Spatial epidemiology : A new approach for understanding and preventing sport injuries
- Singh, Himalaya, Fortington, Lauren, Eime, Rochelle, Thompson, Helen, Finch, Caroline
- Authors: Singh, Himalaya , Fortington, Lauren , Eime, Rochelle , Thompson, Helen , Finch, Caroline
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Epidemiologist Vol. 22, no. 1 (2015), p. 32-34
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text: false
- Reviewed:
- Description: In order to develop effective strategies to prevent sports injuries, we need to have an understanding of the people and populations who are most at risk of injury as well as the risk factors associated with sustaining injury. Spatial epidemiology is a method used to address questions of when, where, to whom and how health outcomes such as sports injuries occur at a population level, taking into account geographic variation. The aim of this article is to outline the potential application of spatial epidemiology to achieve a better understanding of sports injuries to inform prevention strategies.
An overview of geospatial methods used in unintentional injury epidemiology
- Singh, Himalaya, Fortington, Lauren, Thompson, Helen, Finch, Caroline
- Authors: Singh, Himalaya , Fortington, Lauren , Thompson, Helen , Finch, Caroline
- Date: 2016
- Type: Text , Journal article
- Relation: Injury Epidemiology Vol. 3, no. 32 (2016), p. 1-12
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text:
- Reviewed:
- Description: BACKGROUND: Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies. METHODS: Nine electronic databases were searched for papers published in 2000-2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods. RESULTS: From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental). CONCLUSION: The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches.
- Authors: Singh, Himalaya , Fortington, Lauren , Thompson, Helen , Finch, Caroline
- Date: 2016
- Type: Text , Journal article
- Relation: Injury Epidemiology Vol. 3, no. 32 (2016), p. 1-12
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text:
- Reviewed:
- Description: BACKGROUND: Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies. METHODS: Nine electronic databases were searched for papers published in 2000-2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods. RESULTS: From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental). CONCLUSION: The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches.
Soil moisture, organic carbon, and nitrogen content prediction with hyperspectral data using regression models
- Datta, Dristi, Paul, Manoranjan, Murshed, Manzur, Teng, Shyh Wei, Schmidtke, Leigh
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2022
- Type: Text , Journal article
- Relation: Sensors (Basel, Switzerland) Vol. 22, no. 20 (2022), p.
- Full Text:
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- Description: Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2022
- Type: Text , Journal article
- Relation: Sensors (Basel, Switzerland) Vol. 22, no. 20 (2022), p.
- Full Text:
- Reviewed:
- Description: Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
Stability prediction of residual soil and rock slope using artificial neural network
- Paliwal, M., Goswami, H., Ray, A., Bharati, A. K., Rai, R., Khandelwal, M.
- Authors: Paliwal, M. , Goswami, H. , Ray, A. , Bharati, A. K. , Rai, R. , Khandelwal, M.
- Date: 2022
- Type: Text , Journal article
- Relation: Advances in Civil Engineering Vol. 2022, no. (2022), p.
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- Description: A sudden downward movement of the geomaterial, either composed of soil, rock, or a mixture of both, along the mountain slopes due to various natural or anthropogenic factors is known as a landslide. The Himalayan Mountain slopes are either made up of residual soil or rocks. Residual soil is formed from weathering of the bedrock and mainly occurs in gentle-to-moderate slope inclinations. In contrast, steep slopes are mostly devoid of soil cover and are primarily rocky. A stability prediction system that can analyse the slope under both the condition of the soil or rock surface is missing. In this study, artificial neural network technology has been utilised to predict the stability of jointed rock and residual soil slope of the Himalayan region. The database for the artificial neural network was obtained from numerical simulation of several residual soils and rock slope models. Nonlinear equations have been formulated by coding the artificial neural network algorithm. An android application has also been developed to predict the stability of residual soil and rock slope instantly. It was observed that the developed android app provides promising results in predicting the factor of safety and stability state of the slopes. © 2022 Mahesh Paliwal et al. This is an open access article distributed under the Creative Commons Attribution License.
- Authors: Paliwal, M. , Goswami, H. , Ray, A. , Bharati, A. K. , Rai, R. , Khandelwal, M.
- Date: 2022
- Type: Text , Journal article
- Relation: Advances in Civil Engineering Vol. 2022, no. (2022), p.
- Full Text:
- Reviewed:
- Description: A sudden downward movement of the geomaterial, either composed of soil, rock, or a mixture of both, along the mountain slopes due to various natural or anthropogenic factors is known as a landslide. The Himalayan Mountain slopes are either made up of residual soil or rocks. Residual soil is formed from weathering of the bedrock and mainly occurs in gentle-to-moderate slope inclinations. In contrast, steep slopes are mostly devoid of soil cover and are primarily rocky. A stability prediction system that can analyse the slope under both the condition of the soil or rock surface is missing. In this study, artificial neural network technology has been utilised to predict the stability of jointed rock and residual soil slope of the Himalayan region. The database for the artificial neural network was obtained from numerical simulation of several residual soils and rock slope models. Nonlinear equations have been formulated by coding the artificial neural network algorithm. An android application has also been developed to predict the stability of residual soil and rock slope instantly. It was observed that the developed android app provides promising results in predicting the factor of safety and stability state of the slopes. © 2022 Mahesh Paliwal et al. This is an open access article distributed under the Creative Commons Attribution License.
Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques
- Rezaeineshat, Afsaneh, Monjezi, Masoud, Mehrdanesh, Amirhossein, Khandelwal, Manoj
- Authors: Rezaeineshat, Afsaneh , Monjezi, Masoud , Mehrdanesh, Amirhossein , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 2 (2020), p.
