Risk-based surveillance for avian influenza control along poultry market chains in South China: The value of social network analysis.

Over the past two decades, the poultry sector in China went through a phase of tremendous growth as well as rapid intensification and concentration. Highly pathogenic avian influenza virus (HPAIV) subtype H5N1 was first detected in 1996 in Guangdong province, South China and started spreading throughout Asia in early 2004. Since then, control of the disease in China has relied heavily on wide-scale preventive vaccination combined with movement control, quarantine and stamping out. This strategy has been successful in drastically reducing the number of outbreaks during the past 5 years. However, HPAIV H5N1 is still circulating and is regularly isolated in traditional live bird markets (LBMs) where viral infection can persist, which represent a public health hazard for people visiting them. The use of social network analysis in combination with epidemiological surveillance in South China has identified areas where the success of current strategies for HPAI control in the poultry production sector may benefit from better knowledge of poultry trading patterns and the LBM network configuration as well as their capacity for maintaining HPAIV H5N1 infection. We produced a set of LBM network maps and estimated the associated risk of HPAIV H5N1 within LBMs and along poultry market chains, providing new insights into how live poultry trade and infection are intertwined. More specifically, our study provides evidence that several biosecurity factors such as daily cage cleaning, daily cage disinfection or manure processing contribute to a reduction in HPAIV H5N1 presence in LBMs. Of significant importance is that the results of our study also show the association between social network indicators and the presence of HPAIV H5N1 in specific network configurations such as the one represented by the counties of origin of the birds traded in LBMs. This new information could be used to develop more targeted and effective control interventions.

Ranking of Prospective New Generation Location for a Power Network in a Deregulated System

In developing countries high rate of growth in demand of electric energy is felt, and so the addition of new generating units becomes necessary. In deregulated power systems private generating stations are encouraged to add new generations. Finding the appropriate location of new generator to be installed can be obtained by running repeated power flows, carrying system studies like analyzing the voltage profile, voltage stability, loss analysis etc. In this paper a new methodology is proposed which will mainly consider the existing network topology into account. A concept of T-index is introduced in this paper, which considers the electrical distances between generator and load nodes.This index is used for ranking significant new generation expansion locations and also indicates the amount of permissible generations that can be installed at these new locations. This concept facilitates for the medium and long term planning of power generation expansions within the available transmission corridors. Studies carried out on a sample 7-bus system, EHV equivalent 24-bus system and IEEE 39 bus system are presented for illustration purpose.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time

Nested association mapping (NAM) offers power to dissect complex, quantitative traits. This study made use of a recently developed sorghum backcross (BC)-NAM population to dissect the genetic architecture of flowering time in sorghum; to compare the QTL identified with other genomic regions identified in previous sorghum and maize flowering time studies and to highlight the implications of our findings for plant breeding. A subset of the sorghum BC-NAM population consisting of over 1,300 individuals from 24 families was evaluated for flowering time across multiple environments. Two QTL analysis methodologies were used to identify 40 QTLs with predominately small, additive effects on flowering time; 24 of these co-located with previously identified QTL for flowering time in sorghum and 16 were novel in sorghum. Significant synteny was also detected with the QTL for flowering time detected in a comparable NAM resource recently developed for maize (Zea mays) by Buckler et al. (Science 325:714-718, 2009). The use of the sorghum BC-NAM population allowed us to catalogue allelic variants at a maximal number of QTL and understand their contribution to the flowering time phenotype and distribution across diverse germplasm. The successful demonstration of the power of the sorghum BC-NAM population is exemplified not only by correspondence of QTL previously identified in sorghum, but also by correspondence of QTL in different taxa, specifically maize in this case. The unification across taxa of the candidate genes influencing complex traits, such as flowering time can further facilitate the detailed dissection of the genetic control and causal genes.

