An Exploration of the Emergence of Pattern and Form from Constraints on Growth

Growing structures are subjects of the space in which they develop. When space is limited or growth is constrained complex patterns and formations can arise. One example of this is seen in the bark patterns of trees. The rigid outer bark layer constrains the growth of the inner layers, resulting in the formation of intricate fracture patterns. An understanding of bark pattern formation has been hampered by insufficient information regarding the biomechanical properties of bark and the corresponding difficulties in faithfully modeling bark fractures using continuum mechanics. Grasstrees, however, have a discrete bark-like structure, making them particularly well suited for computational studies. In this thesis I present a model of grasstree development capturing both primary and secondary growth. A biomechanical model based on a mass-spring network represents the surface of the trunk, permitting the emergence of fractures. This model reproduces key features of grasstree bark patterns which have the same statistical character as trees found in nature. The results support the general hypothesis that the observed bark patterns found in grasstrees may be explained in terms of mechanical fractures driven by secondary growth and that bark pattern formation is primarily a biomechanical phenomenon. Furthermore, I extend the grasstree model to analyze the patterning of discrete elements on the surface of pandanus fruit. Pandanus fruit also exhibit patterns apparently related to fracturing and constraints of space. In this case, the results show that the pattern is likely a result of a higher level mechanisms as opposed to purely biomechanical

Deep learning for radar classification of drones

Proliferation in drone numbers requires the development of a surveillance system for monitoring low-altitude airspace occupancy and ensuring safety in the skies. Radar offers a method of achieving 24-hour surveillance in all weather conditions, making it an appropriate solution for this challenge. In order to be effective, a radar solution needs to be able to discriminate between birds and drones for the removal of false alarms. This thesis investigates the use of convolutional neural networks (CNNs) for drone classification. A CNN is trained for drone-bird discrimination on radar spectrograms obtained using an L-band staring radar, and performance is assessed and compared with machine learning benchmarks. The CNN is validated on an extensive data set, and the classifier’s ability to generalise against new models of drone and unseen clutter environments is investigated. Dynamic classifier selection is used to make an intelligent choice of CNN classifier based on features extracted from the tracker and the spectrogram, leading to a reduction in false positives whilst maintaining a high true positive rate at low signal to noise ratio (SNR). It is desirable to classify drones at longer ranges (therefore with lower SNR) to maximise the time available to deploy drone countermeasures. CNN classification performance is therefore investigated as a function of SNR, firstly through the addition of Gaussian noise to an experimentally obtained dataset of radar spectrograms, and secondly through data collected in increasingly demanding environments. Whilst classification accuracy falls with decreasing SNR, the augmentation of training data is shown to enhance performance at low SNR by 14%. Bayesian optimisation is used to maximise performance through the optimisation of augmentation hyperparameters, leading to an improvement in performance of more than 10%. Finally, CNNs are used to perform drone vs drone classification. Performance is established for drone group (fixed wing vs rotary wing, F1 score 0.97), drone subgroup (F1 score 0.95) and drone model (F1 score 0.67) classification using CNNs, across 9 different drone models measured at Ku-band. Multiple classification strategies are considered (single stage and multi-stage), and the relationship between dwell-time and performance is explored, forming a comprehensive investigation of CNN use for drone-to-drone classification. The feature space of drone classification is investigated, and decision tree classifiers are used to explore the relationship between processing length, coherency, and performance. Training parallel classifiers on various processing lengths and fusing the results leads to an improvement in performance of 26%

The Role of Scientific Evidence in Canada\u27s West Coast Energy Conflicts

With salience, credibility, and legitimacy as organizing themes, we investigated how opposing communities engaged with scientific information for two contentious proposed energy projects in western Canada, and how their perceptions of science influenced its use in decision-making. The Trans Mountain pipeline expansion, to carry diluted bitumen from northern Alberta’s oil sands to tankers on British Columbia’s (BC) south coast, was expected to adversely impact biodiversity and contribute to climate change. The Bute Inlet hydroelectric project, a large renewable energy project planned for BC’s Central Coast, was anticipated to impact biodiversity but was largely seen as climate-friendly. Based on surveys and interviews with 68 participants who had made one or more personal or professional decisions pertaining to the projects, we discovered that values, cultural cognition, and media effects permeated all aspects of using scientific evidence—from commissioning scientific research to selecting, assessing, and weighing it with other forms of information. As a result, science was developed and used to support positions rather than to inform decisions. We discuss ways to improve the use of science in environmental assessments and other planning and development processes where engaged communities are divided by oppositional positions. We hope this research will lead to community-university partnerships that identify broadly salient, credible, and legitimate sources of information about energy and climate issues, and foster knowledge mobilization across conflict divides

The role of pre-incident information and responder communication in effective management of casualties, including members of vulnerable groups, during a decontamination field exercise

