A DNA toolbox for non-invasive genetic studies of sambar deer (Rusa unicolor)
- Davies, Chris, Wright, Wendy, Wedrowicz, Faye, Hogan, Fiona
- Authors: Davies, Chris , Wright, Wendy , Wedrowicz, Faye , Hogan, Fiona
- Date: 2020
- Type: Text , Journal article
- Relation: Australian Mammalogy Vol. 42, no. 1 (2020), p. 58-66
- Full Text:
- Reviewed:
- Description: Invasive sambar deer (Rusa unicolor) are having significant detrimental impacts on natural environments in south-eastern Australia. Little, however, is known about their ecology, limiting evidence-based management strategies directed at reducing deer impacts. Genetic data, generated from DNA isolated from deer scats, can be used to fill ecological knowledge gaps. This study outlines a non-invasive genetic sampling strategy by which good-quality DNA from a single deer scat can be used to determine (1) species of origin, (2) sex and (3) a unique DNA profile. DNA from deer tissue and sambar deer scat samples were used to develop and optimise molecular methods to collect reliable genetic information. A DNA toolbox is presented that describes how to find, collect and store scat samples, isolate DNA and use molecular markers to generate informative genetic data. Generating genetic data using this approach will support studies aimed at acquiring ecological knowledge about sambar deer. Such knowledge will be critical for developing evidence-based recommendations to improve on-ground management decisions for sambar deer.
- Authors: Davies, Chris , Wright, Wendy , Wedrowicz, Faye , Hogan, Fiona
- Date: 2020
- Type: Text , Journal article
- Relation: Australian Mammalogy Vol. 42, no. 1 (2020), p. 58-66
- Full Text:
- Reviewed:
- Description: Invasive sambar deer (Rusa unicolor) are having significant detrimental impacts on natural environments in south-eastern Australia. Little, however, is known about their ecology, limiting evidence-based management strategies directed at reducing deer impacts. Genetic data, generated from DNA isolated from deer scats, can be used to fill ecological knowledge gaps. This study outlines a non-invasive genetic sampling strategy by which good-quality DNA from a single deer scat can be used to determine (1) species of origin, (2) sex and (3) a unique DNA profile. DNA from deer tissue and sambar deer scat samples were used to develop and optimise molecular methods to collect reliable genetic information. A DNA toolbox is presented that describes how to find, collect and store scat samples, isolate DNA and use molecular markers to generate informative genetic data. Generating genetic data using this approach will support studies aimed at acquiring ecological knowledge about sambar deer. Such knowledge will be critical for developing evidence-based recommendations to improve on-ground management decisions for sambar deer.
A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things
- Rashid, Md Mamunur, Kamruzzaman, Joarder, Hassan, Mohammad, Shahriar Shafin, Sakib, Bhuiyan, Md Zakirul
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
- Full Text:
- Reviewed:
- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
- Full Text:
- Reviewed:
- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
An application of high-dimensional statistics to predictive modeling of grade variability
- Hinz, Juri, Grigoryev, Igor, Novikov, Alexander
- Authors: Hinz, Juri , Grigoryev, Igor , Novikov, Alexander
- Date: 2020
- Type: Text , Journal article , Article
- Relation: Geosciences (Switzerland) Vol. 10, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Hinz, Juri , Grigoryev, Igor , Novikov, Alexander
- Date: 2020
- Type: Text , Journal article , Article
- Relation: Geosciences (Switzerland) Vol. 10, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
An application of high-dimensional statistics to predictive modeling of grade variability
- Hinz, Juri, Grigoryev, Igor, Novikov, Alexander
- Authors: Hinz, Juri , Grigoryev, Igor , Novikov, Alexander
- Date: 2020
- Type: Text , Journal article
- Relation: Geosciences (Switzerland) Vol. 10, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Hinz, Juri , Grigoryev, Igor , Novikov, Alexander
- Date: 2020
- Type: Text , Journal article
- Relation: Geosciences (Switzerland) Vol. 10, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
An enhancement to the spatial pyramid matching for image classification and retrieval
- Karmakar, Priyabrata, Teng, Shyh, Lu, Guojun, Zhang, Dengsheng
- Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 22463-22472
- Full Text:
- Reviewed:
- Description: Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval. © 2013 IEEE.
- Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 22463-22472
- Full Text:
- Reviewed:
- Description: Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval. © 2013 IEEE.
Anxiety and depression after a cardiac event : prevalence and predictors
- Murphy, Barbara, Le Grande, Michael, Alvarenga, Marlies, Worcester, Marian, Jackson, Alun
- Authors: Murphy, Barbara , Le Grande, Michael , Alvarenga, Marlies , Worcester, Marian , Jackson, Alun
- Date: 2020
- Type: Text , Journal article , Article
- Relation: Frontiers in Psychology Vol. 10, no. (2020), p.
