, the limited number of stations makes the spatial interpolation not very much appealing. On the other hand, the gridded rainfall nowadays has high spatial resolution and improved accuracy, but still, relatively less accurate than its counterpart. To achieve a better precipitation data set, the study proposes cokriging method, a blending algorithm, to yield the blended satellite-gauge gridded rainfall at approximately 10-km resolution. The Global Satellite Mapping of Precipitation (GSMaP, 0.1⁰×0.1⁰) and daily rainfall observations from gauge stations are used. The blended product is compared with satellite data by cross-validation method. The newly-yield blended product is then utilized to re-calibrate the hydrological model. Several scenarios are simulated by the hydrological models calibrated by gauge observations alone and blended product. The performance of two calibrated hydrological models is then assessed and compared based on simulated and observed runoff.

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian Consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data over twelve months ahead. The disaggregated models were estimated by SARIMA and will have different levels of disaggregation. Aggregated models will be estimated by time series techniques such as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy comparison will be made by the selection model procedure known as Model Confidence Set and by Diebold-Mariano procedure. We were able to find evidence of forecast accuracy gains in models using more disaggregated data

Application of chemometric methods in the evaluation of chemical and spectroscopic data on organic matter from Oxisols in sewage sludge applications

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)