Machine recognition of hand-drawn circuit diagrams

An application of image processing techniques to recognition of hand-drawn circuit diagrams is presented. The scanned image of a diagram is pre-processed to remove noise and converted to bilevel. Morphological operations are applied to obtain a clean, connected representation using thinned lines. The diagram comprises of nodes, connections and components. Nodes and components are segmented using appropriate thresholds on a spatially varying object pixel density. Connection paths are traced using a pixel-stack. Nodes are classified using syntactic analysis. Components are classified using a combination of invariant moments, scalar pixel-distribution features, and vector relationships between straight lines in polygonal representations. A node recognition accuracy of 82% and a component recognition accuracy of 86% was achieved on a database comprising 107 nodes and 449 components. This recogniser can be used for layout “beautification” or to generate input code for circuit analysis and simulation packages

Management of virus diseases in vegetables

• To undertake an audit of management systems used for tomato spotted wilt virus (TSWV) in greenhouse and field production with the aim of improving disease management determining knowledge gaps in virus-vector relationships. • To investigate the basis for the development of resistance breaking strains of TSWV in capsicums and apply this to virus management in capsicums. • To further develop effective virus management systems in vegetable cucurbit crops. Aspects to be investigated include value of barrier crops, non-insecticide products and cultivar tolerance to virus. • To further develop and assess the adoption and impact of integrated viral disease management systems in field grown and protected cropping systems as part of the vegetable industry development plan.

Zero-non-zero patterned vector error correction modeling for I(2) cointegrated time series with applications in testing PPP and stock market relationships

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.

Exploring Relationships Between Vector-Borne Diseases and Landscape Architecture: Aedes aegypti, Aedes albopictus and Landscape Architecture

Thesis (Master's)--University of Washington, 2016-06

Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Abundance and prevalence of Aedes aegypti immatures and relationships with household water storage in rural areas in southern Viet Nam

Since 2000, the Government of Viet Nam has committed to provide rural communities with increased access to safe water through a variety of household water supply schemes (wells, ferrocement tanks and jars) and piped water schemes. One possible, unintended consequence of these schemes is the concomitant increase in water containers that may serve as habitats for dengue mosquito immatures, principally Aedes aegypti. To assess these possible impacts we undertook detailed household surveys of Ae. aegypti immatures, water storage containers and various socioeconomic factors in three rural communes in southern Viet Nam. Positive relationships between the numbers of household water storage containers and the prevalence and abundance of Ae. aegypti immatures were found. Overall, water storage containers accounted for 92–97% and 93–96% of the standing crops of III/IV instars and pupae, respectively. Interestingly, households with higher socioeconomic levels had significantly higher numbers of water storage containers and therefore greater risk of Ae. aegypti infestation. Even after provision of piped water to houses, householders continued to store water in containers and there was no observed decrease in water storage container abundance in these houses, compared to those that relied entirely on stored water. These findings highlight the householders’ concerns about the limited availability of water and their strong behavoural patterns associated with storage of water. We conclude that household water storage container availability is a major risk factor for infestation with Ae. aegypti immatures, and that recent investment in rural water supply infrastructure are unlikely to mitigate this risk, at least in the short term.

Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine

In this paper, downscaling models are developed using a support vector machine (SVM) for obtaining projections of monthly mean maximum and minimum temperatures (T-max and T-min) to river-basin scale. The effectiveness of the model is demonstrated through application to downscale the predictands for the catchment of the Malaprabha reservoir in India, which is considered to be a climatically sensitive region. The probable predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1978-2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 1978-2100. The predictor variables are classified into three groups, namely A, B and C. Large-scale atmospheric variables Such as air temperature, zonal and meridional wind velocities at 925 nib which are often used for downscaling temperature are considered as predictors in Group A. Surface flux variables such as latent heat (LH), sensible heat, shortwave radiation and longwave radiation fluxes, which control temperature of the Earth's surface are tried as plausible predictors in Group B. Group C comprises of all the predictor variables in both the Groups A and B. The scatter plots and cross-correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3 and to Study the predictor-predictand relationships. The impact of trend in predictor variables on downscaled temperature was studied. The predictor, air temperature at 925 mb showed an increasing trend, while the rest of the predictors showed no trend. The performance of the SVM models that are developed, one for each combination of predictor group, predictand, calibration period and location-based stratification (land, land and ocean) of climate variables, was evaluated. In general, the models which use predictor variables pertaining to land surface improved the performance of SVM models for downscaling T-max and T-min