- Full Text:
- Reviewed:
- Description: Introduction: Patients who are anxious or depressed after an acute cardiac event are at increased risk of a subsequent event and premature death. It is therefore important to identify these patients early in order to initiate supportive or even preventive measures. In the present study, we report on the prevalence of anxiety and depression during the first 12 months after an acute cardiac event, and the patient characteristics predictive of increased anxiety and depression risk in early and late convalescence. Methods: We recruited a sample of 911 patients with acute myocardial infarction (AMI), acute coronary syndrome (ACS), and/or unstable angina (UA), and/or undergoing coronary artery bypass graft surgery (CABGS). Patients completed the Hospital Anxiety and Depression Scale (HADS) close to the time of their event, and again during early (2–4 months post-event) and late (6–12 months post-event) convalescence. Using HADS-A and HADS-D cut-offs of 8+, prevalence rates for anxiety, depression, and comorbid anxiety and depression were determined for each timepoint. Chi-square tests and odds ratios were used to identify baseline patient characteristics associated with increased anxiety and depression risk over 12 months. Results: Anxiety rates were 43, 28, and 27% at the time of the event, early, and late convalescence. Depression rates were 22, 17, and 15%, respectively. Factors consistently associated with increased anxiety and depression risk were history of depression, financial strain, poor self-rated health, low socioeconomic status, younger age (<55 years), and smoking. Obesity, diabetes, and social isolation (living alone or being unpartnered) were identified as important albeit less significant risk factors. Neither sex nor event type were predictive of anxiety or depression. Conclusion: This large patient sample provided the opportunity to identify rates of anxiety and depression during the 12 months after a cardiac event and key patient characteristics for increased risk. These risk factors are easily identifiable at the time of the event, and could be used to guide the targeting of support programs for patients at risk. © Copyright © 2020 Murphy, Le Grande, Alvarenga, Worcester and Jackson.
- Authors: Murphy, Barbara , Le Grande, Michael , Alvarenga, Marlies , Worcester, Marian , Jackson, Alun
- Date: 2020
- Type: Text , Journal article , Article
- Relation: Frontiers in Psychology Vol. 10, no. (2020), p.
- Full Text:
- Reviewed:
- Description: Introduction: Patients who are anxious or depressed after an acute cardiac event are at increased risk of a subsequent event and premature death. It is therefore important to identify these patients early in order to initiate supportive or even preventive measures. In the present study, we report on the prevalence of anxiety and depression during the first 12 months after an acute cardiac event, and the patient characteristics predictive of increased anxiety and depression risk in early and late convalescence. Methods: We recruited a sample of 911 patients with acute myocardial infarction (AMI), acute coronary syndrome (ACS), and/or unstable angina (UA), and/or undergoing coronary artery bypass graft surgery (CABGS). Patients completed the Hospital Anxiety and Depression Scale (HADS) close to the time of their event, and again during early (2–4 months post-event) and late (6–12 months post-event) convalescence. Using HADS-A and HADS-D cut-offs of 8+, prevalence rates for anxiety, depression, and comorbid anxiety and depression were determined for each timepoint. Chi-square tests and odds ratios were used to identify baseline patient characteristics associated with increased anxiety and depression risk over 12 months. Results: Anxiety rates were 43, 28, and 27% at the time of the event, early, and late convalescence. Depression rates were 22, 17, and 15%, respectively. Factors consistently associated with increased anxiety and depression risk were history of depression, financial strain, poor self-rated health, low socioeconomic status, younger age (<55 years), and smoking. Obesity, diabetes, and social isolation (living alone or being unpartnered) were identified as important albeit less significant risk factors. Neither sex nor event type were predictive of anxiety or depression. Conclusion: This large patient sample provided the opportunity to identify rates of anxiety and depression during the 12 months after a cardiac event and key patient characteristics for increased risk. These risk factors are easily identifiable at the time of the event, and could be used to guide the targeting of support programs for patients at risk. © Copyright © 2020 Murphy, Le Grande, Alvarenga, Worcester and Jackson.
Application of thermal fragmentation in Australian hard rock underground narrow-vein mining
- Drake, Bradley, Koroznikova, Larissa, Tuck, Michael, Durkin, Steve
- Authors: Drake, Bradley , Koroznikova, Larissa , Tuck, Michael , Durkin, Steve
- Date: 2020
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 37, no. 1 (2020), p. 219-229
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- Reviewed:
- Description: This paper presents the results from the investigation of the application of thermal fragmentation in Australian hard rock underground narrow-vein mining. Two geologically similar samples from an underground narrow-vein hard rock gold mine were collected to obtain a measure of the technology’s ability to recover ore by the creation of large thermal openings to assess the applicability of the thermal method. Particle size distribution showed a higher generation of fine product, − 2 mm, by thermal fragmentation compared with selective blasting by 31%. The Bond work index for thermal ore (12.62 kWh/t) is half to that of the blasted ore value (25.32 kWh/t). The average grindability obtained for the thermal ore sample was greater than the blasted sample by a factor of 2.44, a higher value indicating a decrease in the energy required to grind. The thermal fragmentation method generates product with higher dissolution of gold in cyanide, by 14% for the − 9.5 + 2 mm size fraction samples. Additionally, the thermal fragmentation results in higher production of − 9.5 + 2 mm material by 15 % compared with selective blasting. © 2019, Society for Mining, Metallurgy & Exploration Inc.
