997 resultados para Flight engineering


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This correspondence considers the problem of optimally controlling the thrust steering angle of an ion-propelled spaceship so as to effect a minimum time coplanar orbit transfer from the mean orbital distance of Earth to mean Martian and Venusian orbital distances. This problem has been modelled as a free terminal time-optimal control problem with unbounded control variable and with state variable equality constraints at the final time. The problem has been solved by the penalty function approach, using the conjugate gradient algorithm. In general, the optimal solution shows a significant departure from earlier work. In particular, the optimal control in the case of Earth-Mars orbit transfer, during the initial phase of the spaceship's flight, is found to be negative, resulting in the motion of the spaceship within the Earth's orbit for a significant fraction of the total optimized orbit transfer time. Such a feature exhibited by the optimal solution has not been reported at all by earlier investigators of this problem.

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The use of UAVs for remote sensing tasks; e.g. agriculture, search and rescue is increasing. The ability for UAVs to autonomously find a target and perform on-board decision making, such as descending to a new altitude or landing next to a target is a desired capability. Computer-vision functionality allows the Unmanned Aerial Vehicle (UAV) to follow a designated flight plan, detect an object of interest, and change its planned path. In this paper we describe a low cost and an open source system where all image processing is achieved on-board the UAV using a Raspberry Pi 2 microprocessor interfaced with a camera. The Raspberry Pi and the autopilot are physically connected through serial and communicate via MAVProxy. The Raspberry Pi continuously monitors the flight path in real time through USB camera module. The algorithm checks whether the target is captured or not. If the target is detected, the position of the object in frame is represented in Cartesian coordinates and converted into estimate GPS coordinates. In parallel, the autopilot receives the target location approximate GPS and makes a decision to guide the UAV to a new location. This system also has potential uses in the field of Precision Agriculture, plant pest detection and disease outbreaks which cause detrimental financial damage to crop yields if not detected early on. Results show the algorithm is accurate to detect 99% of object of interest and the UAV is capable of navigation and doing on-board decision making.

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There are some scenarios in which Unmmaned Aerial Vehicle (UAV) navigation becomes a challenge due to the occlusion of GPS systems signal, the presence of obstacles and constraints in the space in which a UAV operates. An additional challenge is presented when a target whose location is unknown must be found within a confined space. In this paper we present a UAV navigation and target finding mission, modelled as a Partially Observable Markov Decision Process (POMDP) using a state-of-the-art online solver in a real scenario using a low cost commercial multi rotor UAV and a modular system architecture running under the Robotic Operative System (ROS). Using POMDP has several advantages to conventional approaches as they take into account uncertainties in sensor information. We present a framework for testing the mission with simulation tests and real flight tests in which we model the system dynamics and motion and perception uncertainties. The system uses a quad-copter aircraft with an board downwards looking camera without the need of GPS systems while avoiding obstacles within a confined area. Results indicate that the system has 100% success rate in simulation and 80% rate during flight test for finding targets located at different locations.

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In the face of increasing CO2 emissions from conventional energy (gasoline), and the anticipated scarcity of Crude oil, a worldwide effort is underway for cost-effective renewable alternative energy sources. Here, we review a simple line of reasoning: (a) geologists claim that Much crude oil comes from diatoms; (b) diatoms do indeed make oil; (c) agriculturists Claim that diatoms could make 10-200 times as much oil per hectare as oil seeds; and (d) therefore, sustainable energy could be made from diatoms. In this communication, we propose ways of harvesting oil from diatoms, using biochemical engineering and also a new solar panel approach that utilizes genomically modifiable aspects of diatom biology, offering the prospect of ``milking'' diatoms for Sustainable energy by altering them to actively secrete oil products. Secretion by and milking of diatoms may provide a way around the puzzle of how to make algae that both grow quickly and have a very high oil content.

