A data forest: multi-dimensional visualization

As Terabyte datasets become the norm, the focus has shifted away from our ability to produce and store ever larger amounts of data, onto its utilization. It is becoming increasingly difficult to gain meaningful insights into the data produced. Also many forms of the data we are currently producing cannot easily fit into traditional visualization methods. This paper presents a new and novel visualization technique based on the concept of a Data Forest. Our Data Forest has been designed to be used with vir tual reality (VR) as its presentation method. VR is a natural medium for investigating large datasets. Our approach can easily be adapted to be used in a variety of different ways, from a stand alone single user environment to large multi-user collaborative environments. A test application is presented using multi-dimensional data to demonstrate the concepts involved.

Data forest: a collaborative version

As we increase our ability to produce and store ever larger amounts of data, it is becoming increasingly difficult to understand what the data is trying to tell us. Not all the data we are currently producing can easily fit into traditional visualization methods. This paper presents a new and novel visualization technique based on the concept of a Data Forest. Our Data Forest has been developed to be utilised by virtual reality (VR) systems. VR is a natural information medium. This approach can easily be adapted to be used in collaborative environments. A test application has been developed to demonstrate the concepts involved and a collaborative version tested.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

NOx and O3 above a tropical rainforest: an analysis with a global and box model

A cross-platform field campaign, OP3, was conducted in the state of Sabah in Malaysian Borneo between April and July of 2008. Among the suite of observations recorded, the campaign included measurements of NOx and O3 – crucial outputs of any model chemistry mechanism. We describe the measurements of these species made from both the ground site and aircraft. We then use the output from two resolutions of the chemistry transport model p-TOMCAT to illustrate the ability of a global model chemical mechanism to capture the chemistry at the rainforest site. The basic model performance is good for NOx and poor for ozone. A box model containing the same chemical mechanism is used to explore the results of the global model in more depth and make comparisons between the two. Without some parameterization of the nighttime boundary layer – free troposphere mixing (i.e. the use of a dilution parameter), the box model does not reproduce the observations, pointing to the importance of adequately representing physical processes for comparisons with surface measurements. We conclude with a discussion of box model budget calculations of chemical reaction fluxes, deposition and mixing, and compare these results to output from p-TOMCAT. These show the same chemical mechanism behaves similarly in both models, but that emissions and advection play particularly strong roles in influencing the comparison to surface measurements.

Evaluation of a Lagrangian box model using field measurements from EASE (Eastern Atlantic Summer Experiment) 1996

The Cambridge Tropospheric Trajectory model of Chemistry and Transport (CiTTyCAT), a Lagrangian chemistry model, has been evaluated using atmospheric chemical measurements collected during the East Atlantic Summer Experiment 1996 (EASE '96). This field campaign was part of the UK Natural Environment Research Council's (NERC) Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) programme, conducted at Mace Head, Republic of Ireland, during July and August 1996. The model includes a description of gas-phase tropospheric chemistry, and simple parameterisations for surface deposition, mixing from the free troposphere and emissions. The model generally compares well with the measurements and is used to study the production and loss of O3 under a variety of conditions. The mean difference between the hourly O3 concentrations calculated by the model and those measured is 0.6 ppbv with a standard deviation of 8.7 ppbv. Three specific air-flow regimes were identified during the campaign – westerly, anticyclonic (easterly) and south westerly. The westerly flow is typical of background conditions for Mace Head. However, on some occasions there was evidence of long-range transport of pollutants from North America. In periods of anticyclonic flow, air parcels had collected emissions of NOx and VOCs immediately before arriving at Mace Head, leading to O3 production. The level of calculated O3 depends critically on the precise details of the trajectory, and hence on the emissions into the air parcel. In several periods of south westerly flow, low concentrations of O3 were measured which were consistent with deposition and photochemical destruction inside the tropical marine boundary layer.