- Authors: Drake, Bradley , Koroznikova, Larissa , Tuck, Michael , Durkin, Steve
- Date: 2020
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 37, no. 1 (2020), p. 219-229
- Full Text:
- Reviewed:
- Description: This paper presents the results from the investigation of the application of thermal fragmentation in Australian hard rock underground narrow-vein mining. Two geologically similar samples from an underground narrow-vein hard rock gold mine were collected to obtain a measure of the technology’s ability to recover ore by the creation of large thermal openings to assess the applicability of the thermal method. Particle size distribution showed a higher generation of fine product, − 2 mm, by thermal fragmentation compared with selective blasting by 31%. The Bond work index for thermal ore (12.62 kWh/t) is half to that of the blasted ore value (25.32 kWh/t). The average grindability obtained for the thermal ore sample was greater than the blasted sample by a factor of 2.44, a higher value indicating a decrease in the energy required to grind. The thermal fragmentation method generates product with higher dissolution of gold in cyanide, by 14% for the − 9.5 + 2 mm size fraction samples. Additionally, the thermal fragmentation results in higher production of − 9.5 + 2 mm material by 15 % compared with selective blasting. © 2019, Society for Mining, Metallurgy & Exploration Inc.
Audit education in a socialist oriented market economy – the case of Vietnam
- Dang, Ky
- Authors: Dang, Ky
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: The objective of the research project is to contribute to the understanding of the auditing profession in Vietnam. In particular, it provides information on the challenges facing auditors in an emerging economy where economic transformation is in progress and where auditing, as a profession, is under development. Auditors operate in an environment of conflicting priorities where they must maintain independence and objectivity in discharging their responsibilities to stakeholders and society. In Vietnam, the audit profession only came into existence in 1986 when the country embarked on its new economic model. Whilst studies have been conducted on the status of current accounting practice in Vietnam, studies regarding the audit profession have been limited. In this research project an examination of the issues affecting audit quality in Vietnam are investigated and suggestions for changes to address the deficiencies are made. In particular, the project focuses on the relevance and appropriateness of the education of auditors. A national survey of accountants, auditors and accounting academics in Vietnam was undertaken. The survey results indicate that in Vietnam there are deficiencies in audit practices over and above those commonly observed in other countries. These deficiencies are the result of the unique history of Vietnam, the current stage of economic development and the education system for auditors. From an auditing perspective, the slow adaptation of the education system to the new economic environment is having negative effects on accounting graduates and their employment prospects. Although inadequate training was identified as the single most important factor affecting audit quality, the ineffective enforcement regime was also a contributing factor. This research project indicates that there is a need for an overhaul of the current education system in Vietnam and for universities to develop an accounting and auditing curriculum that meets the needs of employers while complying with government’s education objectives and international standards of auditing and accounting.
- Description: Doctor of Philosophy
- Authors: Dang, Ky
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: The objective of the research project is to contribute to the understanding of the auditing profession in Vietnam. In particular, it provides information on the challenges facing auditors in an emerging economy where economic transformation is in progress and where auditing, as a profession, is under development. Auditors operate in an environment of conflicting priorities where they must maintain independence and objectivity in discharging their responsibilities to stakeholders and society. In Vietnam, the audit profession only came into existence in 1986 when the country embarked on its new economic model. Whilst studies have been conducted on the status of current accounting practice in Vietnam, studies regarding the audit profession have been limited. In this research project an examination of the issues affecting audit quality in Vietnam are investigated and suggestions for changes to address the deficiencies are made. In particular, the project focuses on the relevance and appropriateness of the education of auditors. A national survey of accountants, auditors and accounting academics in Vietnam was undertaken. The survey results indicate that in Vietnam there are deficiencies in audit practices over and above those commonly observed in other countries. These deficiencies are the result of the unique history of Vietnam, the current stage of economic development and the education system for auditors. From an auditing perspective, the slow adaptation of the education system to the new economic environment is having negative effects on accounting graduates and their employment prospects. Although inadequate training was identified as the single most important factor affecting audit quality, the ineffective enforcement regime was also a contributing factor. This research project indicates that there is a need for an overhaul of the current education system in Vietnam and for universities to develop an accounting and auditing curriculum that meets the needs of employers while complying with government’s education objectives and international standards of auditing and accounting.
- Description: Doctor of Philosophy
Comparative effectiveness of three exercise types to treat clinical depression in older adults : a systematic review and network meta-analysis of randomised controlled trials
- Miller, Kyle, Gonçalves-Bradley, Daniela, Areerob, Pinyadapat, Hennessy, Declan, Mesagno, Christopher, Grace, Fergal
- Authors: Miller, Kyle , Gonçalves-Bradley, Daniela , Areerob, Pinyadapat , Hennessy, Declan , Mesagno, Christopher , Grace, Fergal
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Ageing Research Reviews Vol. 58 (2020).
