Improved management of key northern region weeds: diverse problems, diverse solutions

The cropping region of northern Australia has a diverse range of cropping systems and weed flora. A fallow phase is commonly required between crops to enable the accumulation of stored soil water in these farming systems dominated by reduced tillage. During the fallow phase, weed control is important and is heavily reliant on herbicides. The most commonly used herbicide has been glyphosate. As a result of over-reliance on glyphosate, there are now seven confirmed glyphosate-resistant weeds and several glyphosate-tolerant species common in the region. As a result, the control of summer fallow weeds is become more complex. This paper outlines project work investigating improved weed control for summer fallows in the northern cropping region. Areas of research include weed ecology, chemical and non-chemical tactics, glyphosate resistance and resistance surveys. The project also has an economic and extension component. As a result of our research we have a better understanding of the ecology of major northern weeds and spread of glyphosate resistance in the region. We have identified and defined alternative herbicide and non-chemical approaches for the effective control of summer fallow weeds and have extended our research effectively to industry.

Optimization of gasket thickness in a Bridgman anvil system

The effect of gasket thickness on the pressure in the Bridgman anvil system has been studied experimentally. The existence of the optimum thickness from the experimental data has been interpreted in a theoretical model of stress distribution in an anvil system. Review of Scientific Instruments is copyrighted by The American Institute of Physics.

Weeds of Direct-Seeded Rice in Asia: Problems and Opportunities

Rice production symbolizes the single largest land use for food production on the Earth. The significance of this cereal as a source of energy and income seems overwhelming for millions of people in Asia, representing 90% of global rice production and consumption. Estimates indicate that the burgeoning population will need 25% more rice by 2025 than today's consumption. As the demand for rice is increasing, its production in Asia is threatened by a dwindling natural resource base, socioeconomic limitations, and uncertainty of climatic optima. Transplanting in puddled soil with continuous flooding is a common method of rice crop establishment in Asia. There is a dire need to look for rice production technologies that not only cope with existing limitations of transplanted rice but also are viable, economical, and secure for future food demand.Direct seeding of rice has evolved as a potential alternative to the current detrimental practice of puddling and nursery transplanting. The associated benefits include higher water productivity, less labor and energy inputs, less methane emissions, elimination of time and edaphic conflicts in the rice-wheat cropping system, and early crop maturity. Realization of the yield potential and sustainability of this resource-conserving rice production technique lies primarily in sustainable weed management, since weeds have been recognized as the single largest biological constraint in direct-seeded rice (DSR). Weed competition can reduce DSR yield by 30-80% and even complete crop failure can occur under specific conditions. Understanding the dynamics and outcomes of weed-crop competition in DSR requires sound knowledge of weed ecology, besides production factors that influence both rice and weeds, as well as their association. Successful adoption of direct seeding at the farmers' level in Asia will largely depend on whether farmers can control weeds and prevent shifts in weed populations from intractable weeds to more difficult-to-control weeds as a consequence of direct seeding. Sustainable weed management in DSR comprises all the factors that give DSR a competitive edge over weeds regarding acquisition and use of growth resources. This warrants the need to integrate various cultural practices with weed control measures in order to broaden the spectrum of activity against weed flora. A weed control program focusing entirely on herbicides is no longer ecologically sound, economically feasible, and effective against diverse weed flora and may result in the evolution of herbicide-resistant weed biotypes. Rotation of herbicides with contrasting modes of action in conjunction with cultural measures such as the use of weed-competitive rice cultivars, sowing time, stale seedbed technique, seeding rate, crop row spacing, fertilizer and water inputs and their application method/timing, and manual and mechanical hoeing can prove more effective and need to be optimized keeping in view the type and intensity of weed infestation. This chapter tries to unravel the dynamics of weed-crop competition in DSR. Technological issues, limitations associated with DSR, and opportunities to combat the weed menace are also discussed as a pragmatic approach for sustainable DSR production. A realistic approach to secure yield targets against weed competition will combine the abovementioned strategies and tactics in a coordinated manner. This chapter further suggests the need of multifaceted and interdisciplinary research into ecologically based weed management, as DSR seems inevitable in the near future.

Ant colony optimization for reporting cell planning in mobile computing using selective paging strategy

Location management problem that arise in mobile computing networks is addressed. One method used in location management is to designate sonic of the cells in the network as "reporting cells". The other cells in the network are "non-reporting cells". Finding an optimal set of reporting cells (or reporting cell configuration) for a given network. is a difficult combinatorial optimization problem. In fact this is shown to be an NP-complete problem. in an earlier study. In this paper, we use the selective paging strategy and use an ant colony optimization method to obtain the best/optimal set of reporting cells for a given a network.

Solution of a class of vibration problems by flowgraph methods

A 4-degree-of-freedom single-input system and a 3-degree-of-freedom multi-input system are solved by the Coates', modified Coates' and Chan-Mai flowgraph methods. It is concluded that the Chan-Mai flowgraph method is superior to other flowgraph methods in such cases.

A parametric differentiation version with finite-difference scheme applicable to a class of problems in boundary layer flow with massive blowing

A numerical procedure, based on the parametric differentiation and implicit finite difference scheme, has been developed for a class of problems in the boundary-layer theory for saddle-point regions. Here, the results are presented for the case of a three-dimensional stagnation-point flow with massive blowing. The method compares very well with other methods for particular cases (zero or small mass blowing). Results emphasize that the present numerical procedure is well suited for the solution of saddle-point flows with massive blowing, which could not be solved by other methods.