Biomass partitioning and growth efficiency in four naturally regenerated forest tree species

Current forest growth models and yield tables are almost exclusively based on data from mature trees, reducing their applicability to young and developing stands. To address this gap, young European beech, sessile oak, Scots pine and Norway spruce trees approximately 0 to 10 years old were destructively sampled in a range of naturally regenerated forest stands in Central Europe. Diameter at base and height were first measured in situ for up to 175 individuals per species. Subsequently, the trees were excavated and dry biomass of foliage, branches, stems and roots was measured. Allometric relations were then used to calculate biomass allocation coefficients (BAC) and growth efficiency (GE) patterns in young trees. We found large differences in BAC and GE between broadleaves and conifers, but also between species within these categories. Both BAC and GE are strongly age-specific in young trees, their rapidly changing values reflecting different growth strategies in the earliest stages of growth. We show that linear relationships describing biomass allocation in older trees are not applicable in young trees. To accurately predict forest biomass and carbon stocks, forest growth models need to include species and age specific parameters of biomass allocation patterns.

Grey-box radial basis function modelling

A fundamental principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: (i) the underlying data generating mechanism exhibits known symmetric property, and (ii) the underlying process obeys a set of given boundary value constraints. The class of efficient orthogonal least squares regression algorithms can readily be applied without any modification to construct parsimonious grey-box RBF models with enhanced generalisation capability.

Toxoplasma gondii antibodies in wild rodents and marsupials from the Atlantic Forest, state of São Paulo, Brazil

Toxoplasma gondii é um protozoário parasita que infecta animais de sangue quente, incluindo seres humanos. Pequenos roedores e marsupiais têm papel importante na epidemiologia do T. gondii, pois são fontes de infecção para os felídeos domésticos e selvagens. Amostras de soro de 151 roedores e 48 marsupiais, capturados na Mata Atlântica, Estado de São Paulo, Sudeste do Brasil, foram analisadas para a pesquisa de anticorpos anti-T. gondii. Os anticorpos foram detectados pelo Teste de Aglutinação Modificada (MAT ≥ 25), com 8,6% (13/151) dos roedores e 10,4% (5/48) dos marsupiais soropositivos, com títulos variando de 25 a 6.400 e de 25 a 3.200, respectivamente, para os roedores e os marsupiais. Três das oito espécies de roedores (Akodon spp., Oligoryzomys nigripes e Rattus norvegicus) e uma das quatro espécies de marsupiais (Didelphis aurita) apresentaram animais positivos. A presença de anticorpos anti-T. gondii foi descrita pela primeira vez no roedor Oligoryzomys nigripes.

Protecting degraded rainforests: enhancement of forest carbon stocks under REDD+

The likely Reducing Emissions from Deforestation and Degradation (REDD+) mechanism includes strategies for the enhancement of forest carbon stocks. Recent concerns have been expressed that such enhancement, or restoration, of forest carbon could be counterproductive to biodiversity conservation, because forests are managed as “carbon farms” with the application of intensive silvicultural management that could homogenize diverse degraded rainforests. Restoration increases regeneration rates in degraded forest compared to naturally regenerating forest, and thus could yield significant financial returns for carbon sequestered. Here, we argue that such forest restoration projects are, in fact, likely to provide a number of benefits to biodiversity conservation including the retention of biodiversity, the prevention of forest conversion to agriculture, and employment opportunities for poor local communities. As with other forms of forest-based carbon offsets, there are possible moral hazard and leakage problems with restoration. However, due to the multiple benefits, we urge that enhancement of forest carbon stocks be detailed as a major component in the future negotiations of REDD+.

Forest soil carbon cycle under elevated CO2 – a case of increased throughput?

Forest soils account for a large part of the stable carbon pool held in terrestrial ecosystems. Future levels of atmospheric CO2 are likely to increase C input into the soils through increased above- and below-ground production of forests. This increased input will result in greater sequestration of C only if the additional C enters stable pools. In this review, we compare current observations from four large-scale Free Air FACE Enrichment (FACE) experiments on forest ecosystems (EuroFACE, Aspen-FACE, Duke FACE and ORNL-FACE) and consider their predictive power for long-term C sequestration. At all sites, FACE increased fine root biomass, and in most cases higher fine root turnover resulted in higher C input into soil via root necromass. However, at all sites, soil CO2 efflux also increased in excess of the increased root necromass inputs. A mass balance calculation suggests that a large part of the stimulation of soil CO2 efflux may be due to increased root respiration. Given the duration of these experiments compared with the life cycle of a forest and the complexity of processes involved, it is not yet possible to predict whether elevated CO2 will result in increased C storage in forest soil.