- Full Text:
- Reviewed:
- Description: Background: Few studies have directly compared the effects of different exercise therapies on clinical depression in older adults. Thus, we conducted a systematic review and network meta-analysis of current evidence from randomised controlled trials (RCTs) to compare the effectiveness of three major exercise types (aerobic, resistance, and mind-body exercise) in clinically depressed older adults. Methods: We followed PRISMA-NMA guidelines and searched databases for eligible RCTs (inception – September 12th, 2019). RCTs were eligible if they included clinically depressed adults aged >65 years, implemented one or more exercise therapy arms using aerobic, resistance, or mind-body exercise, and assessed depressive symptoms at baseline and follow-up using a validated clinical questionnaire. Results: A network meta-analysis was performed on 15 eligible RCTs comprising 596 participants (321 treatment and 275 controls), including aerobic (n = 6), resistance (n = 5), and mind-body (n = 4) exercise trials. Compared with controls, mind-body exercise showed the largest improvement on depressive symptoms (g = −0.87 to −1.38), followed by aerobic exercise (g = −0.51 to −1.02), and resistance exercise (g = −0.41 to −0.92). Notably, there were no statistically significant differences between exercise types: aerobic versus resistance (g = −0.10, PrI = −2.23, 2.03), mind-body versus aerobic (g = −0.36, PrI = −2.69, 1.97), or mind-body versus resistance (g = −0.46, PrI = −2.75, 1.83). Conclusions: These findings should guide optimal exercise prescription for allied health professionals and stakeholders in clinical geriatrics. Notably, clinically depressed older adults may be encouraged to self-select their preferred exercise type in order to achieve therapeutic benefit on symptoms of depression. In coalition with high levels of compliance, these data provide encouraging evidence for the antidepressant effect of either aerobic, resistance, or mind-body exercise as effective treatment adjucts for older adults presenting with clinical depression. © 2019
- Authors: Miller, Kyle , Gonçalves-Bradley, Daniela , Areerob, Pinyadapat , Hennessy, Declan , Mesagno, Christopher , Grace, Fergal
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Ageing Research Reviews Vol. 58 (2020).
- Full Text:
- Reviewed:
- Description: Background: Few studies have directly compared the effects of different exercise therapies on clinical depression in older adults. Thus, we conducted a systematic review and network meta-analysis of current evidence from randomised controlled trials (RCTs) to compare the effectiveness of three major exercise types (aerobic, resistance, and mind-body exercise) in clinically depressed older adults. Methods: We followed PRISMA-NMA guidelines and searched databases for eligible RCTs (inception – September 12th, 2019). RCTs were eligible if they included clinically depressed adults aged >65 years, implemented one or more exercise therapy arms using aerobic, resistance, or mind-body exercise, and assessed depressive symptoms at baseline and follow-up using a validated clinical questionnaire. Results: A network meta-analysis was performed on 15 eligible RCTs comprising 596 participants (321 treatment and 275 controls), including aerobic (n = 6), resistance (n = 5), and mind-body (n = 4) exercise trials. Compared with controls, mind-body exercise showed the largest improvement on depressive symptoms (g = −0.87 to −1.38), followed by aerobic exercise (g = −0.51 to −1.02), and resistance exercise (g = −0.41 to −0.92). Notably, there were no statistically significant differences between exercise types: aerobic versus resistance (g = −0.10, PrI = −2.23, 2.03), mind-body versus aerobic (g = −0.36, PrI = −2.69, 1.97), or mind-body versus resistance (g = −0.46, PrI = −2.75, 1.83). Conclusions: These findings should guide optimal exercise prescription for allied health professionals and stakeholders in clinical geriatrics. Notably, clinically depressed older adults may be encouraged to self-select their preferred exercise type in order to achieve therapeutic benefit on symptoms of depression. In coalition with high levels of compliance, these data provide encouraging evidence for the antidepressant effect of either aerobic, resistance, or mind-body exercise as effective treatment adjucts for older adults presenting with clinical depression. © 2019
Consistency of control performance in 3d overhead cranes under payload mass uncertainty
- Hoang, Uyen, Le, Hai, Thai, Nguyen, Pham, Hung, Nguyen, Linh
- Authors: Hoang, Uyen , Le, Hai , Thai, Nguyen , Pham, Hung , Nguyen, Linh
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 9, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Hoang, Uyen , Le, Hai , Thai, Nguyen , Pham, Hung , Nguyen, Linh
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 9, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Depth sequence coding with hierarchical partitioning and spatial-domain quantization
- Shahriyar, Shampa, Murshed, Manzur, Ali, Mortuza, Paul, Manoranjan
- Authors: Shahriyar, Shampa , Murshed, Manzur , Ali, Mortuza , Paul, Manoranjan
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Circuits and Systems for Video Technology Vol. 30, no. 3 (2020), p. 835-849
- Full Text:
- Reviewed:
- Description: Depth coding in 3D-HEVC deforms object shapes due to block-level edge-approximation and lacks efficient techniques to exploit the statistical redundancy, due to the frame-level clustering tendency in depth data, for higher coding gain at near-lossless quality. This paper presents a standalone mono-view depth sequence coder, which preserves edges implicitly by limiting quantization to the spatial-domain and exploits the frame-level clustering tendency efficiently with a novel binary tree-based decomposition (BTBD) technique. The BTBD can exploit the statistical redundancy in frame-level syntax, motion components, and residuals efficiently with fewer block-level prediction/coding modes and simpler context modeling for context-adaptive arithmetic coding. Compared with the depth coder in 3D-HEVC, the proposed one has achieved significantly lower bitrate at lossless to near-lossless quality range for mono-view coding and rendered superior quality synthetic views from the depth maps, compressed at the same bitrate, and the corresponding texture frames. © 1991-2012 IEEE.