Computational Methods for Reconstruction and Analysis of Genome-Scale Metabolic Networks

Metabolism is the cellular subsystem responsible for generation of energy from nutrients and production of building blocks for larger macromolecules. Computational and statistical modeling of metabolism is vital to many disciplines including bioengineering, the study of diseases, drug target identification, and understanding the evolution of metabolism. In this thesis, we propose efficient computational methods for metabolic modeling. The techniques presented are targeted particularly at the analysis of large metabolic models encompassing the whole metabolism of one or several organisms. We concentrate on three major themes of metabolic modeling: metabolic pathway analysis, metabolic reconstruction and the study of evolution of metabolism. In the first part of this thesis, we study metabolic pathway analysis. We propose a novel modeling framework called gapless modeling to study biochemically viable metabolic networks and pathways. In addition, we investigate the utilization of atom-level information on metabolism to improve the quality of pathway analyses. We describe efficient algorithms for discovering both gapless and atom-level metabolic pathways, and conduct experiments with large-scale metabolic networks. The presented gapless approach offers a compromise in terms of complexity and feasibility between the previous graph-theoretic and stoichiometric approaches to metabolic modeling. Gapless pathway analysis shows that microbial metabolic networks are not as robust to random damage as suggested by previous studies. Furthermore the amino acid biosynthesis pathways of the fungal species Trichoderma reesei discovered from atom-level data are shown to closely correspond to those of Saccharomyces cerevisiae. In the second part, we propose computational methods for metabolic reconstruction in the gapless modeling framework. We study the task of reconstructing a metabolic network that does not suffer from connectivity problems. Such problems often limit the usability of reconstructed models, and typically require a significant amount of manual postprocessing. We formulate gapless metabolic reconstruction as an optimization problem and propose an efficient divide-and-conquer strategy to solve it with real-world instances. We also describe computational techniques for solving problems stemming from ambiguities in metabolite naming. These techniques have been implemented in a web-based sofware ReMatch intended for reconstruction of models for 13C metabolic flux analysis. In the third part, we extend our scope from single to multiple metabolic networks and propose an algorithm for inferring gapless metabolic networks of ancestral species from phylogenetic data. Experimenting with 16 fungal species, we show that the method is able to generate results that are easily interpretable and that provide hypotheses about the evolution of metabolism.

Problems and Algorithms for Sequence Segmentations

The analysis of sequential data is required in many diverse areas such as telecommunications, stock market analysis, and bioinformatics. A basic problem related to the analysis of sequential data is the sequence segmentation problem. A sequence segmentation is a partition of the sequence into a number of non-overlapping segments that cover all data points, such that each segment is as homogeneous as possible. This problem can be solved optimally using a standard dynamic programming algorithm. In the first part of the thesis, we present a new approximation algorithm for the sequence segmentation problem. This algorithm has smaller running time than the optimal dynamic programming algorithm, while it has bounded approximation ratio. The basic idea is to divide the input sequence into subsequences, solve the problem optimally in each subsequence, and then appropriately combine the solutions to the subproblems into one final solution. In the second part of the thesis, we study alternative segmentation models that are devised to better fit the data. More specifically, we focus on clustered segmentations and segmentations with rearrangements. While in the standard segmentation of a multidimensional sequence all dimensions share the same segment boundaries, in a clustered segmentation the multidimensional sequence is segmented in such a way that dimensions are allowed to form clusters. Each cluster of dimensions is then segmented separately. We formally define the problem of clustered segmentations and we experimentally show that segmenting sequences using this segmentation model, leads to solutions with smaller error for the same model cost. Segmentation with rearrangements is a novel variation to the segmentation problem: in addition to partitioning the sequence we also seek to apply a limited amount of reordering, so that the overall representation error is minimized. We formulate the problem of segmentation with rearrangements and we show that it is an NP-hard problem to solve or even to approximate. We devise effective algorithms for the proposed problem, combining ideas from dynamic programming and outlier detection algorithms in sequences. In the final part of the thesis, we discuss the problem of aggregating results of segmentation algorithms on the same set of data points. In this case, we are interested in producing a partitioning of the data that agrees as much as possible with the input partitions. We show that this problem can be solved optimally in polynomial time using dynamic programming. Furthermore, we show that not all data points are candidates for segment boundaries in the optimal solution.

Multi-user resource-sharing problem for the Internet

In this thesis we study a series of multi-user resource-sharing problems for the Internet, which involve distribution of a common resource among participants of multi-user systems (servers or networks). We study concurrently accessible resources, which for end-users may be exclusively accessible or non-exclusively. For all kinds we suggest a separate algorithm or a modification of common reputation scheme. Every algorithm or method is studied from different perspectives: optimality of protocols, selfishness of end users, fairness of the protocol for end users. On the one hand the multifaceted analysis allows us to select the most suited protocols among a set of various available ones based on trade-offs of optima criteria. On the other hand, the future Internet predictions dictate new rules for the optimality we should take into account and new properties of the networks that cannot be neglected anymore. In this thesis we have studied new protocols for such resource-sharing problems as the backoff protocol, defense mechanisms against Denial-of-Service, fairness and confidentiality for users in overlay networks. For backoff protocol we present analysis of a general backoff scheme, where an optimization is applied to a general-view backoff function. It leads to an optimality condition for backoff protocols in both slot times and continuous time models. Additionally we present an extension for the backoff scheme in order to achieve fairness for the participants in an unfair environment, such as wireless signal strengths. Finally, for the backoff algorithm we suggest a reputation scheme that deals with misbehaving nodes. For the next problem -- denial-of-service attacks, we suggest two schemes that deal with the malicious behavior for two conditions: forged identities and unspoofed identities. For the first one we suggest a novel most-knocked-first-served algorithm, while for the latter we apply a reputation mechanism in order to restrict resource access for misbehaving nodes. Finally, we study the reputation scheme for the overlays and peer-to-peer networks, where resource is not placed on a common station, but spread across the network. The theoretical analysis suggests what behavior will be selected by the end station under such a reputation mechanism.