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The baker s yeast Saccharomyces cerevisiae has a long tradition in alcohol production from D-glucose of e.g. starch. However, without genetic modifications it is unable to utilise the 5-carbon sugars D-xylose and L arabinose present in plant biomass. In this study, one key metabolic step of the catabolic D-xylose pathway in recombinant D-xylose-utilising S. cerevisiae strains was studied. This step, carried out by xylulokinase (XK), was shown to be rate-limiting, because overexpression of the xylulokinase-encoding gene XKS1 increased both the specific ethanol production rate and the yield from D xylose. In addition, less of the unwanted side product xylitol was produced. Recombinant D-xylose-utilizing S. cerevisiae strains have been constructed by expressing the genes coding for the first two enzymes of the pathway, D-xylose reductase (XR) and xylitol dehydrogenase (XDH) from the D-xylose-utilising yeast Pichia stipitis. In this study, the ability of endogenous genes of S. cerevisiae to enable D-xylose utilisation was evaluated. Overexpression of the GRE3 gene coding for an unspecific aldose reductase and the ScXYL2 gene coding for a xylitol dehydrogenase homologue enabled growth on D-xylose in aerobic conditions. However, the strain with GRE3 and ScXYL2 had a lower growth rate and accumulated more xylitol compared to the strain with the corresponding enzymes from P. stipitis. Use of the strictly NADPH-dependent Gre3p instead of the P. stipitis XR able to utilise both NADH and NADPH leads to a more severe redox imbalance. In a S. cerevisiae strain not engineered for D-xylose utilisation the presence of D-xylose increased xylitol dehydrogenase activity and the expression of the genes SOR1 or SOR2 coding for sorbitol dehydrogenase. Thus, D-xylose utilisation by S. cerevisiae with activities encoded by ScXYL2 or possibly SOR1 or SOR2, and GRE3 is feasible, but requires efficient redox balance engineering. Compared to D-xylose, D-glucose is a cheap and readily available substrate and thus an attractive alternative for xylitol manufacture. In this study, the pentose phosphate pathway (PPP) of S. cerevisiae was engineered for production of xylitol from D-glucose. Xylitol was formed from D-xylulose 5-phosphate in strains lacking transketolase activity and expressing the gene coding for XDH from P. stipitis. In addition to xylitol, ribitol, D-ribose and D-ribulose were also formed. Deletion of the xylulokinase-encoding gene increased xylitol production, whereas the expression of DOG1 coding for sugar phosphate phosphatase increased ribitol, D-ribose and D-ribulose production. Strains lacking phosphoglucose isomerase (Pgi1p) activity were shown to produce 5 carbon compounds through PPP when DOG1 was overexpressed. Expression of genes encoding glyceraldehyde 3-phosphate dehydrogenase of Bacillus subtilis, GapB, or NAD-dependent glutamate dehydrogenase Gdh2p of S. cerevisiae, altered the cellular redox balance and enhanced growth of pgi1 strains on D glucose, but co-expression with DOG1 reduced growth on higher D-glucose concentrations. Strains lacking both transketolase and phosphoglucose isomerase activities tolerated only low D-glucose concentrations, but the yield of 5-carbon sugars and sugar alcohols on D-glucose was about 50% (w/w).

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Background and Aims Considerable variation has been documented with fleet safety interventions’ abilities to create lasting behavioural change, and research has neglected to consider employees’ perceptions regarding the effectiveness of fleet interventions. This is a critical oversight as employees’ beliefs and acceptance levels (as well as the perceived organisational commitment to safety) can ultimately influence levels of effectiveness, and this study aimed to examine such perceptions in Australian fleet settings. Method 679 employees sourced from four Australian organisations completed a safety climate questionnaire as well as provided perspectives about the effectiveness of 35 different safety initiatives. Results Countermeasures that were perceived as most effective were a mix of human and engineering-based approaches: - (a) purchasing safer vehicles; - (b) investigating serious vehicle incidents, and; - (c) practical driver skills training. In contrast, least effective countermeasures were considered to be: - (a) signing a promise card; - (b) advertising a company’s phone number on the back of cars for complaints and compliments, and; - (c) communicating cost benefits of road safety to employees. No significant differences in employee perceptions were identified based on age, gender, employees’ self-reported crash involvement or employees’ self-reported traffic infringement history. Perceptions of safety climate were identified to be “moderate” but were not linked to self-reported crash or traffic infringement history. However, higher levels of safety climate were positively correlated with perceived effectiveness of some interventions. Conclusion Taken together, employees believed occupational road safety risks could best be managed by the employer by implementing a combination of engineering and human resource initiatives to enhance road safety. This paper will further outline the key findings in regards to practice as well as provide direction for future research.