- Authors: Shahriyar, Shampa , Murshed, Manzur , Ali, Mortuza , Paul, Manoranjan
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Circuits and Systems for Video Technology Vol. 30, no. 3 (2020), p. 835-849
- Full Text:
- Reviewed:
- Description: Depth coding in 3D-HEVC deforms object shapes due to block-level edge-approximation and lacks efficient techniques to exploit the statistical redundancy, due to the frame-level clustering tendency in depth data, for higher coding gain at near-lossless quality. This paper presents a standalone mono-view depth sequence coder, which preserves edges implicitly by limiting quantization to the spatial-domain and exploits the frame-level clustering tendency efficiently with a novel binary tree-based decomposition (BTBD) technique. The BTBD can exploit the statistical redundancy in frame-level syntax, motion components, and residuals efficiently with fewer block-level prediction/coding modes and simpler context modeling for context-adaptive arithmetic coding. Compared with the depth coder in 3D-HEVC, the proposed one has achieved significantly lower bitrate at lossless to near-lossless quality range for mono-view coding and rendered superior quality synthetic views from the depth maps, compressed at the same bitrate, and the corresponding texture frames. © 1991-2012 IEEE.
Energy sector development : system dynamics analysis
- Laimon, Mohamd, Mai, Thanh, Goh, Steven, Yusaf, Talal
- Authors: Laimon, Mohamd , Mai, Thanh , Goh, Steven , Yusaf, Talal
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Sciences-Basel Vol. 10, no. 1 (Jan 2020), p. 19
- Full Text:
- Reviewed:
- Description: The development of a complex and dynamic system such as the energy sector requires a comprehensive understanding of its constituent components and their interactions, and thus requires approaches that can adapt to the dynamic complexity in systems. Previous efforts mainly used reductionist approaches, which examine the components of the system in isolation, neglecting their interdependent nature. Such approaches reduce our ability to understand the system and/or mitigate undesirable outcomes. We adopt a system dynamics approach to construct an integrated model for analysing the behaviour of the energy sector. Although the Australian energy sector is used as a case study, the model can be applied in other context elsewhere around the world The results indicate that the current trajectory of the Australian energy sector is unsustainable and growth is not being controlled. Limits to growth are fast approaching due to excessive fossil fuel extraction, high emissions and high energy dependency. With the current growth, Australia's global CO2 emissions footprint will increase to unprecedented levels reaching 12% by 2030 (9.5% for exports and 2.5% for domestic). Oil dependency will account for 43% and 47% of total consumption by 2030 and 2050. By 2032, coal will be the only fossil fuel resource available in Australia. Expansion of investment in coal and gas production is a large risk.
- Authors: Laimon, Mohamd , Mai, Thanh , Goh, Steven , Yusaf, Talal
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Sciences-Basel Vol. 10, no. 1 (Jan 2020), p. 19
- Full Text:
- Reviewed:
- Description: The development of a complex and dynamic system such as the energy sector requires a comprehensive understanding of its constituent components and their interactions, and thus requires approaches that can adapt to the dynamic complexity in systems. Previous efforts mainly used reductionist approaches, which examine the components of the system in isolation, neglecting their interdependent nature. Such approaches reduce our ability to understand the system and/or mitigate undesirable outcomes. We adopt a system dynamics approach to construct an integrated model for analysing the behaviour of the energy sector. Although the Australian energy sector is used as a case study, the model can be applied in other context elsewhere around the world The results indicate that the current trajectory of the Australian energy sector is unsustainable and growth is not being controlled. Limits to growth are fast approaching due to excessive fossil fuel extraction, high emissions and high energy dependency. With the current growth, Australia's global CO2 emissions footprint will increase to unprecedented levels reaching 12% by 2030 (9.5% for exports and 2.5% for domestic). Oil dependency will account for 43% and 47% of total consumption by 2030 and 2050. By 2032, coal will be the only fossil fuel resource available in Australia. Expansion of investment in coal and gas production is a large risk.
Evidence for cryptic diversity in the “pan-antarctic” springtail friesea antarctica and the description of two new species
- Carapelli, Antonio, Greenslade, Penelope, Nardi, Francesco, Leo, Chiara, Convey, Peter, Frati, Francesco, Fanciulli, Paolo
- Authors: Carapelli, Antonio , Greenslade, Penelope , Nardi, Francesco , Leo, Chiara , Convey, Peter , Frati, Francesco , Fanciulli, Paolo
- Date: 2020
- Type: Text , Journal article
- Relation: Insects Vol. 11, no. 3 (2020), p.
- Full Text:
- Reviewed:
- Description: The invertebrate terrestrial fauna of Antarctica is being investigated with increasing interest to discover how life interacts with the extreme polar environment and how millions of years of evolution have shaped their biodiversity. Classical taxonomic approaches, complemented by molecular tools, are improving our understanding of the systematic relationships of some species, changing the nomenclature of taxa and challenging the taxonomic status of others. The springtail Friesea grisea has previously been described as the only species with a “pan-Antarctic” distribution. However, recent genetic comparisons have pointed to another scenario. The latest morphological study has confined F. grisea to the sub-Antarctic island of South Georgia, from which it was originally described, and resurrected F. antarctica as a congeneric species occurring on the continental mainland. Molecular data demonstrate that populations of this taxon, ostensibly occurring across Maritime and Continental Antarctica, as well as on some offshore islands, are evolutionarily isolated and divergent and cannot be included within a single species. The present study, combining morphological with molecular data, attempts to validate this hypothesis and challenges the taxonomic status of F. antarctica, suggesting that two additional new species, described here as Friesea gretae sp. nov. and Friesea propria sp. nov., are present in Continental Antarctica. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Carapelli, Antonio , Greenslade, Penelope , Nardi, Francesco , Leo, Chiara , Convey, Peter , Frati, Francesco , Fanciulli, Paolo
- Date: 2020
- Type: Text , Journal article
- Relation: Insects Vol. 11, no. 3 (2020), p.
