Comparative productivity of irrigated short-term ryegrass (Lolium multiflorum) pasture receiving nitrogen, grown alone or in a mixture with white (Trifolium repens) and Persian (T. resupinatum) clovers

Dairy farms in subtropical Australia use irrigated, annually sown short-term ryegrass (Lolium multiflorum) or mixtures of short-term ryegrass and white (Trifolium repens) and Persian (shaftal) (T. resupinatum) clover during the winter-spring period in all-year-round milk production systems. A series of small plot cutting experiments was conducted in 3 dairying regions (tropical upland, north Queensland, and subtropical southeast Queensland and northern New South Wales) to determine the most effective rate and frequency of application of nitrogen (N) fertiliser. The experiments were not grazed, nor was harvested material returned to the plots, after sampling. Rates up to 100 kg N/ha.month (as urea or calcium ammonium nitrate) and up to 200 kg N/ha every 2 months (as urea) were applied to pure stands of ryegrass in 1991. In 1993 and 1994, urea, at rates up to 150 kg N/ha.month and to 200 kg N/ha every 2 months, was applied to pure stands of ryegrass; urea, at rates up to 50 kg N/ha.month, was also applied to ryegrass-clover mixtures. The results indicate that applications of 50-85 kg N/ha.month can be recommended for short-term ryegrass pastures throughout the subtropics and tropical uplands of eastern Australia, irrespective of soil type. At this rate, dry matter yields will reach about 90% of their potential, forage nitrogen concentration will be increased, there is minimal risk to stock from nitrate poisoning and there will be no substantial increase in soil N. The rate of N for ryegrass-clover pastures is slightly higher than for pure ryegrass but, at these rates, the clover component will be suppressed. However, increased ryegrass yields and higher forage nitrogen concentrations will compensate for the reduced clover component. At application rates up to 100 kg N/ha.month, build-up of NO3--N and NH4+-N in soil was generally restricted to the surface layers (0-20 cm) of the soil, but there was a substantial increase throughout the soil profile at 150 kg N/ha.month. The build-up of NO3--N and NH4+-N was greater and was found at lower rates on the lighter soil compared with heavy clays. Generally, most of the soil N was in the NO3--N form and most was in the top 20 cm.

Clearigate ® Treatments for Control of Giant Salvinia

Results of recent field trials using the chelated copper formulation Clearigate® 4 showed that applying a 20% solution by volume was effective for controlling populations of giant salvinia in irrigation canals. 5 Lower rates may be efficacious, thereby reducing chemical use and cost; however, little is known about the dose-response effects of Clearigate® against giant salvinia. The objective of this study was to determine the effective rate range of chelated copper applied as Clearigate® for control of giant salvinia.

O fator comum associado à dinâmica de preços das commodities : a relação de cointegração e o fator dinâmico

Este trabalho analisa a importância dos fatores comuns na evolução recente dos preços dos metais no período entre 1995 e 2013. Para isso, estimam-se modelos cointegrados de VAR e também um modelo de fator dinâmico bayesiano. Dado o efeito da financeirização das commodities, DFM pode capturar efeitos dinâmicos comuns a todas as commodities. Além disso, os dados em painel são aplicados para usar toda a heterogeneidade entre as commodities durante o período de análise. Nossos resultados mostram que a taxa de juros, taxa efetiva do dólar americano e também os dados de consumo têm efeito permanente nos preços das commodities. Observa-se ainda a existência de um fator dinâmico comum significativo para a maioria dos preços das commodities metálicas, que tornou-se recentemente mais importante na evolução dos preços das commodities.

Ceticismo ou reputação nas recompras de ações?

