941 resultados para wildlife damage management


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This paper presents the feasibility of using structural modal strain energy as a parameter employed in correlation- based damage detection method for truss bridge structures. It is an extension of the damage detection method adopting multiple damage location assurance criterion. In this paper, the sensitivity of modal strain energy to damage obtained from the analytical model is incorporated into the correlation objective function. Firstly, the sensitivity matrix of modal strain energy to damage is conducted offline, and for an arbitrary damage case, the correlation coefficient (objective function) is calculated by multiplying the sensitivity matrix and damage vector. Then, a genetic algorithm is used to iteratively search the damage vector maximising the correlation between the corresponding modal strain energy change (hypothesised) and its counterpart in measurement. The proposed method is simulated and compared with the conventional methods, e.g. frequency-error method, coordinate modal assurance criterion and multiple damage location assurance criterion using mode shapes on a numerical truss bridge structure. The result demonstrates the modal strain energy correlation method is able to yield acceptable damage detection outcomes with less computing efforts, even in a noise contaminated condition.

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Structural health is a vital aspect of infrastructure sustainability. As a part of a vital infrastructure and transportation network, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs are a difficult burden for infrastructure owners. The structural health monitoring (SHM) systems proposed recently are incorporated with vibration-based damage detection techniques, statistical methods and signal processing techniques and have been regarded as efficient and economical ways to assess bridge condition and foresee probable costly failures. In this chapter, the recent developments in damage detection and condition assessment techniques based on vibration-based damage detection and statistical methods are reviewed. The vibration-based damage detection methods based on changes in natural frequencies, curvature or strain modes, modal strain energy, dynamic flexibility, artificial neural networks, before and after damage, and other signal processing methods such as Wavelet techniques, empirical mode decomposition and Hilbert spectrum methods are discussed in this chapter.

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Many farmers in South and Southeast Asia describe rice tungro disease as a cancer disease because of the severe damage it causes and the difficulty of controlling it (121). As the most important of the 14 rice viral diseases, tungro was first recognized as a leafhopper-transmitted virus disease in 1963 (88). However, tungro, which means “degenerated growth” in a Filipino dialect, has a much longer history. It is almost certain that tungro was responsible for a disease outbreak that occurred in 1859 in Indonesia, which was referred to at the time as mentek (83). In the past, a variety of names has been given to tungro, including accep na pula in the Philippines, penyakit merah in Malaysia, and yelloworange leaf in Thailand (83).

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Marinas currently exist primarily to service recreational boats, and these vessels are a potential cause of both problems and opportunities in environmental management. Thus, on the one hand, destructive fuel and other pollutants may be expelled, boat wakes can cause littoral soil erosion, physical damage results from collisions with marine life, and litter and noise pollution occur in otherwise pristine habitat. Boats also provide access to otherwise inaccessible natural environments for educational and other management reasons. In this study, boat traffic at three large marinas located along the Queensland coastline has been field surveyed for introductory information. No attempt was made at this juncture to survey the behaviour of the boat crews and passengers (concerning actual destinations, activities on board, etc. or to survey the recreational boat industry. Such studies rely on boat registration records and personal questionnaires. Some other surveys relating to fishing draw on boat ramp surveys and direct submissions by recreational fishers; these provide some data on daily usage of boat ramps, but without particular attention to boats. We believe field observations of overall boat activities in the water are necessary for environmental management purposes. The aim of the survey was to provide information to help prioritize the potential impacts that boats’ activities have on the surrounding natural environment. Any impact by boats will be a product of their numbers, size, frequency of movement, carrying capacity and routes/destinations. The severity of impacts will dictate the appropriate management action.

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Dynamic computer simulation techniques are used to develop and apply a multi-criteria procedure, incorporating changes in natural frequencies, modal flexibility and the modal strain energy, for damage localisation in beams and plates. Numerically simulated modal data obtained through finite element analyses are used to develop algorithms based on changes of modal flexibility and modal strain energy before and after damage and used as the indices for assessment of the state of structural health. The proposed procedure is illustrated through its application to flexural members under different damage scenarios and the results confirm its feasibility for damage assessment.

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Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

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Acoustic emission (AE) analysis is one of the several diagnostic techniques available nowadays for structural health monitoring (SHM) of engineering structures. Some of its advantages over other techniques include high sensitivity to crack growth and capability of monitoring a structure in real time. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). In AE technique, these stress waves are recorded by means of suitable sensors placed on the surface of a structure. Recorded signals are subsequently analysed to gather information about the nature of the source. By enabling early detection of crack growth, AE technique helps in planning timely retrofitting or other maintenance jobs or even replacement of the structure if required. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. Large amount of data is generated during AE testing, hence effective data analysis is necessary, especially for long term monitoring uses. Appropriate analysis of AE data for quantification of damage level is an area that has received considerable attention. Various approaches available for damage quantification for severity assessment are discussed in this paper, with special focus on civil infrastructure such as bridges. One method called improved b-value analysis is used to analyse data collected from laboratory testing.

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The Black Rat (Rattus rattus), a global pest within the macadamia production industry, causes up to 30% crop damage in Australian orchards. During early stages of production in Australia, research demonstrated the importance of non crop adjacent habitats as significant in affecting the patterns of crop damage seen throughout orchards. Where once rodent damage was limited to the outside edges of orchard blocks, growers are now reporting finding crop damage throughout entire orchards. This study therefore aims to explore the spatial patterns of rodent distribution and damage now occurring in Australian macadamia orchards. We show that rodent damage and rodent distribution in these newer production regions differ from that shown in previous Australian research. Previous Australian research has shown damage patterns which were associated with the edges of orchard blocks however this study demonstrates a more widespread damage distribution. In the current study there is no relationship between rodent damage and the orchard edge. Arboreal rodent nests were identified within these newer orchard systems, suggesting rodents are residing within the tree component of the orchard system and not dependent on adjacent non-crop habitat for shelter. Results from this study confirm that rodents have modified their nesting and foraging behaviour in newer orchards systems in Australia. We suggest that this is a response of increased and prolonged availability of macadamia nuts in newer production regions enabling populations to be maintained throughout the year. Management strategies will require modification if control is to be achieved.