- Full Text:
- Reviewed:
- Description: The invertebrate terrestrial fauna of Antarctica is being investigated with increasing interest to discover how life interacts with the extreme polar environment and how millions of years of evolution have shaped their biodiversity. Classical taxonomic approaches, complemented by molecular tools, are improving our understanding of the systematic relationships of some species, changing the nomenclature of taxa and challenging the taxonomic status of others. The springtail Friesea grisea has previously been described as the only species with a “pan-Antarctic” distribution. However, recent genetic comparisons have pointed to another scenario. The latest morphological study has confined F. grisea to the sub-Antarctic island of South Georgia, from which it was originally described, and resurrected F. antarctica as a congeneric species occurring on the continental mainland. Molecular data demonstrate that populations of this taxon, ostensibly occurring across Maritime and Continental Antarctica, as well as on some offshore islands, are evolutionarily isolated and divergent and cannot be included within a single species. The present study, combining morphological with molecular data, attempts to validate this hypothesis and challenges the taxonomic status of F. antarctica, suggesting that two additional new species, described here as Friesea gretae sp. nov. and Friesea propria sp. nov., are present in Continental Antarctica. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Fraud detection for online banking for scalable and distributed data
- Authors: Haq, Ikram
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.
- Description: Doctor of Philosophy
- Authors: Haq, Ikram
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.
- Description: Doctor of Philosophy
High intensity interval training (HIIT) improves cardiorespiratory fitness (CRF) in healthy, overweight and obese adolescents : a systematic review and meta-analysis of controlled studies
- Martin-Smith, Rhona, Cox, Ashley, Buchan, Duncan, Baker, Julien, Grace, Fergal, Sculthorpe, Nicholas
- Authors: Martin-Smith, Rhona , Cox, Ashley , Buchan, Duncan , Baker, Julien , Grace, Fergal , Sculthorpe, Nicholas
- Date: 2020
- Type: Text , Journal article , Review
- Relation: International Journal of Environmental Research and Public Health Vol. 17, no. 8 (2020), p.
- Full Text:
- Reviewed:
- Description: Background: High Intensity Interval Training (HIIT) is a sustainable and effective method for improving Cardiorespiratory Fitness (CRF) in adolescents. HIIT is proven to produce equal or greater improvements in CRF when compared to moderate intensity continuous exercise (MICE) in adolescents. Methods: The studies included were considered eligible if: (1) Participants were adolescents (11–18 years old); (2) Examined changes in CRF measured either directly or indirectly; (3) Included a non-exercising control group or MICE comparison group; (4) Participants were matched at enrolment; (5) Reported HIIT protocol information; (6) Provided HIIT intensity. A meta-analysis was conducted to determine the effect of HIIT on CRF. Meta-regression and moderator analyses were performed out to quantitatively examine moderators of protocol design on CRF improvements. Results: HIIT displays a moderate effect to improve CRF (g = 0.86, 95% CI 0.518–1.106, p < 0.001). Neither study duration (weeks), nor total or weekly accumulated HIIT volume (min) displayed any significant moderation effect on pooled improvement on CRF (p > 0.05). Conclusions: HIIT is an effective method to improve CRF in adolescents, irrespective of body composition. Notably, meta regression analysis identified that prolonged high volume HIIT programs are similarly effective to short term low volume HIIT programs. This becomes of particular interest for those involved in school curricula, where short HIIT exercise may provide a pragmatic adjunct to the health benefits of Physical Education (PE) lessons. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Martin-Smith, Rhona , Cox, Ashley , Buchan, Duncan , Baker, Julien , Grace, Fergal , Sculthorpe, Nicholas
- Date: 2020
- Type: Text , Journal article , Review
- Relation: International Journal of Environmental Research and Public Health Vol. 17, no. 8 (2020), p.
- Full Text:
- Reviewed:
- Description: Background: High Intensity Interval Training (HIIT) is a sustainable and effective method for improving Cardiorespiratory Fitness (CRF) in adolescents. HIIT is proven to produce equal or greater improvements in CRF when compared to moderate intensity continuous exercise (MICE) in adolescents. Methods: The studies included were considered eligible if: (1) Participants were adolescents (11–18 years old); (2) Examined changes in CRF measured either directly or indirectly; (3) Included a non-exercising control group or MICE comparison group; (4) Participants were matched at enrolment; (5) Reported HIIT protocol information; (6) Provided HIIT intensity. A meta-analysis was conducted to determine the effect of HIIT on CRF. Meta-regression and moderator analyses were performed out to quantitatively examine moderators of protocol design on CRF improvements. Results: HIIT displays a moderate effect to improve CRF (g = 0.86, 95% CI 0.518–1.106, p < 0.001). Neither study duration (weeks), nor total or weekly accumulated HIIT volume (min) displayed any significant moderation effect on pooled improvement on CRF (p > 0.05). Conclusions: HIIT is an effective method to improve CRF in adolescents, irrespective of body composition. Notably, meta regression analysis identified that prolonged high volume HIIT programs are similarly effective to short term low volume HIIT programs. This becomes of particular interest for those involved in school curricula, where short HIIT exercise may provide a pragmatic adjunct to the health benefits of Physical Education (PE) lessons. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine
- Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar
- Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 9, no. 1 (2020), p.