Characterisation of wood–water relationships and transverse anatomy and their relationship to drying degrade

Characterisation of a number of key wood properties utilising ‘state of the art’ tools was achieved for four commercial Australian hardwood species: Corymbia citriodora, Eucalyptus pilularis, Eucalyptus marginata and Eucalyptus obliqua. The wood properties were measured for input into microscopic (cellular level) and macroscopic (board level) vacuum drying models currently under development. Morphological characterisation was completed using a combination of ESEM, optical microscopy and a custom vector-based image analysis software. A clear difference in wood porosity, size, wall thickness and orientation was evident between species. Wood porosity was measured using a combination of fibre and vessel porosity. A highly sensitive microbalance and scanning laser micrometres were used to measure loss of moisture content in conjunction with directional shrinkage on micro-samples of E. obliqua to investigate the validity of measuring collapse-free shrinkage in very thin sections. Collapse-free shrinkage was characterised, and collapse propensity was verified when testing thicker samples. Desorption isotherms were calculated for each species using wood–water relations data generated from shrinkage experiments. Fibre geometry and wood shrinkage anisotropy were used to explain the observed difficulty in drying of the different species in terms of collapse and drying stress-related degrade.

Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Causality analysis of groundwater dynamics based on a Vector Autoregressive model in the semi-arid basin of Gundal (South India)

Causal relationships existing between observed levels of groundwater in a semi-arid sub-basin of the Kabini River basin (Karnataka state, India) are investigated in this study. A Vector Auto Regressive model is used for this purpose. Its structure is built on an upstream/downstream interaction network based on observed hydro-physical properties. Exogenous climatic forcing is used as an input based on cumulated rainfall departure. Optimal models are obtained thanks to a trial approach and are used as a proxy of the dynamics to derive causal networks. It appears to be an interesting tool for analysing the causal relationships existing inside the basin. The causal network reveals 3 main regions: the Northeastern part of the Gundal basin is closely coupled to the outlet dynamics. The Northwestern part is mainly controlled by the climatic forcing and only marginally linked to the outlet dynamic. Finally, the upper part of the basin plays as a forcing rather than a coupling with the lower part of the basin allowing for a separate analysis of this local behaviour. The analysis also reveals differential time scales at work inside the basin when comparing upstream oriented with downstream oriented causalities. In the upper part of the basin, time delays are close to 2 months in the upward direction and lower than 1 month in the downward direction. These time scales are likely to be good indicators of the hydraulic response time of the basin which is a parameter usually difficult to estimate practically. This suggests that, at the sub-basin scale, intra-annual time scales would be more relevant scales for analysing or modelling tropical basin dynamics in hard rock (granitic and gneissic) aquifers ubiquitous in south India. (c) 2012 Elsevier B.V. All rights reserved.

