Genetic Analysis of Tolerance to Rice Tungro Bacilliform Virus in Rice (Oryza sativa L.) Through Agroinoculation

Balimau Putih [an Indonesian cultivar tolerant to rice tungro bacilliform virus (RTBV)] was crossed with IR64 (RTBV, susceptible variety) to produce the three filial generations F1, F2 and F3. Agroinoculation was used to introduce RTBV into the test plants. RTBV tolerance was based on the RTBV level in plants by analysis of coat protein using enzyme-linked immunosorbent assay. The level of RTBV in cv. Balimau Putih was significantly lower than that of IR64 and the susceptible control, Taichung Native 1. Mean RTBV levels of the F1, F2 and F3 populations were comparable with one another and with the average of the parents. Results indicate that there was no dominance and an additive gene action may control the expression of tolerance to RTBV. Tolerance based on the level of RTBV coat protein was highly heritable (0.67) as estimated using the mean values of F3 lines, suggesting that selection for tolerance to RTBV can be performed in the early selfing generations using the technique employed in this study. The RTBV level had a negative correlation with plant height, but positive relationship with disease index value

Information sharing in the 21st century : progress and challenges

With the increasing threat of cyber and other attacks on critical infrastructure, governments throughout the world have been organizing industry to share information on possible threats. In Australia the Office of the Attorney General has formed Trusted Information Sharing Networks (TISN) for the various critical industries such as banking and electricity. Currently the majority of information for a TISN is shared at physical meetings. To meet cyber threats there are clearly limitations to physical meetings. Many of these limitations can be overcome by the creation of a virtual information sharing network (VISN). However there are many challenges to overcome in the design of a VISN both from a policy and technical viewpoint. We shall discuss some of these challenges in this talk.

Stretch : episodes of a recreation in progress

An episodic recreation of Hibberd's Stretch of the Imagination presented as a performance extract as part of the Enter the New Wave Symposium at Melbourne University September 2007.

Optimization of long rural feeders using a genetic algorithm

This paper describes the optimization of conductor size and the voltage regulator location & magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.

Dynamic coast control of train movement with genetic algorithm

The railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. Train service usually varies with the population activities throughout a day and train coordination and service regulation are then expected to meet the daily passengers' demand. Dwell time control at stations and fixed coasting point in an inter-station run are the current practices to regulate train service in most metro railway systems. However, a flexible and efficient train control and operation is not always possible. To minimize energy consumption of train operation and make certain compromises on the train schedule, coast control is an economical approach to balance run-time and energy consumption in railway operation if time is not an important issue, particularly at off-peak hours. The capability to identify the starting point for coasting according to the current traffic conditions provides the necessary flexibility for train operation. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigates the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. Further, a hierarchical genetic algorithm (HGA) is introduced here to identify the number of coasting points required according to the traffic conditions, and Minimum-Allele-Reserve-Keeper (MARK) is adopted as a genetic operator to achieve fitter solutions.

Railway junction conflict resolution by genetic algorithm

In general, simple and traditional methods are applied to resolve traffic conflicts at railway junctions. They are, however, either inefficient or computationally demanding. A simple genetic algorithm is presented to enable a search for a near optimal resolution to be carried out while meeting the constraints on generation evolution and minimising the search time.

Signalling layout for fixed-block railway lines with real-coded genetic algorithms.

Signalling layout design is one of the keys to railway operations with fixed-block signalling system and it also carries direct effect on overall train efficiency and safety. Based on an analysis to system objectives, this paper presents an optimization model with two objectives in order to devise an efficient signalling layout scheme. Taking into account the present railway line design practices in China, the paper describes steps of the computer-based signalling layout optimisation with real-coded genetic algorithms. A computer-aided system, based on train movement simulator, has also been employed to assist the optimisation process. A case study on a practical railway line has been conducted to make comparisons between the proposed GA-based approach and the current practices. The results illustrate the improved performance of the proposed approach in reducing signal block joints and shortening minimum train service headway.

Sustainable infrastructure project planning : progress in contemporary decision support tools

Most infrastructure project developments are complex in nature, particularly in the planning phase. During this stage, many vague alternatives are tabled - from the strategic to operational level. Human judgement and decision making are characterised by biases, errors and the use of heuristics. These factors are intangible and hard to measure because they are subjective and qualitative in nature. The problem with human judgement becomes more complex when a group of people are involved. The variety of different stakeholders may cause conflict due to differences in personal judgements. Hence, the available alternatives increase the complexities of the decision making process. Therefore, it is desirable to find ways of enhancing the efficiency of decision making to avoid misunderstandings and conflict within organisations. As a result, numerous attempts have been made to solve problems in this area by leveraging technologies such as decision support systems. However, most construction project management decision support systems only concentrate on model development and neglect fundamentals of computing such as requirement engineering, data communication, data management and human centred computing. Thus, decision support systems are complicated and are less efficient in supporting the decision making of project team members. It is desirable for decision support systems to be simpler, to provide a better collaborative platform, to allow for efficient data manipulation, and to adequately reflect user needs. In this chapter, a framework for a more desirable decision support system environment is presented. Some key issues related to decision support system implementation are also described.

Coast control of train movement with genetic algorithms

Railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. We present an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigate the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. The multiple coasting point control with hierarchical genetic algorithm (HGA) is then proposed to integrate the determination of the number of coasting points.