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In light of the high stakes of the deepwater horizon civil trial and the important precedent-setting role that the case will have on the assessment of future marine disasters, the methodologies underpinning the calculations of damage on both sides will be subjected to considerable scrutiny. Despite the importance of the case, however, there seems to be a pronounced lack of convergence about it in the academic literature. Contributions from scientific journals frequently make comparisons to the Ixtoc I oil spill off the coast of Mexico in 1979; the legal literature, by stark contrast, seems to be much more focused on the Exxon Valdez spill that occurred off the shores of Alaska in 1989. This paper accordingly calls for a more thorough consideration of other analogs beyond the Exxon Valdez spill—most notably, the Ixtoc I incident—in arriving at an assessment of the damage caused by the Deepwater Horizon disaster.

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The feral pig, Sus scrofa, is a widespread and abundant invasive species in Australia. Feral pigs pose a significant threat to the environment, agricultural industry, and human health, and in far north Queensland they endanger World Heritage values of the Wet Tropics. Historical records document the first introduction of domestic pigs into Australia via European settlers in 1788 and subsequent introductions from Asia from 1827 onwards. Since this time, domestic pigs have been accidentally and deliberately released into the wild and significant feral pig populations have become established, resulting in the declaration of this species as a class 2 pest in Queensland. The overall objective of this study was to assess the population genetic structure of feral pigs in far north Queensland, in particular to enable delineation of demographically independent management units. The identification of ecologically meaningful management units using molecular techniques can assist in targeting feral pig control to bring about effective long-term management. Molecular genetic analysis was undertaken on 434 feral pigs from 35 localities between Tully and Innisfail. Seven polymorphic and unlinked microsatellite loci were screened and fixation indices (FST and analogues) and Bayesian clustering methods were used to identify population structure and management units in the study area. Sequencing of the hyper-variable mitochondrial control region (D-loop) of 35 feral pigs was also examined to identify pig ancestry. Three management units were identified in the study at a scale of 25 to 35 km. Even with the strong pattern of genetic structure identified in the study area, some evidence of long distance dispersal and/or translocation was found as a small number of individuals exhibited ancestry from a management unit outside of which they were sampled. Overall, gene flow in the study area was found to be influenced by environmental features such as topography and land use, but no distinct or obvious natural or anthropogenic geographic barriers were identified. Furthermore, strong evidence was found for non-random mating between pigs of European and Asian breeds indicating that feral pig ancestry influences their population genetic structure. Phylogenetic analysis revealed two distinct mitochondrial DNA clades, representing Asian domestic pig breeds and European breeds. A significant finding was that pigs of Asian origin living in Innisfail and south Tully were not mating randomly with European breed pigs populating the nearby Mission Beach area. Feral pig control should be implemented in each of the management units identified in this study. The control should be coordinated across properties within each management unit to prevent re-colonisation from adjacent localities. The adjacent rainforest and National Park Estates, as well as the rainforest-crop boundary should be included in a simultaneous control operation for greater success.

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Climate change is predicted to increase the frequency and severity of extreme weather events which pose significant challenges to the ability of government and other relief agencies to plan for, cope with and respond to disasters. Consequently, it is important that communities in climate sensitive and potential disaster prone areas strengthen their resilience to natural disasters in order to expeditiously recover from potential disruptions and damage caused by disasters. Building self reliance and, particularly in the immediate aftermath of a disaster, can facilitate short-term and long-term community recovery. To build stronger and more resilient communities, it is essential to have a better understanding of their current resilience capabilities by assessing areas of strength, risks and vulnerabilities so that their strengths can be enhanced and the risks and vulnerability can be appropriately addressed and mitigated through capacity building programs. While a number of conceptual frameworks currently exist to assess the resilience level of communities to disasters, they have tended to differ on their emphasis, scope and definition of what constitutes community resilience and how community resilience can be most effectively and accurately assessed. These limitations are attributed to the common approach of viewing community resilience through a mono-disciplinary lens. To overcome this, this paper proposes an integrated conceptual framework that takes into account the complex interplay of environmental, social, governance, infrastructure and economic attributes associated with community resilience. The framework can be operationalised using a range of resilience indicators to suit the nature of a disaster and the specific characteristics of a study region.

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Since the pioneering work of Hough in 1902 (1) the term ‘delayed onset muscle soreness (DOMS)’ has dominated the field of athletic recovery. DOMS typically occurs after exercise induced muscle damage (EIMD), particularly if the exercise is unaccustomed or involves a large amount of eccentric (muscle lengthening) contractions. The symptoms of EIMD manifest as a temporary reduction in muscle force, disturbed proprioceptive acuity, increases in inflammatory markers both within the injured muscle and in the blood as well as increased muscle soreness, stiffness and swelling. The intensity of discomfort and soreness associated with DOMS increases within the first 24 hours, peaks between 24 and 72 hours, before subsiding and eventually disappearing 5-7 days after the exercise. Consequently, DOMS may interfere with athletic training or competition and several recovery interventions have been utilised by athletes and coaches in an attempt to offset the negative effects...