FITTING BOLE-VOLUME EQUATIONS TO SPATIALLY CORRELATED WITHIN-TREE DATA

Equations to predict the volume of an individual tree bole between stump height and the height at which its diameter has tapered to a specified minimum are common in forestry. When fitting such a regression equation, a sample of trees which span the range of sizes needed for eventual application of the equation is selected. Bole diameter is measured at ascending heights on the bole. Each tree, therefore, contributes multiple measurements to the data fitted to the equation. In contrast to past practice, we model these data in a manner which accounts for the likely spatial correlation among measurements within a tree. The resulting mixed-effects nonlinear model is fitted by REML and also by generalized estimating equations (GEE). Results from the two approaches are nearly identical, which suggests that the computationally less demanding GEE may be acceptable as a routine alternative to a fully parameterized approach

Fusion: A Safe and Secure Software Platform for Autonomous Driving

The vastly increasing amount of software in vehicles, its variability and complexity, as well as the computational requirements, especially for those built with autonomous driving in mind, require new approaches to the structure and integration of software. The traditional approaches of single-purpose embedded devices with integrated software are no longer a suitable choice. New architectures introduce general purpose compute devices, capable of high-performance computation, as well as high variability of software. Managing the increasing complexity, also at runtime, in a safe and secure manner, are open challenges. Solving these challenges is a high-complexity development and integration effort requiring design-time and runtime configuration, approaches to communication middleware, operating system configuration, such as task scheduling, monitoring, tight integration of security and safety, and, especially in the case of autonomous driving, concepts for dynamic adaption of the system to the situation, e.g., fail-operational concepts. We present Fusion, a next-generation software platform supporting the development of autonomous driving systems

SITE-SPECIFIC VERSUS WHOLE-FIELD FERTILITY AND LIME MANAGEMENT IN MICHIGAN SOYBEANS AND CORN

Prior research into variable-rate application (VRA) of fertilizer nutrients has found profitability to be lacking in single nutrient applications to U.S. cereal crops. This study examines the yield and cost effects of VRA phosphorus, potassium and lime application on Michigan corn and soybean farm fields in 1998-2001. After four years, we found no yield gain from site-specific management, but statistically significant added costs, resulting in no gain in profitability. Contrary to results elsewhere, there was no evidence of enhanced spatial yield stability due to site-specific fertility management. Likewise, there was no evidence of decreased variability of phosphorus, potassium or lime after VRA treatment. Site-specific response functions and yield goals might also enhance the likelihood of profitable VRA in the future.Crop Production/Industries,

Project Report No. 38, Average Observed Fusiform Rust Transition Paths

fusiform rust ( Cronatrium quercuum [Berk. ] Miyabe ex Shirai f . sp . tusiforme L. ) is a devastating disease in loblolly ( Pinus taeda L. ) and slash ( Pinus elliottii Englem. ) pine plantations throughout the southern United States . Pine stems infected with fusiform rust are subject to hazards such as wind breakage, and if a pine stem with a gall on it does survive to harvest, utilization of the infected stem piece may be down-graded from possible lumber to probable pulpwood or maybe completely discarded

Modeling Effective Dosages in Hormetic Dose-Response Studies

BACKGROUND: Two hormetic modifications of a monotonically decreasing log-logistic dose-response function are most often used to model stimulatory effects of low dosages of a toxicant in plant biology. As just one of these empirical models is yet properly parameterized to allow inference about quantities of interest, this study contributes the parameterized functions for the second hormetic model and compares the estimates of effective dosages between both models based on 23 hormetic data sets. Based on this, the impact on effective dosage estimations was evaluated, especially in case of a substantially inferior fit by one of the two models. METHODOLOGY/PRINCIPAL FINDINGS: The data sets evaluated described the hormetic responses of four different test plant species exposed to 15 different chemical stressors in two different experimental dose-response test designs. Out of the 23 data sets, one could not be described by any of the two models, 14 could be better described by one of the two models, and eight could be equally described by both models. In cases of misspecification by any of the two models, the differences between effective dosages estimates (0-1768%) greatly exceeded the differences observed when both models provided a satisfactory fit (0-26%). This suggests that the conclusions drawn depending on the model used may diverge considerably when using an improper hormetic model especially regarding effective dosages quantifying hormesis. CONCLUSIONS/SIGNIFICANCE: The study showed that hormetic dose responses can take on many shapes and that this diversity can not be captured by a single model without risking considerable misinterpretation. However, the two empirical models considered in this paper together provide a powerful means to model, prove, and now also to quantify a wide range of hormetic responses by reparameterization. Despite this, they should not be applied uncritically, but after statistical and graphical assessment of their adequacy

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed

An R 2 statistic for fixed effects in the linear mixed model

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R2 statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R2 statistic for the linear mixed model by using only a single model. The proposed R2 statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R2 statistic arises as a 1–1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model to a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R2 statistic leads immediately to a natural definition of a partial R2 statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R2, a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated blood pressure outcomes for the study

Ecological Interactions of the Sexually Deceptive Orchid Orchis Galilaea

Plant species dependent on highly specific interactions with pollinators are vulnerable to environmental change. Conservation strategies therefore require a detailed understanding of pollination ecology. This two-year study examined the interactions between the sexually deceptive orchid, Orchis galilaea, and its pollinator Lasioglossum marginatum. Relationships were investigated across three different habitats known to support O. galilaea (garrigue, oak woodland, and mixed oak/pine woodland) in Lebanon. Visitation rates to flowers were extremely low and restricted to male bees. The reproductive success of O. galilaea under ambient conditions was 29.3% (±2.4), compared to 89.0% (±2.1) in plants receiving cross-pollination by hand. No difference in reproductive success was found between habitat types, but values of reproductive success were positively correlated to the abundance of male bees. Pollination limitation can have negative impacts on the population growth of orchids, and this study provides clear evidence for more holistic approaches to habitat conservation to support specific interactions