Effectiveness of on-line resources to enhance student learning and engagement

BACKGROUND For engineering graduates to be work-ready with marketable skills they must not only be well-versed with engineering science and its applications, but also able to adapt to using commercial software that is widely used in engineering practice. Hydrological/hydraulic modelling is one aspect of engineering practice which demands the ability to apply fundamentals into design and construction using software. The user manuals for such software are usually tailored for the experienced engineer but not for undergraduates who typically are novices to concepts of modelling and software tools. As the focus of a course such as Advanced Water Engineering is on the wider aspects of engineering application of hydrological and hydraulic concepts, it is ineffective for the lecturers to direct the students to user manuals as students have neither the time nor the desire to sift through numerous pages in a manual. An alternative and efficient way to demonstrate the use of the software is enabling students to develop a model to simulate real-world scenario using the tools of the software and directing them to make informed decisions based on outcomes. PURPOSE Past experience of the lecturer showed that the resources available for the students left a knowledge gap leading to numerous student queries outside contact hours. The purpose of this study is to assess how effective purpose-built video resources can be in supplementing the traditional learning resources to enhance student learning. APPROACH Short-length animated video clips comprising guided step-by-step instructions were prepared using screen capture software to capture screen activity and later edited to focus on specific features using pop-up annotations; Vocal narration was purposely excluded to avoid disturbances due to noise and allow different learning paces of individual students. The video clips were made available to the students alongside the traditional resources/approaches such as in-class demonstrations, guideline notes, and tips for efficient and error-free procedural descriptions. The number of queries the lecturer received from the student cohort outside the lecture times was recorded. An anonymous survey to assess the usefulness and adequacy of the courseware was conducted. OUTCOMES While a significant decline in the number of student queries was noted, an overwhelming majority of the survey respondents confirmed the usefulness of the purpose-developed courseware. CONCLUSIONS/RECOMMENDATIONS/SUMMARY The survey and lecturer’s experience indicated that animated demonstration video clips illustrating the various steps involved in developing hydrologic and hydraulic models and simulating design scenarios is an effective supplement for traditional learning resources. Among the many advantages of the custom-made video clips as a learning resource are that they (1) highlight the aspects that are important to undergraduate learning but not available in the software manuals as the latter are designed for more mature users/learners; (2) provide short, to-the point communication in a step-by-step manner; (3) allow students flexibility to self-learn at their own pace; (4) enhance student learning; and (5) enable time savings for the lecturer in the long term by avoiding queries of a repetitive nature. It is expected that these newly developed resources will be improved to incorporate students’ suggestions before being offered to future cohorts of students. The concept can also be expanded to other relevant courses where animated demonstrations of key modelling steps are beneficial to student learning.

Calibration, estimation, and sampling issues of car-following parameters

Drivers behave in different ways, and these different behaviors are a cause of traffic disturbances. A key objective for simulation tools is to correctly reproduce this variability, in particular for car-following models. From data collection to the sampling of realistic behaviors, a chain of key issues must be addressed. This paper discusses data filtering, robustness of calibration, correlation between parameters, and sampling techniques of acceleration-time continuous car-following models. The robustness of calibration is systematically investigated with an objective function that allows confidence regions around the minimum to be obtained. Then, the correlation between sets of calibrated parameters and the validity of the joint distributions sampling techniques are discussed. This paper confirms the need for adapted calibration and sampling techniques to obtain realistic sets of car-following parameters, which can be used later for simulation purposes.

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.

