The Encyclopedia of Tourism Management and Marketing is, quite simply, the definitive reference work in the field. Carefully curated by leading tourism scholar Dimitrios Buhalis, this is the largest tourism management and marketing ontology that has ever been put together and offers a holistic examination of this interdisciplinary field This is a 4-volume set. Volume 1 contains entries A–D, Volume 2 contains entries E–I, Volume 3 contains entries J–R and Volume 4 contains entries S–Z. Page numbers start from 1 in each volume.

This file contains the raw dataset used for the article 'Biochemical, sensory, and molecular evaluation of flavour and consumer acceptability in Australian papaya (<em>Carica papaya</em> L.) varieties', which is submitted to International Journal of Molecular Sciences.

Orphanage Trafficking in International Law explores the process of orphanage trafficking as a form of child trafficking in international law, examining the contexts in which it occurs and providing a comprehensive, holistic approach to addressing the issue as a form of trafficking. In doing so, this book establishes the method and process of orphanage trafficking as an issue of international concern. It reconceptualises the activity of orphanage tourism as a demand driver for child trafficking and a form of exploitation, and makes recommendations for how countries where orphanage trafficking occurs, as well as countries that contribute to orphanage trafficking via funding and volunteers, should tackle the issue.

Although artificial intelligence (AI) is solving real-world challenges and transforming industries, there are serious concerns about its ability to behave and make decisions in a responsible way. Many AI ethics principles and guidelines for responsible AI have been recently issued by governments, organisations, and enterprises. However, these AI ethics principles and guidelines are typically high-level and do not provide concrete guidance on how to design and develop responsible AI systems. To address this shortcoming, we first present an empirical study where we interviewed 21 scientists and engineers to understand the practitioners' perceptions on AI ethics principles and their implementation. We then propose a template that enables AI ethics principles to be operationalised in the form of concrete patterns and suggest a list of patterns using the newly created template. These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.

Power electronic circuits are often called switching converters because of the use of power semiconductor devices that act as switches to facilitate necessary conversions of electrical energy from one form to another. There are energy losses during this conversion, and the power semiconductor devices are a major contributor to the reduced conversion efficiency. In the case of power transistors, the resistance of the device (commonly referred as “on resistance”) operating as a switch in “on” mode is responsible for the so-called conduction losses, whereas intrinsic parasitic capacitances are responsible for the energy loss during switching between “on” and “off” modes. With the increasing need to reduce the size of switching converters for applications such as battery-operated vehicles, the share of switching losses is increasing due to the use of higher voltages and increased switching frequency. In response to the need for better understanding of the switching losses, this thesis studies the nature and impact of parasitic capacitances on the efficiency of power electronic systems. The devices-under-test are commercial devices of different types i.e. power MOSFET, SJ MOSFET, SiC MOSFETs, and GaN-HEMT. The thesis is presented in the format of a “thesis by a series of published and unpublished papers” and includes six journal articles/manuscripts as individual chapters. There are four facets of the contributions made by this thesis. The first is a new measurement method for accurate quantification of power losses during turn-on and turn-off intervals. The second is two new equations for accurate calculations of both the energy storage in and a current flow through a voltage-dependent capacitor. The third facet is in terms of providing circuit designers with a simple and transparent SPICE model for GaN˗HEMTs. Finally, a new optimization method is proposed to enable circuit designers to select the best power transistors for specific power electronic systems using two parameters that are readily available in manufacturers’ datasheets. The contributions from this thesis could help the power-electronic engineers to improve the efficiency of their next-generation systems. This is becoming urgent with the rising concern for environmental protection and energy demand worldwide.