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Transposons, mobile genetic elements that are ubiquitous in all living organisms have been used as tools in molecular biology for decades. They have the ability to move into discrete DNA locations with no apparent homology to the target site. The utility of transposons as molecular tools is based on their ability to integrate into various DNA sequences efficiently, producing extensive mutant clone libraries that can be used in various molecular biology applications. Bacteriophage Mu is one of the most useful transposons due to its well-characterized and simple in vitro transposition reaction. This study establishes the properties of the Mu in vitro transposition system as a versatile multipurpose tool in molecular biology. In addition, this study describes Mu-based applications for engineering proteins by random insertional transposon mutagenesis in order to study structure-function relationships in proteins. We initially characterized the properties of the minimal Mu in vitro transposition system. We showed that the Mu transposition system works efficiently and accurately and produces insertions into a wide spectrum of target sites in different DNA molecules. Then, we developed a pentapeptide insertion mutagenesis strategy for inserting random five amino acid cassettes into proteins. These protein variants can be used especially for screening important sites for protein-protein interactions. Also, the system may produce temperature-sensitive variants of the protein of interest. Furthermore, we developed an efficient screening system for high-resolution mapping of protein-protein interfaces with the pentapeptide insertion mutagenesis. This was accomplished by combining the mutagenesis with subsequent yeast two-hybrid screening and PCR-based genetic footprinting. This combination allows the analysis of the whole mutant library en masse, without the need for producing or isolating separate mutant clones, and the protein-protein interfaces can be determined at amino acid accuracy. The system was validated by analysing the interacting region of JFC1 with Rab8A, and we show that the interaction is mediated via the JFC1 Slp homology domain. In addition, we developed a procedure for the production of nested sets of N- and C-terminal deletion variants of proteins with the Mu system. These variants are useful in many functional studies of proteins, especially in mapping regions involved in protein-protein interactions. This methodology was validated by analysing the region in yeast Mso1 involved in an interaction with Sec1. The results of this study show that the Mu in vitro transposition system is versatile for various applicational purposes and can efficiently be adapted to random protein engineering applications for functional studies of proteins.

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E-government provides a platform for governments to implement web enabled services that facilitate communication between citizens and the government. However, technology driven design approach and limited understanding of citizens' requirements, have led to a number of critical usability problems on the government websites. Hitherto, there has been no systematic attempt to analyse the way in which theory of User Centred Design (UCD) can contribute to address the usability issues of government websites. This research seeks to fill this gap by synthesising perspectives drawn from the study of User Centred Design and examining them based on the empirical data derived from case study of the Scottish Executive website. The research employs a qualitative approach in the collection and analysis of data. The triangulated analysis of the findings reveals that e-government web designers take commercial development approach and focus only on technical implementations which lead to websites that do not meet citizens' expectations. The research identifies that e-government practitioners can overcome web usability issues by transferring the theory of UCD to practice.