A genome-wide association study of sleep habits and insomnia

Several aspects of sleep behavior such as timing, duration and quality have been demonstrated to be heritable. To identify common variants that influence sleep traits in the population, we conducted a genome-wide association study of six sleep phenotypes assessed by questionnaire in a sample of 2,323 individuals from the Australian Twin Registry. Genotyping was performed on the Illumina 317, 370, and 610K arrays and the SNPs in common between platforms were used to impute non-genotyped SNPs. We tested for association with more than 2,000,000 common polymorphisms across the genome. While no SNPs reached the genome-wide significance threshold, we identified a number of associations in plausible candidate genes. Most notably, a group of SNPs in the third intron of the CACNA1C gene ranked as most significant in the analysis of sleep latency (P = 1.3 x 10(-)(6)). We attempted to replicate this association in an independent sample from the Chronogen Consortium (n = 2,034), but found no evidence of association (P = 0.73). We have identified several other suggestive associations that await replication in an independent sample. We did not replicate the results from previous genome-wide analyses of self-reported sleep phenotypes after correction for multiple testing.

A genome-wide linkage scan provides evidence for both new and previously reported loci influencing common migraine

Latent class analysis was performed on migraine symptom data collected in a Dutch population sample (N = 12,210, 59% female) in order to obtain empirical groupings of individuals suffering from symptoms of migraine headache. Based on these heritable groupings (h(2) = 0.49, 95% CI: 0.41-0.57) individuals were classified as affected (migrainous headache) or unaffected. Genome-wide linkage analysis was performed using genotype data from 105 families with at least 2 affected siblings. In addition to this primary phenotype, linkage analyses were performed for the individual migraine symptoms. Significance levels, corrected for the analysis of multiple traits, were determined empirically via a novel simulation approach. Suggestive linkage for migrainous headache was found on chromosomes 1 (LOD = 1.63; pointwise P = 0.0031), 13 (LOD = 1.63; P = 0.0031), and 20 (LOD = 1.85; P = 0.0018). Interestingly, the chromosome 1 peak was located close to the ATP1A2 gene, associated with familial hemiplegic migraine type 2 (FHM2). Individual symptom analysis produced a LOD score of 1.97 (P = 0.0013) on chromosome 5 (photo/phonophobia), a LOD score of 2.13 (P = 0.0009) on chromosome 10 (moderate/severe pain intensity) and a near significant LOD score of 3.31 (P = 0.00005) on chromosome 13 (pulsating headache). These peaks were all located near regions previously reported in migraine linkage studies. Our results provide important replication and support for the presence of migraine susceptibility genes within these regions, and further support the utility of an LCA-based phenotyping approach and analysis of individual symptoms in migraine genetic research. Additionally, our novel "2-step" analysis and simulation approach provides a powerful means to investigate linkage to individual trait components.

A reanalysis of 409 European-Ancestry and African American schizophrenia pedigrees reveals significant linkage to 8p23.3 with evidence of locus heterogeneity

The detection and replication of schizophrenia risk loci can require substantial sample sizes, which has prompted various collaborative efforts for combining multiple samples. However, pooled samples may comprise sub-samples with substantial population genetic differences, including allele frequency differences. We investigated the impact of population differences via linkage reanalysis of Molecular Genetics of Schizophrenia 1 (MGS1) affected sibling-pair data, comprising two samples of distinct ancestral origin: European (EA: 263 pedigrees) and African-American (AA: 146 pedigrees). To exploit the linkage information contained within these distinct continental samples, we performed separate analyses of the individual samples, allowing for within-sample locus heterogeneity, and the pooled sample, allowing for both within-sample and between-sample heterogeneity. Significance levels, corrected for the multiple tests, were determined empirically. For all suggestive peaks, stronger linkage evidence was obtained in either the EA or AA sample than the combined sample, regardless of how heterogeneity was modeled for the latter. Notably, we report genomewide significant linkage of schizophrenia to 8p23.3 and evidence for a second, independent susceptibility locus, reaching suggestive linkage, 29 cM away on 8p21.3. We also detected suggestive linkage on chromosomes 5p13.3 and 7q36.2. Many regions showed pronounced differences in the extent of linkage between the EA and AA samples. This reanalysis highlights the potential impact of population differences upon linkage evidence in pooled data and demonstrates a useful approach for the analysis of samples drawn from distinct continental groups.

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.