This study provides a voice to students in the second academic year of their program of science study at one Australian university. It builds on previous work focusing on the phenomenon of ‘sophomore slump’, in conjunction with escalating attrition. This research aimed to address a gap in understanding the influential factors that characterise the lived experience of this second-year cohort. The study utilises an embedded mixed methods phenomenological approach. Two surveys comprised of Likert questions, open responses and demographic data were administered to capture student expectations and reflections, representing their lived experience. Data were analysed via the quantitative analysis of Likert scale questions and thematic content analysis of open responses. Survey data were collected from three separate second-year cohorts from the years 2015 to 2017 in four science undergraduate degree programs, across three time points within the academic calendar year. The response rate was variable with a total of 132 (18% response rate) for the expectations survey, 54 (11% response rate) for middle-of-year reflections; and 182 (24% response rate) for the end-of-year reflections from students. The key finding from the expectations survey was that respondents demonstrated an understanding that the academic curriculum would escalate both in quantity and difficulty along with how their evidence of learning would be assessed. Furthermore, they expected to spend more time on their university studies to be successful and this factor would contribute to a reduction in participating in other activities, including gaining sufficient rest. Of concern for universities was the finding that respondents anticipated receiving equivalent or higher levels of academic support for their learning journey than those offered to first-year students. Yet the support available was both reduced and varied in nature, thus indicating a disconnection. While initially most respondents believed they possessed appropriate expectations of how their second academic year would transpire, many found their expectations did not match the lived experience. This misalignment between expectations and actuality was an area that contributed to a poorer overall lived experience. The key findings from respondents’ reflections across the surveys of their second academic year was the identification of three major areas of influence that were both academic and non-academic in nature. These were: (i) The science academic curriculum: The nature of the curriculum, supporting resources, learning environments and assessment were highly influential on the lived experience of the curriculum. Interactions with academic staff and peers supported framing appropriate expectations and successful academic outcomes. (ii) The ability of students to balance priorities effectively: This area was impacted by students’ perceived value and thus desire to undertake various pursuits (academic or otherwise); and their need to undertake paid employment. The capacity of respondents to manage their time effectively to allow for these activities impacted effective balance. (iii) Overall student wellbeing: This was impacted by how connected they were to others; an established sense of purpose that could bephysiological and psychological wellbeing. Previously there has been a dearth of research pertaining to second-year undergraduates in Australia, including in the sciences discipline. The outcomes of this study resonate with those from studies in the United States of America and the United Kingdom but also have discreet points of difference, by establishing three major areas of influence for science students. When considered holistically, the aims of this study have been achieved. It has provided a voice for second-year students and has contributed to a greater understanding of the interplay of academic, social, and personal aspects and provides evidence that can be utilised to enhance the lived experience of second-year science students.

The central question this thesis addresses is ‘how does foreign aid affect human security in Kenya?’ Kenya, as a sub-Saharan African country and recipient of large-scale foreign aid, is a microcosm of what is happening in Africa and a good place to start a thorough investigation into the effectiveness of foreign aid to poor countries. More than US$1 trillion has been transferred to Africa from rich countries in development-related aid in the last fifty years, but evidence indicates that poverty levels continue to surge upwards. The key challenges to foreign aid effectiveness in Kenya as identified through the existing literature and confirmed in the results chapters are the rising complexities of Kenyan human security issues, the legacies of colonialism and neo-colonial practices as well as foreign aid’s controversies, including corruption, bureaucracy, donor fatigue and international actors’ hidden agenda. A qualitative research methodology involving traveling to Kenya to conduct face-to-face, semi-structured interviews with local Kenyans and residents who have direct knowledge about the foreign aid scheme’s processes and practices was the most appropriate method to investigate individuals’ lived experiences. The locals’ original accounts have been triangulated with the material from primary and secondary sources to inform this study. Apart from providing a geo-political analysis of the long-term effects of colonialism in a key African country, this investigation of the foreign aid schemes intends to contribute further knowledge and firsthand information necessary to reshape and improve the processes and practices, for the benefit of both the donors and the intended beneficiaries.

Understanding customer experience in real-time can potentially support people’s safety and comfort while in public spaces. Existing techniques, such as surveys and interviews, can only analyse data at specific times. Therefore, organisations that manage public spaces, such as local government or business entities, cannot respond immediately when urgent actions are needed. Manual monitoring through surveillance cameras can enable organisation personnel to observe people. However, fatigue and human distraction during constant observation cannot ensure reliable and timely analysis. Artificial intelligence (AI) can automate people observation and analyse their movement and any related properties in real-time. Analysing people’s facial expressions can provide insight into how comfortable they are in a certain area, while analysing crowd density can inform us of the area’s safety level. By observing the long-term patterns of crowd density, movement, and spatial data, the organisation can also gain insight to develop better strategies for improving people’s safety and comfort. There are three challenges to making an AI-enabled video surveillance system work well in public spaces. First is the readiness of AI models to be deployed in public space settings. Existing AI models are designed to work in generic/particular settings and will suffer performance degradation when deployed in a real-world setting. Therefore, the models require further development to tailor them for the specific environment of the targeted deployment setting. Second is the inclusion of AI continual learning capability to adapt the models to the environment. AI continual learning aims to learn from new data collected from cameras to adapt the models to constant visual changes introduced in the setting. Existing continuous learning approaches require long-term data retention and past data, which then raise data privacy issues. Third, most of the existing AI-enabled surveillance systems rely on centralised processing, meaning data are transmitted to a central/cloud machine for video analysis purposes. Such an approach involves data privacy and security risks. Serious data threats, such as data theft, eavesdropping or cyberattack, can potentially occur during data transmission. This study aims to develop an AI-enabled intelligent video surveillance system based on deep learning techniques for public spaces established on responsible AI principles. This study formulates three responsible AI criteria, which become the guidelines to design, develop, and evaluate the system. Based on the criteria, a framework is constructed to scale up the system over time to be readily deployed in a specific real-world environment while respecting people’s privacy. The framework incorporates three AI learning approaches to iteratively refine the AI models within the ethical use of data. First is the AI knowledge transfer approach to adapt existing AI models from generic deployment to specific real-world deployment with limited surveillance datasets. Second is the AI continuous learning approach to continuously adapt AI models to visual changes introduced by the environment without long-period data retention and the need for past data. Third is the AI federated learning approach to limit sensitive and identifiable data transmission by performing computation locally on edge devices rather than transmitting to the central machine. This thesis contributes to the study of responsible AI specifically in the video surveillance context from both technical and non-technical perspectives. It uses three use cases at an international airport as the application context to understand passenger experience in real-time to ensure people’s safety and comfort. A new video surveillance system is developed based on the framework to provide automated people observation in the application context. Based on real deployment using the airport’s selected cameras, the evaluation demonstrates that the system can provide real-time automated video analysis for three use cases while respecting people’s privacy. Based on comprehensive experiments, AI knowledge transfer can be an effective way to address limited surveillance datasets issue by transferring knowledge from similar datasets rather than training from scratch on surveillance datasets. It can be further improved by incrementally transferring knowledge from multi-datasets with smaller gaps rather than a one-stage process. Learning without Forgetting is a viable approach for AI continuous learning in the video surveillance context. It consistently outperforms fine-tuning and joint-training approaches with lower data retention and without the need for past data. AI federated learning can be a feasible solution to allow continuous learning in the video surveillance context without compromising model accuracy. It can obtain comparable accuracy with quicker training time compared to joint-training.