- Full Text:
- Reviewed:
- Description: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 9, no. 1 (2020), p.
- Full Text:
- Reviewed:
- Description: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Implementing inclusion : classroom journeys
- Authors: Elvey, Moya
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: This study interrogates the professional experiences, attitudes and pedagogical choices of eight classroom teachers in regular schools and inquires into their impact on the development of inclusive teaching practices. Approached from the perspective of an experienced teaching practitioner, the study responds to the call for an increased focus on the role of classroom teachers in implementing inclusion in schools. The study is underpinned by a theoretical stance that promotes the value of inclusive education through a human rights, access and equity framework. It advocates for the importance of overcoming the discriminatory practices that marginalise some students. Consistent with a qualitative, ethnographic methodology, observations and interviews with practicing teachers provide insights into the factors that encourage, and sometimes discourage, the enactment of inclusive pedagogies. The literature on inclusive education provides guidance throughout the data collection and analysis process. This includes frameworks designed by other researchers that outline and define inclusive teaching strategies. The study exposes the pivotal role that ongoing teacher professional learning, along with strategic guidance and support from colleagues and school leaders, plays in enhancing teacher capacity and positive attitudes towards student diversity. It also uncovers evidence that when medical reports and pressure from ‘others’ such as health professionals, encourage teachers to focus on student ‘deficits’ and ‘problems’, they are more likely to seek out and adopt strategies that marginalise and set some students apart from their peers. A fundamental finding of this study is that when teachers and their school leaders focus on developing understanding about ‘effective’ pedagogies - on quality education for all - responsive, inclusive, student-centred teaching approaches often become embedded in their everyday classroom practice.
- Description: PhD
- Authors: Elvey, Moya
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: This study interrogates the professional experiences, attitudes and pedagogical choices of eight classroom teachers in regular schools and inquires into their impact on the development of inclusive teaching practices. Approached from the perspective of an experienced teaching practitioner, the study responds to the call for an increased focus on the role of classroom teachers in implementing inclusion in schools. The study is underpinned by a theoretical stance that promotes the value of inclusive education through a human rights, access and equity framework. It advocates for the importance of overcoming the discriminatory practices that marginalise some students. Consistent with a qualitative, ethnographic methodology, observations and interviews with practicing teachers provide insights into the factors that encourage, and sometimes discourage, the enactment of inclusive pedagogies. The literature on inclusive education provides guidance throughout the data collection and analysis process. This includes frameworks designed by other researchers that outline and define inclusive teaching strategies. The study exposes the pivotal role that ongoing teacher professional learning, along with strategic guidance and support from colleagues and school leaders, plays in enhancing teacher capacity and positive attitudes towards student diversity. It also uncovers evidence that when medical reports and pressure from ‘others’ such as health professionals, encourage teachers to focus on student ‘deficits’ and ‘problems’, they are more likely to seek out and adopt strategies that marginalise and set some students apart from their peers. A fundamental finding of this study is that when teachers and their school leaders focus on developing understanding about ‘effective’ pedagogies - on quality education for all - responsive, inclusive, student-centred teaching approaches often become embedded in their everyday classroom practice.
- Description: PhD
Internal and external validity of the brief version of the multidimensional personality questionnaire : exploratory structural equation modelling
- Gomez, Rapson, Griffiths, Mark, Stavropoulos, Vasileios
- Authors: Gomez, Rapson , Griffiths, Mark , Stavropoulos, Vasileios
- Date: 2020
- Type: Text , Journal article , Article
- Relation: PLoS ONE Vol. 15, no. 4 (2020)
- Full Text:
- Reviewed:
- Description: The present study used exploratory structural equation modelling (ESEM) to examine the theorized dimension structure of the brief version of the Multidimensional Personality Questionnaire (MPQ-BR) at the scale-level (i.e., 11 lower-order primary factors loading on four higher-order factors) and item-level (sets of 12 items loading on 11 lower-order primary factors). A total of 214 adults from the community addressed the MPQ-BR and the Behavioral Inhibition System (BIS)/Behavioral Approach System (BAS) scales. The findings revealed poor fit and poorly defined factors at the item-level alongside adequate fit and well-defined factors at the scale-level. The higher-order factors in the latter model were supported for external validity in terms of demonstrating the expected theoretical and empirical correlations with the scales of the BIS/BAS scales. Result related implications for professional application, as well as potential revisions of the MPQ-BF are illustrated. © 2020 Gomez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Authors: Gomez, Rapson , Griffiths, Mark , Stavropoulos, Vasileios
- Date: 2020
- Type: Text , Journal article , Article
- Relation: PLoS ONE Vol. 15, no. 4 (2020)
- Full Text:
- Reviewed:
- Description: The present study used exploratory structural equation modelling (ESEM) to examine the theorized dimension structure of the brief version of the Multidimensional Personality Questionnaire (MPQ-BR) at the scale-level (i.e., 11 lower-order primary factors loading on four higher-order factors) and item-level (sets of 12 items loading on 11 lower-order primary factors). A total of 214 adults from the community addressed the MPQ-BR and the Behavioral Inhibition System (BIS)/Behavioral Approach System (BAS) scales. The findings revealed poor fit and poorly defined factors at the item-level alongside adequate fit and well-defined factors at the scale-level. The higher-order factors in the latter model were supported for external validity in terms of demonstrating the expected theoretical and empirical correlations with the scales of the BIS/BAS scales. Result related implications for professional application, as well as potential revisions of the MPQ-BF are illustrated. © 2020 Gomez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Investigating cumulative effects of preperformance routine interventions in beach volleyball serving
- Wergin, Vanessa, Beckmann, Jurgen, Gröpel, Peter, Mesagno, Christopher
- Authors: Wergin, Vanessa , Beckmann, Jurgen , Gröpel, Peter , Mesagno, Christopher
- Date: 2020
- Type: Text , Journal article , Article
- Relation: PLoS ONE Vol. 15, no. 1 (2020), p.
