Fluctuations in Number of Cercospora beticola Conidia in Relationship to Environment and Disease Severity in Sugar Beet

All content of Phytopathology is open access without restriction 12 months after publicationCercospora leaf spot, caused by Cercospora beticola, is the most damaging foliar disease of sugar beet in Minnesota (MN) and North Dakota (ND). Research was conducted to characterize the temporal progression of aerial concentration of C. beticola conidia in association with the environment and disease severity in sugar beet. In 2003 and 2004, volumetric spore traps were placed within inoculated sugar beet plots to determine daily dispersal of conidia at Breckenridge, MN, and St. Thomas, ND. Plots were rated weekly for disease severity. At both locations, conidia were first collected in early July 2003 and late June in 2004. Peaks of conidia per cubic meter of air were observed with maxima in late August 2003 and in early September 2004 at both locations. Peaks of airborne conidium concentration were significantly correlated with the average temperature of daily hours when relative humidity was greater than 87%. Weekly mean hourly conidia per cubic meter of air was significantly (P <0.01) associated with disease severity during both years and across locations. This study showed that C. beticola conidial numbers may be used to estimate potential disease severity that, with further research, could be incorporated in a disease forecasting model to rationalize Cercospora leaf spot management.Peer reviewe

Finite element modelling of electrostatic fields in process tomography capacitive electrode systems for flow response evaluation

Various aspects and results of 2-D finite element (FE) modeling of electrostatic fields in 12-electrode capacitive systems for two-phase flow imaging are described. The capacitive technique relies on changes in capacitances between electrodes (mounted on the outer surface of the flow pipe) due to the change in permittivities of flow components. The measured capacitances between various electrode pairs and the field computation data are used to reconstruct the cross sectional image of the flow components. FE modeling of the electric field is necessary to optimize design variables and evaluate the system response to various flow regimes, likely to be encountered in practice. Results are presented in terms of normalized capacitances for various flow regimes. The effects of key geometric parameters of the electrode system are presented and analyzed

Evaluation of Includem’s intensive support services – September 2007

In 2005 Includem commissioned a two-year evaluation of its intensive support services provided to young people as part of the Intensive Support and Monitoring Service in Edinburgh, Glasgow, Dundee, East Dunbartonshire, and West Dunbartonshire. Over two years Includem’s provided intensive support, normally around 15 hours per week plus access to Includem’s 24-hour crisis helpline, to over 200 young people, including 69 young people with a Movement Restriction Condition ('electronic tag')

Validation of Finite Element Modelling of Multielectrode Capacitive System for Process Tomography Flow Imaging

Finite element modelling of process tomography sensor systems is necessary for their CAD both for performance evaluation and design optimization. This paper involves the validation of finite element models of a 12-electrode capacitive sensor system for multiphase flow imaging. Various results of modelling have been compared in the form of standing mode capacitances and sensor sensitivity distribution with experimental data obtained from UMIST. There is good agreement between simulation results and experiments especially for high sensitivity regions inside the pipe

Distributing the Kalman Filter for Large-Scale Systems

This paper derives a \emph{distributed} Kalman filter to estimate a sparsely connected, large-scale, $n-$dimensional, dynamical system monitored by a network of $N$ sensors. Local Kalman filters are implemented on the ($n_l-$dimensional, where $n_l\ll n$) sub-systems that are obtained after spatially decomposing the large-scale system. The resulting sub-systems overlap, which along with an assimilation procedure on the local Kalman filters, preserve an $L$th order Gauss-Markovian structure of the centralized error processes. The information loss due to the $L$th order Gauss-Markovian approximation is controllable as it can be characterized by a divergence that decreases as $L\uparrow$. The order of the approximation, $L$, leads to a lower bound on the dimension of the sub-systems, hence, providing a criterion for sub-system selection. The assimilation procedure is carried out on the local error covariances with a distributed iterate collapse inversion (DICI) algorithm that we introduce. The DICI algorithm computes the (approximated) centralized Riccati and Lyapunov equations iteratively with only local communication and low-order computation. We fuse the observations that are common among the local Kalman filters using bipartite fusion graphs and consensus averaging algorithms. The proposed algorithm achieves full distribution of the Kalman filter that is coherent with the centralized Kalman filter with an $L$th order Gaussian-Markovian structure on the centralized error processes. Nowhere storage, communication, or computation of $n-$dimensional vectors and matrices is needed; only $n_l \ll n$ dimensional vectors and matrices are communicated or used in the computation at the sensors