Modelling temperature, photoperiod and vernalization responses of Brunonia australis (Goodeniaceae) and Calandrinia sp (Portulacaceae) to predict flowering time

Crop models for herbaceous ornamental species typically include functions for temperature and photoperiod responses, but very few incorporate vernalization, which is a requirement of many traditional crops. This study investigated the development of floriculture crop models, which describe temperature responses, plus photoperiod or vernalization requirements, using Australian native ephemerals Brunonia australis and Calandrinia sp. A novel approach involved the use of a field crop modelling tool, DEVEL2. This optimization program estimates the parameters of selected functions within the development rate models using an iterative process that minimizes sum of squares residual between estimated and observed days for the phenological event. Parameter profiling and jack-knifing are included in DEVEL2 to remove bias from parameter estimates and introduce rigour into the parameter selection process. Development rate of B. australis from planting to first visible floral bud (VFB) was predicted using a multiplicative approach with a curvilinear function to describe temperature responses and a broken linear function to explain photoperiod responses. A similar model was used to describe the development rate of Calandrinia sp., except the photoperiod function was replaced with an exponential vernalization function, which explained a facultative cold requirement and included a coefficient for determining the vernalization ceiling temperature. Temperature was the main environmental factor influencing development rate for VFB to anthesis of both species and was predicted using a linear model. The phenology models for B. australis and Calandrinia sp. described development rate from planting to VFB and from VFB to anthesis in response to temperature and photoperiod or vernalization and may assist modelling efforts of other herbaceous ornamental plants. In addition to crop management, the vernalization function could be used to identify plant communities most at risk from predicted increases in temperature due to global warming.

Low temperature exposure of root system and inflorescence affected flowering and fruit set in 'Chardonnay' grapevines (Vitis vinifera)

The mechanisms by which low temperature affects flowering and fruit set of grapevines are poorly understood, as is the specific response of the grapevine root system and inflorescence to low temperature effects that reduce fruit set. This study aimed to determine the responses of the root system and inflorescence of the grapevine 'Chardonnay' to low temperature (10 degrees C) during flowering, and considered the possible mechanisms of low temperature effects on those parts. Temperature treatments of 10 degrees C or 20 degrees C were imposed to potted 'Chardonnay' grapevines in a glasshouse for up to two weeks during the early stages of flowering. When the root system alone was exposed to 10 degrees C (with the rest of the plant at 20 degrees C) during flowering, the number of attached berries and percentage fruit set were significantly reduced by 50 % than when the root system alone was exposed to 20 degrees C. Whereas, exposure of the inflorescence alone to 10 degrees C (with the rest of the plant at 20 degrees C) delayed flowering, allowed rachis to grow longer, and increased both the number of attached berries (from 22 to 62 per vine) and fruit set (from 8 % to, 20 %), than when the inflorescence alone was exposed to 20 degrees C. This study will enhance our understanding of the possible mechanisms of low temperature effects on grapevine fruit set and productivity.

Genetic control of flowering in spotted gum, Corymbia citriodora subsp. variegata and C. maculata

Genetically controlled asynchrony in anthesis is an effective barrier to gene flow between planted and native forests. We investigated the degree of genetically controlled variation in the timing of key floral developmental stages in a major plantation species in subtropical Australia, Corymbia citriodora subsp. variegata K.D. Hill and L.A.S Johnson, and its relative C. maculata K.D. Hill and L.A.S. Johnson. Flowering observations were made in a common garden planting at Bonalbo in northern New South Wales in spring on 1855 trees from eight regions over three consecutive years, and monthly on a subset of 208 trees for 12 months. Peak anthesis time was stable over years and observations from translocated trees tended to be congruent with the observations in native stands, suggesting strong genetic control of anthesis time. A cluster of early flowering provenances was identified from the north-east of the Great Dividing Range. The recognition of a distinct flowering race from this region accorded well with earlier evidence of adaptive differentiation of populations from this region and geographically-structured genetic groupings in C. citriodora subsp. variegata. The early flowering northern race was more fecund, probably associated with its disease tolerance and greater vigour. Bud abundance fluctuated extensively at the regional level across 3 years suggesting bud abundance was more environmentally labile than timing of anthesis. Overall the level of flowering in the planted stand (age 12 years) was low (8–12% of assessed trees with open flowers), and was far lower than in nearby native stands. Low levels of flowering and asynchrony in peak anthesis between flowering races of C. citriodora subsp. variegata may partially mitigate a high likelihood of gene flow where the northern race is planted in the south of the species range neighbouring native stands