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Dispersal is a highly important life history trait. In fragmented landscapes the long-term persistence of populations depends on dispersal. Evolution of dispersal is affected by costs and benefits and these may differ between different landscapes. This results in differences in the strength and direction of natural selection on dispersal in fragmented landscapes. Dispersal has been shown to be a nonrandom process that is associated with traits such as flight ability in insects. This thesis examines genetic and physiological traits affecting dispersal in the Glanville fritillary butterfly (Melitaea cinxia). Flight metabolic rate is a repeatable trait representing flight ability. Unlike in many vertebrates, resting metabolic rate cannot be used as a surrogate of maximum metabolic rate as no strong correlation between the two was found in the Glanville fritillary. Resting and flight metabolic rate are affected by environmental variables, most notably temperature. However, only flight metabolic rate has a strong genetic component. Molecular variation in the much-studied candidate locus phosphoglucose isomerase (Pgi), which encodes the glycolytic enzyme PGI, has an effect on carbohydrate metabolism in flight. This effect is temperature dependent: in low to moderate temperatures individuals with the heterozygous genotype at the single nucleotide polymorphism (SNP) AA111 have higher flight metabolic rate than the common homozygous genotype. At high temperatures the situation is reversed. This finding suggests that variation in enzyme properties is indeed translated to organismal performance. High-resolution data on individual female Glanville fritillaries moving freely in the field were recorded using harmonic radar. There was a strong positive correlation between flight metabolic rate and dispersal rate. Flight metabolic rate explained one third of the observed variation in the one-hour movement distance. A fine-scaled analysis of mobility showed that mobility peaked at intermediate ambient temperatures but the two common Pgi genotypes differed in their reaction norms to temperature. As with flight metabolic rate, heterozygotes at SNP AA111 were the most active genotype in low to moderate temperatures. The results show that molecular variation is associated with variation in dispersal rate through the link of flight physiology under the influence of environmental conditions. The evolutionary pressures for dispersal differ between males and females. The effect of flight metabolic rate on dispersal was examined in both sexes in field and laboratory conditions. The relationship between flight metabolic rate and dispersal rate in the field and flight duration in the laboratory were found to differ between the two sexes. In females the relationship was positive, but in males the longest distances and flight durations were recorded for individuals with low flight metabolic rate. These findings may reflect male investment in mate locating. Instead of dispersing, males with high flight metabolic rate may establish territories and follow a perching strategy when locating females and hence move less on the landscape level. Males with low metabolic rate may be forced to disperse due to low competitive success or may show adaptations to an alternative strategy: patrolling. In the light of life history trade-offs and the rate of living theory having high metabolic rate may carry a cost in the form of shortened lifespan. Experiments relating flight metabolic rate to longevity showed a clear correlation in the opposite direction: high flight metabolic rate was associated with long lifespan. This suggests that individuals with high metabolic rate do not pay an extra physiological cost for their high flight capacity, rather there are positive correlations between different measures of fitness. These results highlight the importance of condition.

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Efficient and effective growth factor (GF) delivery is an ongoing challenge for tissue regeneration therapies. The accurate quantification of complex molecules such as GFs, encapsulated in polymeric delivery devices, is equally critical and just as complex as achieving efficient delivery of active GFs. In this study, GFs relevant to bone tissue formation, vascular endothelial growth factor (VEGF) and bone morphogenetic protein 7 (BMP-7), were encapsulated, using the technique of electrospraying, into poly(lactic-co-glycolic acid) microparticles that contained poly(ethylene glycol) and trehalose to assist GF bioactivity. Typical quantification procedures, such as extraction and release assays using saline buffer, generated a significant degree of GF interactions, which impaired accurate assessment by enzyme-linked immunosorbent assay (ELISA). When both dry BMP-7 and VEGF were processed with chloroform, as is the case during the electrospraying process, reduced concentrations of the GFs were detected by ELISA; however, the biological effect on myoblast cells (C2C12) or endothelial cells (HUVECs) was unaffected. When electrosprayed particles containing BMP-7 were cultured with preosteoblasts (MC3T3-E1), significant cell differentiation into osteoblasts was observed up to 3 weeks in culture, as assessed by measuring alkaline phosphatase. In conclusion, this study showed how electrosprayed microparticles ensured efficient delivery of fully active GFs relevant to bone tissue engineering. Critically, it also highlights major discrepancies in quantifying GFs in polymeric microparticle systems when comparing ELISA with cell-based assays.

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Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

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In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

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This chapter presents a brief history of the development of ophthalmic biomaterials. Particularities in the development of ophthalmic biomaterials are discussed and some of their historic priorities within the general field of biomaterials are revealed or emphasized. The chapter then discusses the role and integration of ophthalmic biomaterials in tissue engineering and regenerative medicine applications.

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There has recently been a rapidly increasing interest in solar powered UAVs. With the emergence of high power density batteries, long range and low-power micro radio devices, airframes, and powerful micro-processors and motors, small/micro UAVs have become applicable in civilian applications such as remote sensing, mapping, traffic monitoring, search and rescue. The Green Falcon UAV is an innovative project from Queensland University of Technology and has been developed and tested during these past years. It comprises a wide range of subsystems to be analyses and studied such as Solar Panel Cells, Gas sensor, Aerodynamics of the wing and others. Previous test however, resulted in damage to the solar cells and some of the subsystems including motor and ESC. This report describes the repair and verification process followed to improve the efficiency of the Green Falcon UAV. The report shows some of the results obtained in previous static and flight tests as well as some of recommendations.