Background. Resilience influences how a person perceives and responds to pain. Both positive and negative psychosocial factors impact resilience. This is the third study in a series of Delphi studies investigating self-administered questionnaires to assess psychosocial factors in people with pain. Positive psychosocial factors (e.g., optimism, pain acceptance and social support) are considered resilience resources or mechanisms. As these factors also influence recovery from pain, management strategies to optimise these factors are being employed. Numerous self-administered questionnaires have been designed and are being used to assess these factors. This plethora of questionnaires is problematic, because in research and clinical practice different questionnaires are used to assess the same construct. This makes comparison and pooling of data difficult, especially because questionnaires that are believed to measure the same construct, often are biased toward certain aspects of the construct or also include other constructs, making the different questionnaires for a factor not interchangeable. Therefore, it is important that clinicians and researchers use uniform and the most appropriate questionnaires to assess resilience, optimism, pain acceptance and social support, to enable them to track changes during therapy and allow for pooling of data. Aims. This study aimed to identify and reach consensus on the most appropriate questionnaires to assess resilience, optimism, pain acceptance and social support in people with pain. Methods. A three-round Delphi study was conducted to achieve the aims. An international expert panel was formed consisting of 40 experts. In Round 1, experts were asked to list questionnaires that they believed were suitable to assess resilience, optimism, pain acceptance and social support in people with pain. Following Round 1, we conducted a literature review to summarise the clinimetric properties of all suggested questionnaires. In Round 2, experts received PDF-versions of all questionnaires and an overview of the clinimetric properties (internal consistency, test-retest reliability, responsiveness and construct validity) for each questionnaire. In this round, experts were asked to indicate whether they considered the questionnaires appropriate to assess that factor (Yes/No/Don’t know). At least 60% of experts had to rate the questionnaire as appropriate for the questionnaire to be retained for Round 3. In Round 3, experts rated the suitability of the questionnaires on an 11-point Likert scale (0: ‘not at all suitable’; 10: ‘completely suitable’). Consensus was considered reached if at least 75% of experts considered the questionnaire suitable (score of ≥7 on the Likert scale) and if at least 50% of experts had rated that questionnaire. Results. In Round 1, 33 questionnaires were suggested for resilience, 6 for optimism, 14 for pain acceptance and 24 for social support. Because some questionnaires were suggested for different factors, a total of 57 distinct questionnaires were suggested. Following Round 2, the number of questionnaires considered appropriate could be reduced to 8 for resilience, 2 for optimism, 6 for pain acceptance and 12 for social support. In Round 3, consensus was reached for 5 questionnaires: the Pain Resilience Scale for resilience, the Life Orientation Test (revised version) for optimism, the 8-item and revised versions of the Chronic Pain Acceptance Questionnaire for pain acceptance and the Emotional Support Item Bank of the PROMIS tool for social support. Conclusions. Via a Delphi process we have been able to make clear recommendations for the use of self-reported questionnaires to assess resilience, optimism, pain acceptance and social support in people with pain. These evidence-based recommendations will hopefully facilitate more consistent use of questionnaires to assess these factors and ultimately lead to easier comparison and pooling of clinical and research data. Limitations. A limitation of the study is that questionnaires potentially suitable to assess a factor may have been excluded from the study due to an English version not being available. Another limitation is that many experts were involved in the development, translation and/or validation of questionnaires that were included in the Delphi study which might have biased their scores. Therefore, a sensitivity analysis was performed which omitted the scores of these experts. For all questionnaires for which consensus was reached in the main analysis, consensus was also reached in the sensitivity analysis, suggesting that potential selection bias did not influence the results.