Este trabalho investiga as recompras de ações no Brasil pelo prisma da reputação das empresas anunciadoras. Por meio de uma base de dados ainda inexplorada, o formulário de referência anual, que possibilita a diferenciação dos anúncios de recompras pelas quantidades efetivamente compradas. Com metodologia de estudo de evento e regressão, foram analisados os anúncios de recompras de ações da própria companhia em mercado aberto para o período compreendido entre os anos de 2007 a 2014. Os principais resultados encontrados foram à existência de reputação para empresas que apresentam uma taxa efetiva de recompra mais elevada e uma penalização para as empresas que apresentam baixas taxas efetivas de recompra em anúncios anteriores, quanto mais próximo de zero a taxa efetiva de recompra do anuncio anterior, menor é o efeito de um novo anúncio. Por fim, foi encontrado retorno anormal acumulado significativo no mercado acionário brasileiro para empresas que anunciam recompra de ações próprias no mercado aberto de 1,12% no primeiro dia, 1,56% no quinto dia e 1,97% ao final do primeiro mês. Para eventos com histórico, foi de 0,98% no primeiro dia, 1,33% no quinto dia e 1,38% ao final do primeiro mês. Já para o caso nos quais os eventos foram antecedidos imediatamente por anúncios de recompra com taxas efetivas de recompras de no mínimo 70%, foi de 1,42% no primeiro dia, 2,70% no quinto dia e 5,09% ao final do primeiro mês.

Maritime transport liberalization and the challenges to further its implementation in Chile

Includes bibliography

The fiscal impact of trade liberalization and commodity price fluctuation: the case of Dominican Republic, 1980-1998

Includes bibliography

Decoding Delay Minimization in Inter-Session Network Coding

Intra-session network coding has been shown to offer significant gains in terms of achievable throughput and delay in settings where one source multicasts data to several clients. In this paper, we consider a more general scenario where multiple sources transmit data to sets of clients over a wireline overlay network. We propose a novel framework for efficient rate allocation in networks where intermediate network nodes have the opportunity to combine packets from different sources using randomized network coding. We formulate the problem as the minimization of the average decoding delay in the client population and solve it with a gradient-based stochastic algorithm. Our optimized inter-session network coding solution is evaluated in different network topologies and is compared with basic intra-session network coding solutions. Our results show the benefits of proper coding decisions and effective rate allocation for lowering the decoding delay when the network is used by concurrent multicast sessions.

Using catch rate data for simple cost-effective quota setting in the Australian spanner crab (Ranina ranina) fishery.

For many fisheries, there is a need to develop appropriate indicators, methodologies, and rules for sustainably harvesting marine resources. Complexities of scientific and financial factors often prevent addressing these, but new methodologies offer significant improvements on current and historical approaches. The Australian spanner crab fishery is used to demonstrate this. Between 1999 and 2006, an empirical management procedure using linear regression of fishery catch rates was used to set the annual total allowable catch (quota). A 6-year increasing trend in catch rates revealed shortcomings in the methodology, with a 68% increase in quota calculated for the 2007 fishing year. This large quota increase was prevented by management decision rules. A revised empirical management procedure was developed subsequently, and it achieved a better balance between responsiveness and stability. Simulations identified precautionary harvest and catch rate baselines to set quotas that ensured sustainable crab biomass and favourable performance for management and industry. The management procedure was simple to follow, cost-effective, robust to strong trends and changes in catch rates, and adaptable for use in many fisheries. Application of such “tried-and-tested” empirical systems will allow improved management of both data-limited and data-rich fisheries.

TCP-DR an effective protocol for infrastructure based wireless networks

TCP is a dominant protocol for consistent communication over the internet. It provides flow, congestion and error control mechanisms while using wired reliable networks. Its congestion control mechanism is not suitable for wireless links where data corruption and its lost rate are higher. The physical links are transparent from TCP that takes packet losses due to congestion only and initiates congestion handling mechanisms by reducing transmission speed. This results in wasting already limited available bandwidth on the wireless links. Therefore, there is no use to carry out research on increasing bandwidth of the wireless links until the available bandwidth is not optimally utilized. This paper proposed a hybrid scheme called TCP Detection and Recovery (TCP-DR) to distinguish congestion, corruption and mobility related losses and then instructs the data sending host to take appropriate action. Therefore, the link utilization is optimal while losses are either due to high bit error rate or mobility.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.