Real-time wind speed estimation and compensation for improved flight

This paper presents the development and experimental validation of a prototype system for online estimation and compensation of wind disturbances onboard small Rotorcraft unmanned aerial systems (RUAS). The proposed approach consists of integrating a small pitot-static system onboard the vehicle and using simple but effective algorithms for estimating the wind speed in real time. The baseline flight controller has been augmented with a feed-forward term to compensate for these wind disturbances, thereby improving the flight performance of small RUAS in windy conditions. The paper also investigates the use of online airspeed measurements in a closed-loop for controlling the RUAS forward motion without the aid of a global positioning system (GPS). The results of more than 80 flights with a RUAS confirm the validity of our approach.

Energy-based motion control of a slender hull unmanned underwater vehicle

This paper presents a motion control system for tracking of attitude and speed of an underactuated slender-hull unmanned underwater vehicle. The feedback control strategy is developed using the Port-Hamiltonian theory. By shaping of the target dynamics (desired dynamic response in closed loop) with particular attention to the target mass matrix, the influence of the unactuated dynamics on the controlled system is suppressed. This results in achievable dynamics independent of stable uncontrolled states. Throughout the design, the insight of the physical phenomena involved is used to propose the desired target dynamics. Integral action is added to the system for robustness and to reject steady disturbances. This is achieved via a change of coordinates that result in input-to-state stable (ISS) target dynamics. As a final step in the design, an anti-windup scheme is implemented to account for limited actuator capacity, namely saturation. The performance of the design is demonstrated through simulation with a high-fidelity model.

Spatial and temporal distribution of tropical phytoplankton species and biomass in the Gulf of Carpenteria, Australia

The biomass and species composition of tropical phytoplankton in Albatross Bay, Gulf of Carpentaria, northern Australia, were examined monthly for 6 yr (1986 to 1992). Chlorophyll a (chl a) concentrations were highest (2 to 5.7 mu g l(-1)) in the wet season at inshore sites, usually coinciding with low salinities (30 to 33 ppt) and high temperatures (29 to 32 degrees C). At the offshore sites chi a concentrations were lower (0.2 to 2 mu g l(-1)) and did not vary seasonally. Nitrate and phosphate concentrations were generally low (0 to 3.68 mu M and 0.09 to 3 mu M for nitrate and phosphate respectively), whereas silicate was present in concentrations in the range 0.19 to 13 mu M. The phytoplankton community was dominated by diatoms, particularly at the inshore sites, as determined by a combination of microscopic and high-performance liquid chromatography (HPLC) pigment analyses. At the offshore sites the proportion of green flagellates increased. The cyanobacterium genus Trichodesmium and the diatom genera Chaetoceros, Rhizosolenia, Bacteriastrum and Thalassionema dominated the phytoplankton caught in 37 mu m mesh nets; however, in contrast to many other coastal areas studied worldwide there was no distinct species succession of the diatoms and only Trichodesmium showed seasonal changes in abundance. This reflects a stable phytoplankton community in waters without pulses of physical and chemical disturbances. These results are discussed in the context of the commercial prawn fishery in the Gulf of Carpentaria and the possible effect of phytoplankton on prawn larval growth and survival.

The acceptability, usability and short-term outcomes of Get Real: A web-based program for psychotic-like experiences (PLEs)

Background Psychotic-like experiences (PLEs) are subclinical delusional ideas and perceptual disturbances that have been associated with a range of adverse mental health outcomes. This study reports a qualitative and quantitative analysis of the acceptability, usability and short term outcomes of Get Real, a web program for PLEs in young people. Methods Participants were twelve respondents to an online survey, who reported at least one PLE in the previous 3 months, and were currently distressed. Ratings of the program were collected after participants trialled it for a month. Individual semi-structured interviews then elicited qualitative feedback, which was analyzed using Consensual Qualitative Research (CQR) methodology. PLEs and distress were reassessed at 3 months post-baseline. Results User ratings supported the program's acceptability, usability and perceived utility. Significant reductions in the number, frequency and severity of PLE-related distress were found at 3 months follow-up. The CQR analysis identified four qualitative domains: initial and current understandings of PLEs, responses to the program, and context of its use. Initial understanding involved emotional reactions, avoidance or minimization, limited coping skills and non-psychotic attributions. After using the program, participants saw PLEs as normal and common, had greater self-awareness and understanding of stress, and reported increased capacity to cope and accept experiences. Positive responses to the program focused on its normalization of PLEs, usefulness of its strategies, self-monitoring of mood, and information putting PLEs into perspective. Some respondents wanted more specific and individualized information, thought the program would be more useful for other audiences, or doubted its effectiveness. The program was mostly used in low-stress situations. Conclusions The current study provided initial support for the acceptability, utility and positive short-term outcomes of Get Real. The program now requires efficacy testing in randomized controlled trials.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

Migraine and glutamate: Modulators of glutamatergic signalling as potential treatments of neuropsychiatric disorders

Migraine is a complex neurological disorder with a well-documented genetic basis. Migraine is a product of allelic variation in genes of neurological, vascular and hormonal origin interacting with environmental triggers. Presentation can include attacks of head pain with symptoms of nausea, emesis, photophobia, phonophobia, and occasionally, visual sensory disturbances, known as aura. Migraine pain is difficult to ignore, associated with a deep sense of malaise and manifests as a throbbing, pulsatile headache, localized to one side of the head that intensifies with physical activity and that can last from 4-72 hours. Migraine is diagnosed according to criteria developed by the International Headache Society (IHS) and is subdivided into two main types based on the occurrence of aura symptoms that may be present in the early stages of the headache: migraine with aura (MA) and migraine without aura (MO). The majority (about 70%) of migraineurs are diagnosed with the MO subtype whilst the remaining 30% experience MA accompanied by neurological symptoms that manifest as fully reversible, visual, sensory and/or dysphasic speech disturbances in conjunction with their headache. Glutamate is the primary excitatory neurotransmitter in the central nervous system (CNS) and over-excitation of glutamate receptors is regarded as a contributing factor, through various mechanisms, to the pathology of migraine. In this chapter we present an overview of the pathophysiology and co-morbidity of migraine with other psychiatric disorders and discuss the role of the glutamatergic system in migraine, its molecular components as potential drug targets, in addition to the current treatments and progress of modulators of glutamatergic signaling.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.