Using Relationships for Matching Textual Domain Models with Existing Code

We address the task of mapping a given textual domain model (e.g., an industry-standard reference model) for a given domain (e.g., ERP), with the source code of an independently developed application in the same domain. This has applications in improving the understandability of an existing application, migrating it to a more flexible architecture, or integrating it with other related applications. We use the vector-space model to abstractly represent domain model elements as well as source-code artifacts. The key novelty in our approach is to leverage the relationships between source-code artifacts in a principled way to improve the mapping process. We describe experiments wherein we apply our approach to the task of matching two real, open-source applications to corresponding industry-standard domain models. We demonstrate the overall usefulness of our approach, as well as the role of our propagation techniques in improving the precision and recall of the mapping task.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a rare disease in which retinal blood vessels of premature infants fail to develop normally, and is one of the major causes of childhood blindness throughout the world. The Discrete Conditional Phase-type (DC-Ph) model consists of two components, the conditional component measuring the inter-relationships between covariates and the survival component which models the survival distribution using a Coxian phase-type distribution. This paper expands the DC-Ph models by introducing a support vector machine (SVM), in the role of the conditional component. The SVM is capable of classifying multiple outcomes and is used to identify the infant's risk of developing ROP. Class imbalance makes predicting rare events difficult. A new class decomposition technique, which deals with the problem of multiclass imbalance, is introduced. Based on the SVM classification, the length of stay in the neonatal ward is modelled using a 5, 8 or 9 phase Coxian distribution.

Review of the genus Bursaphelenchus Fuchs, 1937 (Nematoda, Aphelenchida) with some conclusions on the host-parasite and vector-parasite evolution.

INTRODUCTION AND GOALS: Genus Bursaphelenchus includes several pests of the world importance for the rural economy, the most dangerous are the Bursaphelenchus xylophilus (the pinewood nematode caused decline of the pine trees in south Asia and in one spot area in Europe, Portugal, Peninsula de Setubal) and the Bursaphelenchus cocophilus, causing the decline of coco-palm plantations in Carribean and Latin American regions. The peculiarity of the host-parasite association of the genus that the nematode life cycle includes three trophic components: plant (mostly a tree), insect vector and a fungus. Goals of the presentation is to list all species of the world fauna and all efficient diagnostic characters, then create the identification tool and analyze the similarity of species and possible ways and causes of the host-parasite evolution of the group. RESULTS: Complete list of species with synonymy and a catalogue of all efficient diagnostic characters with their states, selected from papers of the most experienced taxonomists of the genus, are given for the genus Bursaphelenchus. List of known records of Bursaphelenchus species with names of natural vectors and plants and their families is given (for world pests the most important groups of trees and insects are listed). The tabular, traditional and computer-aided keys are presented. Dendrograms of species relationships (UPGMA, standard distance: mean character difference) based on all efficient taxonomic characters and separately on the spicule characters only, are given. Discussion whether the species groups are natural or purely diagnostic ones is based on the relationships dendrograms and the vector and associated plant ranges of Bursaphelenchus species; the xylophilus species group (B. xylophilus, B. abruptus, B. baujardi, B. conicaudatus, B. eroshenkii, B. fraudulentus, B. kolymensis, B. luxuriosae; B. mucronatus), the hunti group (B. hunti, B. seani, B. kevini and B. fungivorus) are probably the natural ones. CONCLUSIONS: The parasitic nematode association includes three trophic components: plant, insect vector and fungus. The initial insect-plant complex Scolytidae-Pinaceae is changeable and only in rare occasions the change of the preferred vector to Cerambycidae (the xylophilus group), Hymenoptera (the hunti group) led to formation of the natural species-groups. From the analysis it is clear that although the vector range is changeable it is comparatively more important for the evolution of the genus Bursaphelenchus than associations with plants at the family level. Data on the fungi species (3rd component in natural Bursaphelenchus associations) are insufficient for the detailed comparative analysis.

Bayesian estimation and selection of nonlinear vector error correction models: The case of the sugar-ethanol-oil nexus in Brazil

Nonlinear adjustment toward long-run price equilibrium relationships in the sugar-ethanol-oil nexus in Brazil is examined. We develop generalized bivariate error correction models that allow for cointegration between sugar, ethanol, and oil prices, where dynamic adjustments are potentially nonlinear functions of the disequilibrium errors. A range of models are estimated using Bayesian Monte Carlo Markov Chain algorithms and compared using Bayesian model selection methods. The results suggest that the long-run drivers of Brazilian sugar prices are oil prices and that there are nonlinearities in the adjustment processes of sugar and ethanol prices to oil price but linear adjustment between ethanol and sugar prices.