During a CBRNe incident, it is essential that those affected are decontaminated as quickly as possible. Factors which may enhance the speed with which decontamination can be carried out include the provision of pre-incident information to members of the public, an effective responder communication strategy, and consideration of the needs of all those affected. In the current study, we ran a field exercise involving mass decontamination in response to a simulated chemical incident. The study aimed to understand the role of responder communication, the needs of vulnerable individuals, levels of compliance, and the impact of pre-incident information, during decontamination. Eighteen participants took part in the exercise with nine participants having vulnerabilities. Participants completed pre-exercise and post-exercise questionnaires and took part in a post-exercise focus group. Participants' and responders’ behaviour was also observed during the exercise. Results showed that participants reported issues associated with both practical aspects of responder communication (e.g., background noise) and overall responder communication strategy and stated that poor communication from responders would have led to less compliance in a real incident. Vulnerable individuals reported that their needs were not always met, with issues including poor physical and communication-related support, and a lack of consideration for functional aids. However, participants reported positive perceptions of the actions in the pre-incident information. Overall, this research suggests that effective management of a chemical incident must include an effective communication strategy (both before and during an incident) and consideration of the needs of vulnerable individuals

The role of pre-incident information and responder communication in effective management of casualties, including members of vulnerable groups, during a decontamination field exercise

During a CBRNe incident, it is essential that those affected are decontaminated as quickly as possible. Factors which may enhance the speed with which decontamination can be carried out include the provision of pre-incident information to members of the public, an effective responder communication strategy, and consideration of the needs of all those affected. In the current study, we ran a field exercise involving mass decontamination in response to a simulated chemical incident. The study aimed to understand the role of responder communication, the needs of vulnerable individuals, levels of compliance, and the impact of pre-incident information, during decontamination. Eighteen participants took part in the exercise with nine participants having vulnerabilities. Participants completed pre-exercise and post-exercise questionnaires and took part in a post-exercise focus group. Participants' and responders’ behaviour was also observed during the exercise. Results showed that participants reported issues associated with both practical aspects of responder communication (e.g., background noise) and overall responder communication strategy and stated that poor communication from responders would have led to less compliance in a real incident. Vulnerable individuals reported that their needs were not always met, with issues including poor physical and communication-related support, and a lack of consideration for functional aids. However, participants reported positive perceptions of the actions in the pre-incident information. Overall, this research suggests that effective management of a chemical incident must include an effective communication strategy (both before and during an incident) and consideration of the needs of vulnerable individuals

A Review of Automatic Classification of Drones Using Radar:Key Considerations, Performance Evaluation and Prospects

Automatic target classification or recognition is a critical capability in non-cooperative surveillance with radar in several defence and civilian applications. It is a well-established research field and numerous techniques exist for recognising targets, including miniature unmanned air systems or drones (i.e., small, mini, micro and nano platforms), from their radar signatures. These algorithms have notably benefited from advances in machine learning (e.g., deep neural networks) and are increasingly able to achieve remarkably high accuracies. Such classification results are often captured by standard, generic, object recognition metrics and originate from testing on simulated or real radar measurements of drones under high signal to noise ratios. Hence, it is difficult to assess and benchmark the performance of different classifiers under realistic operational conditions. In this paper, we first review the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance measures, from an end-user perspective. These are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustration. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar

Exploring gender differences in uptake of GP partnership roles : a qualitative mixed-methods study

BACKGROUND: The unadjusted gender pay gap in general practice is reported to be 33.5%. This reflects partly the differential rate at which women become partners, but evidence exploring gender differences in GPs' career progression is sparse. AIM: To explore factors affecting uptake of partnership roles, focusing particularly on gender differences. DESIGN AND SETTING: Convergent mixed-methods research design using data from UK GPs. METHOD: Secondary analysis of qualitative interviews and social media analysis of UK GPs' Twitter commentaries, which informed the conduct of asynchronous online focus groups. Findings were combined using methodological triangulation. RESULTS: The sample comprised 40 GP interviews, 232 GPs tweeting about GP partnership roles, and seven focus groups with 50 GPs. Factors at individual, organisational, and national levels influence partnership uptake and career decisions of both men and women GPs. Desire for work-family balance (particularly childcare responsibilities) presented the greatest barrier, for both men and women, as well as workload, responsibility, financial investment, and risk. Greater challenges were, however, reported by women, particularly regarding balancing work-family lives, as well as prohibitive working conditions (including maternity and sickness pay) and discriminatory practices perceived to favour men and full-time GPs. CONCLUSION: There are some long-standing gendered barriers that continue to affect the career decisions of women GPs. The relative attractiveness of salaried, locum, or private roles in general practice appears to discourage both men and women from partnerships presently. Promoting positive workplace cultures through strong role models, improved flexibility in roles, and skills training could potentially encourage greater uptake