Single network adaptive critic design for power system stabilisers

The recently developed single network adaptive critic (SNAC) design has been used in this study to design a power system stabiliser (PSS) for enhancing the small-signal stability of power systems over a wide range of operating conditions. PSS design is formulated as a discrete non-linear quadratic regulator problem. SNAC is then used to solve the resulting discrete-time optimal control problem. SNAC uses only a single critic neural network instead of the action-critic dual network architecture of typical adaptive critic designs. SNAC eliminates the iterative training loops between the action and critic networks and greatly simplifies the training procedure. The performance of the proposed PSS has been tested on a single machine infinite bus test system for various system and loading conditions. The proposed stabiliser, which is relatively easier to synthesise, consistently outperformed stabilisers based on conventional lead-lag and linear quadratic regulator designs.

Protection of a Static Frequency Converter fed traction network, a case study

Aurizon, Australia's largest rail freight operator, is introducing the Static Frequency Converter (SFC) technology into its electric railway network as part of the Bauhinia Electrification Project. The introduction of SFCs has significant implications on the protection systems of the 50kV traction network. The traditional distance protection calculation method does not work in this configuration because of the effect that the SFC in combination with the remote grid has on the apparent impedance, and was substantially reviewed. The standard overcurrent (OC) protection scheme is not suitable due to the minimum fault level being below the maximum load level and was revised to incorporate directionality and under-voltage inhibit. Delta protection was reviewed to improve sensitivity. A new protection function was introduced to prevent back-feeding faults in the transmission network through the grid connection. Protection inter-tripping was included to ensure selectivity between the SFC protection and the system downstream.

Residential tariff structure and battery energy storage impact in queensland LV network

Distributed renewable energy has become a significant contender in the supply of power in the distribution network in Queensland and throughout the world. As the cost of battery storage falls, distribution utilities turn their attention to the impacts of battery storage and other storage technologies on the low voltage (LV) network. With access to detailed residential energy usage data, Energex's available residential tariffs are investigated for their effectiveness in providing customers with financial incentives to move to Time-of Use based tariffs and to reward use of battery storage.

Relaxation Time of Chemical Reactions from Network Thermodynamics

The relationship for the relaxation time(s) of a chemical reaction in terms of concentrations and rate constants has been derived from the network thermodynamic approach developed by Oster, Perelson, and Katchalsky.Generally, it is necessary to draw the bond graph and the “network analogue” of the reaction scheme, followed by loop or nodal analysis of the network and finally solving of the resulting differential equations. In the case of single-step reactions, however, it is possible to obtain an expression for the relaxation time. This approach is simpler and elegant and has certain advantages over the usual kinetic method. The method has been illustrated by taking different reaction schemes as examples.

Data mining for telecommunications network log analysis

Telecommunications network management is based on huge amounts of data that are continuously collected from elements and devices from all around the network. The data is monitored and analysed to provide information for decision making in all operation functions. Knowledge discovery and data mining methods can support fast-pace decision making in network operations. In this thesis, I analyse decision making on different levels of network operations. I identify the requirements decision-making sets for knowledge discovery and data mining tools and methods, and I study resources that are available to them. I then propose two methods for augmenting and applying frequent sets to support everyday decision making. The proposed methods are Comprehensive Log Compression for log data summarisation and Queryable Log Compression for semantic compression of log data. Finally I suggest a model for a continuous knowledge discovery process and outline how it can be implemented and integrated to the existing network operations infrastructure.

The Most Probable Bayesian Network and Beyond

This doctoral dissertation introduces an algorithm for constructing the most probable Bayesian network from data for small domains. The algorithm is used to show that a popular goodness criterion for the Bayesian networks has a severe sensitivity problem. The dissertation then proposes an information theoretic criterion that avoids the problem.