- Full Text:
- Reviewed:
- Description: Pre-performance routines (PPRs) can be used in certain sports to minimize the effects of choking under pressure. This study aimed to investigate the cumulative effectiveness of PPR interventions on the accuracy of beach volleyball serves. Fifty-four beach volleyball players were randomly assigned to one of three PPR intervention groups or a control group. Participants performed 10 serves at a target on the opposite side of the beach volleyball court (pretest), were educated on a PPR intervention, and then completed 10 serves at the target under pressure that was induced through videotaping and ego-relevant instructions (posttest). The results indicated no difference in post-test serving accuracy among the intervention groups and the wait-list control group and no difference in effectiveness between cumulative and isolated PPR use. A possible explanation may be the inefficiency of the pressure manipulation. However, the null results related to isolated and cumulative PPR use under general (i.e., no pressure) conditions are still an important research finding. Future research should investigate the effectiveness of cumulative and other PPRs in other sports in general and under pressure. © 2020 Wergin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Investigating cumulative effects of preperformance routine interventions in beach volleyball serving
- Authors: Wergin, Vanessa , Beckmann, Jurgen , Gröpel, Peter , Mesagno, Christopher
- Date: 2020
- Type: Text , Journal article , Article
- Relation: PLoS ONE Vol. 15, no. 1 (2020), p.
- Full Text:
- Reviewed:
- Description: Pre-performance routines (PPRs) can be used in certain sports to minimize the effects of choking under pressure. This study aimed to investigate the cumulative effectiveness of PPR interventions on the accuracy of beach volleyball serves. Fifty-four beach volleyball players were randomly assigned to one of three PPR intervention groups or a control group. Participants performed 10 serves at a target on the opposite side of the beach volleyball court (pretest), were educated on a PPR intervention, and then completed 10 serves at the target under pressure that was induced through videotaping and ego-relevant instructions (posttest). The results indicated no difference in post-test serving accuracy among the intervention groups and the wait-list control group and no difference in effectiveness between cumulative and isolated PPR use. A possible explanation may be the inefficiency of the pressure manipulation. However, the null results related to isolated and cumulative PPR use under general (i.e., no pressure) conditions are still an important research finding. Future research should investigate the effectiveness of cumulative and other PPRs in other sports in general and under pressure. © 2020 Wergin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Learning large margin multiple granularity features with an improved siamese network for person re-identification
- Li, Da-Xiang, Fei, Gy, Teng, Shyh
- Authors: Li, Da-Xiang , Fei, Gy , Teng, Shyh
- Date: 2020
- Type: Text , Journal article , Article
- Relation: Symmetry-Basel Vol. 12, no. 1 (Jan 2020), p. 16
- Full Text:
- Reviewed:
- Description: Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture for person re-identification, Siamese networks usually adopt standard softmax loss function, and they can only obtain the global features of person images, ignoring the local features and the large margin for classification. In this paper, we design a novel symmetric Siamese network model named Siamese Multiple Granularity Network (SMGN), which can jointly learn the large margin multiple granularity features and similarity metrics for person re-identification. Firstly, two branches for global and local feature extraction are designed in the backbone of the proposed SMGN model, and the extracted features are concatenated together as multiple granularity features of person images. Then, to enhance their discriminating ability, the multiple channel weighted fusion (MCWF) loss function is constructed for the SMGN model, which includes the verification loss and identification loss of the training image pair. Extensive comparative experiments on four benchmark datasets (CUHK01, CUHK03, Market-1501 and DukeMTMC-reID) show the effectiveness of our proposed method and its performance outperforms many state-of-the-art methods.
- Authors: Li, Da-Xiang , Fei, Gy , Teng, Shyh
- Date: 2020
- Type: Text , Journal article , Article
- Relation: Symmetry-Basel Vol. 12, no. 1 (Jan 2020), p. 16
- Full Text:
- Reviewed:
- Description: Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture for person re-identification, Siamese networks usually adopt standard softmax loss function, and they can only obtain the global features of person images, ignoring the local features and the large margin for classification. In this paper, we design a novel symmetric Siamese network model named Siamese Multiple Granularity Network (SMGN), which can jointly learn the large margin multiple granularity features and similarity metrics for person re-identification. Firstly, two branches for global and local feature extraction are designed in the backbone of the proposed SMGN model, and the extracted features are concatenated together as multiple granularity features of person images. Then, to enhance their discriminating ability, the multiple channel weighted fusion (MCWF) loss function is constructed for the SMGN model, which includes the verification loss and identification loss of the training image pair. Extensive comparative experiments on four benchmark datasets (CUHK01, CUHK03, Market-1501 and DukeMTMC-reID) show the effectiveness of our proposed method and its performance outperforms many state-of-the-art methods.