Multi-Scaled Grassland-Woody Plant Dynamics in the Heterogeneous Marl Prairies of the Southern Everglades

The Everglades freshwater marl prairie is a dynamic and spatially heterogeneous landscape, containing thousands of tree islands nested within a marsh matrix. Spatial processes underlie population and community dynamics across the mosaic, especially the balance between woody and graminoid components, and landscape patterns reflect interactions among multiple biotic and abiotic drivers. To better understand these complex, multi-scaled relationships we employed a three-tiered hierarchical design to investigate the effects of seed source, hydrology, and more indirectly fire on the establishment of new woody recruits in the marsh, and to assess current tree island patterning across the landscape. Our analyses were conducted at the ground level at two scales, which we term the micro- and meso-scapes, and results were related to remotely detected tree island distributions assessed in the broader landscape, that is, the macro-scape. Seed source and hydrologic effects on recruitment in the micro- and meso-scapes were analyzed via logistic regression, and spatial aggregation in the macro-scape was evaluated using a grid-based univariate O-ring function. Results varied among regions and scales but several general trends were observed. The patterning of adult populations was the strongest driver of recruitment in the micro- and meso-scape prairies, with recruits frequently aggregating around adults or tree islands. However in the macro-scape biologically associated (second order) aggregation was rare, suggesting that emergent woody patches are heavily controlled by underlying physical and environmental factors such as topography, hydrology, and fire.

Grid-based histogram arithmetic for the probabilistic analysis of functions

The selection of predefined analytic grids (partitions of the numeric ranges) to represent input and output functions as histograms has been proposed as a mechanism of approximation in order to control the tradeoff between accuracy and computation times in several áreas ranging from simulation to constraint solving. In particular, the application of interval methods for probabilistic function characterization has been shown to have advantages over other methods based on the simulation of random samples. However, standard interval arithmetic has always been used for the computation steps. In this paper, we introduce an alternative approximate arithmetic aimed at controlling the cost of the interval operations. Its distinctive feature is that grids are taken into account by the operators. We apply the technique in the context of probability density functions in order to improve the accuracy of the probability estimates. Results show that this approach has advantages over existing approaches in some particular situations, although computation times tend to increase significantly when analyzing large functions.

Quadrature generators based on ring oscillators and shift registers

Quadrature oscillators are key elements in modern radio frequency (RF) transceivers and very useful nowadays in wireless communications, since they can provide: low quadrature error, low phase-noise, and wide tuning range (useful to cover several bands). RC oscillators can be fully integrated without the need of external components (external high Q-inductors), optimizing area, cost, and power consumption. The conventional structure of ring oscillator offers poor frequency stability and phasenoise, low quality factor (Q), and besides being vulnerable to process, voltage and temperature (PVT) variations, its performance degrades as the frequency of operation increases. This thesis is devoted to quadrature oscillators and presents a detailed comparative study of ring oscillator and shift register (SR) approaches. It is shown that in SRs both phase-noise and phase error are reduced, while ring oscillators have the advantage of occupying less area and less consumption due to the reduced number of components in the circuit. Thus, although ring oscillators are more suitable for biomedical applications, SRs are more appropriate for wireless applications, especially when specification requirements are more stringent and demanding. The first architecture studied consists in a simple CMOS ring oscillator employing an odd number of static single-ended inverters as delay cells. Subsequently, the quadrature 4-stage ring oscillator concept is shown and post-layout simulations are presented. The 3 and 4-phase single-frequency local oscillator (LO) generators employing SRs are presented, the latter with 50% and 25% duty-cycles. The circuits operate at 600 MHz and 900 MHz, and were designed in a 130 nm standard CMOS technology with a voltage supply of 1.2 V.

Nonlinear prediction based on score function

The linear prediction coding of speech is based in the assumption that the generation model is autoregresive. In this paper we propose a structure to cope with the nonlinear effects presents in the generation of the speech signal. This structure will consist of two stages, the first one will be a classical linear prediction filter, and the second one will model the residual signal by means of two nonlinearities between a linear filter. The coefficients of this filter are computed by means of a gradient search on the score function. This is done in order to deal with the fact that the probability distribution of the residual signal still is not gaussian. This fact is taken into account when the coefficients are computed by a ML estimate. The algorithm based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics and is based on blind deconvolution of Wiener systems [1]. Improvements in the experimental results with speech signals emphasize on the interest of this approach.

Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora kernoviae in the UK

We developed a stochastic simulation model incorporating most processes likely to be important in the spread of Phytophthora ramorum and similar diseases across the British landscape (covering Rhododendron ponticum in woodland and nurseries, and Vaccinium myrtillus in heathland). The simulation allows for movements of diseased plants within a realistically modelled trade network and long-distance natural dispersal. A series of simulation experiments were run with the model, representing an experiment varying the epidemic pressure and linkage between natural vegetation and horticultural trade, with or without disease spread in commercial trade, and with or without inspections-with-eradication, to give a 2 x 2 x 2 x 2 factorial started at 10 arbitrary locations spread across England. Fifty replicate simulations were made at each set of parameter values. Individual epidemics varied dramatically in size due to stochastic effects throughout the model. Across a range of epidemic pressures, the size of the epidemic was 5-13 times larger when commercial movement of plants was included. A key unknown factor in the system is the area of susceptible habitat outside the nursery system. Inspections, with a probability of detection and efficiency of infected-plant removal of 80% and made at 90-day intervals, reduced the size of epidemics by about 60% across the three sectors with a density of 1% susceptible plants in broadleaf woodland and heathland. Reducing this density to 0.1% largely isolated the trade network, so that inspections reduced the final epidemic size by over 90%, and most epidemics ended without escape into nature. Even in this case, however, major wild epidemics developed in a few percent of cases. Provided the number of new introductions remains low, the current inspection policy will control most epidemics. However, as the rate of introduction increases, it can overwhelm any reasonable inspection regime, largely due to spread prior to detection. (C) 2009 Elsevier B.V. All rights reserved.

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.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Determining spatial resolution in spatial load forecasting using a grid-based model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Achieving a more realistic assessment of rockfall hazards by coupling three-dimensional process models and field-based tree-ring data

Sound knowledge of the spatial and temporal patterns of rockfalls is fundamental for the management of this very common hazard in mountain environments. Process-based, three-dimensional simulation models are nowadays capable of reproducing the spatial distribution of rockfall occurrences with reasonable accuracy through the simulation of numerous individual trajectories on highly-resolved digital terrain models. At the same time, however, simulation models typically fail to quantify the ‘real’ frequency of rockfalls (in terms of return intervals). The analysis of impact scars on trees, in contrast, yields real rockfall frequencies, but trees may not be present at the location of interest and rare trajectories may not necessarily be captured due to the limited age of forest stands. In this article, we demonstrate that the coupling of modeling with tree-ring techniques may overcome the limitations inherent to both approaches. Based on the analysis of 64 cells (40 m × 40 m) of a rockfall slope located above a 1631-m long road section in the Swiss Alps, we illustrate results from 488 rockfalls detected in 1260 trees. We illustrate that tree impact data cannot only be used (i) to reconstruct the real frequency of rockfalls for individual cells, but that they also serve (ii) the calibration of the rockfall model Rockyfor3D, as well as (iii) the transformation of simulated trajectories into real frequencies. Calibrated simulation results are in good agreement with real rockfall frequencies and exhibit significant differences in rockfall activity between the cells (zones) along the road section. Real frequencies, expressed as rock passages per meter road section, also enable quantification and direct comparison of the hazard potential between the zones. The contribution provides an approach for hazard zoning procedures that complements traditional methods with a quantification of rockfall frequencies in terms of return intervals through a systematic inclusion of impact records in trees.

Schleiermacher's Doctrine of Biblical Authority: An Alternative to Content-Based/Supernaturalist and Function-Based Rationalist Models

Author: Kerry W. Holton Title: SCHLEIERMACHER’S DOCTRINE OF BIBLICAL AUTHORITY: AN ALTERNATIVE TO CONTENT-BASED/SUPERNATURALIST AND FUNCTION- BASED/RATIONALIST MODELS Advisor: Theodore M. Vial, Jr. Degree Date: August 2015 This dissertation examines Friedrich Schleiermacher’s understanding of biblical authority and argues that, as an alternative to strictly supernaturalistic and rationalistic models, his understanding allows the New Testament to speak authoritatively in Christian religion in an age of critical, historical awareness. After classifying Schleiermacher’s position in a typology of the doctrine of biblical authority, this dissertation explores his conception of divine revelation and inspiration vis-à-vis scripture. It demonstrates that although he did not believe there is warrant for the claim of a direct connection between divine revelation and scripture, or that scripture is the foundation of faith, he nonetheless asserted that the New Testament is authoritative. He asserted the normative authority of the New Testament on the basis that it is the first presentation of Christian faith. This dissertation examines Schleiermacher’s “canon within the canon,” as well as his denial that the Old Testament shares the same normative worth and inspiration of the New. Although this dissertation finds difficulty with some of Schleiermacher’s views regarding the Old Testament, it names two significant strengths of what is identified as his evangelical, content-based, and rationalist approach to biblical authority. First, it recognizes and values the co-presence and co-activity of the supernatural and the natural !ii in the production of the New Testament canon. This allows both scripture and the church to share religious authority. Second, it allows Christian faith and the historical-method to coexist, as it does not require people to contradict what they know to be the case about science, history, and philosophy. Thus, this dissertation asserts that Schleiermacher’s understanding of biblical authority is a robust one, since, for him, the authority of scripture does not lie in some property of the texts themselves that historians or unbelievers can take away.

Optical loading sensor based on ring-cavity fiber laser incorporating a 45°-tilted fiber polarizing grating

We have experimentally demonstrated an active loading sensor system based on a fiber ring laser with singlepolarization output using an intra-cavity 45°-tilted fiber grating. When the laser cavity fiber is subjected to loading, the laser output is encoded with the loading information that can be measured and monitored by a standard power meter. The achieved loading sensitivity is 0.033/kg • m-1 and 0.042/kg • m-1 for two different interaction lengths. The experimental results clearly show that such a single-polarization fiber laser may be commercially developed into a low-cost, high-sensitivity loading sensor system.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.