- Full Text:
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- Description: Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG.
- Authors: Rezaeineshat, Afsaneh , Monjezi, Masoud , Mehrdanesh, Amirhossein , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 2 (2020), p.
- Full Text:
- Reviewed:
- Description: Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG.
Injury rate and patterns of Sydney grade cricketers : A prospective study of injuries in 408 cricketers
- Soomro, Najeebullah, Redrup, Daniel, Evens, Chris, Strasiotto, Luke, Singh, Shekhar, Lyle, David, Singh, Himalaya, Ferdinands, Rene, Sanders, Ross
- Authors: Soomro, Najeebullah , Redrup, Daniel , Evens, Chris , Strasiotto, Luke , Singh, Shekhar , Lyle, David , Singh, Himalaya , Ferdinands, Rene , Sanders, Ross
- Date: 2018
- Type: Text , Journal article
- Relation: Postgraduate Medical Journal Vol. 94, no. 1114 (2018), p. 425-431
- Full Text:
- Reviewed:
- Description: Background The grade cricket competition, also known as premier cricket
- Authors: Soomro, Najeebullah , Redrup, Daniel , Evens, Chris , Strasiotto, Luke , Singh, Shekhar , Lyle, David , Singh, Himalaya , Ferdinands, Rene , Sanders, Ross
- Date: 2018
- Type: Text , Journal article
- Relation: Postgraduate Medical Journal Vol. 94, no. 1114 (2018), p. 425-431
- Full Text:
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- Description: Background The grade cricket competition, also known as premier cricket
Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band
- Datta, Dristi, Paul, Manoranjan, Murshed, Manzur, Teng, Shyh Wei, Schmidtke, Leigh
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2023
- Type: Text , Journal article
- Relation: Environments Vol. 10, no. 5 (2023), p. 77
- Full Text:
- Reviewed:
- Description: Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2023
- Type: Text , Journal article
- Relation: Environments Vol. 10, no. 5 (2023), p. 77
- Full Text:
- Reviewed:
- Description: Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
Stability prediction of Himalayan residual soil slope using artificial neural network
- Ray, Arunava, Kumar, Vikash, Kumar, Amit, Rai, Rajesh, Khandelwal, Manoj, Singh, T.
- Authors: Ray, Arunava , Kumar, Vikash , Kumar, Amit , Rai, Rajesh , Khandelwal, Manoj , Singh, T.
- Date: 2020
- Type: Text , Journal article
- Relation: Natural Hazards Vol. 103, no. 3 (2020), p. 3523-3540
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- Description: In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. © 2020, Springer Nature B.V.
- Authors: Ray, Arunava , Kumar, Vikash , Kumar, Amit , Rai, Rajesh , Khandelwal, Manoj , Singh, T.
- Date: 2020
- Type: Text , Journal article
- Relation: Natural Hazards Vol. 103, no. 3 (2020), p. 3523-3540
- Full Text:
- Reviewed:
- Description: In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. © 2020, Springer Nature B.V.
- Authors: Mestrom, Sanne
- Date: 2012
- Type: Text , Visual art work
- Full Text:
Autumn food habits of the brown bear Ursus arctos in the Golestan National Park : A pilot study in Iran
- Soofi, Mahmood, Qashqaei, Ali, Aryal, Achyut, Coogan, Sean
- Authors: Soofi, Mahmood , Qashqaei, Ali , Aryal, Achyut , Coogan, Sean
- Date: 2018
- Type: Text , Journal article
- Relation: Mammalia Vol. 82, no. 4 (2018), p. 338-342
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- Description: Food consumed by brown bears in the Golestan National Park in Iran was analyzed during autumn 2011. We identified 22 food items in 61 scats, with the most important food items being hawthorn fruit, cherry plum fruit and chestnut-leaved oak hard mast, based on importance value (IV) estimates of 26.4%, 18.1% and 12.9%, respectively. The overall bear diet (percent digestible dry matter) was composed of 77.9% soft mast (i.e. fruit), 21.3% hard mast and small proportions of other vegetation (0.3%) or animal matter (0.4%). One anthropogenic food was identified (vine grape) and was of minor importance (IV=0.2%).
- Authors: Soofi, Mahmood , Qashqaei, Ali , Aryal, Achyut , Coogan, Sean
- Date: 2018
- Type: Text , Journal article
- Relation: Mammalia Vol. 82, no. 4 (2018), p. 338-342
- Full Text:
- Reviewed:
- Description: Food consumed by brown bears in the Golestan National Park in Iran was analyzed during autumn 2011. We identified 22 food items in 61 scats, with the most important food items being hawthorn fruit, cherry plum fruit and chestnut-leaved oak hard mast, based on importance value (IV) estimates of 26.4%, 18.1% and 12.9%, respectively. The overall bear diet (percent digestible dry matter) was composed of 77.9% soft mast (i.e. fruit), 21.3% hard mast and small proportions of other vegetation (0.3%) or animal matter (0.4%). One anthropogenic food was identified (vine grape) and was of minor importance (IV=0.2%).
A framework for sustainability performance assessment for manufacturing processes
- Authors: Singh, Karmjit
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: Sustainable manufacturing methods make it possible to develop products in ways which minimize negative environmental impacts, conserve energy and save natural resources whilst being economically sound. The concepts of sustainability in manufacturing being are still fairly broad, in scope, and need to be more focused and firmly established at the process, machine or factory levels. This project proposes a structure for manufacturing with a main objective to develop a sustainability framework which encompasses various production processes. Structured information models for the seamless flow of information across the design and manufacturing domains, for selected manufacturing processes, are defined. The thesis work identifies key performance indicators (KPIs) for the assessment of manufacturing sustainability and performs analysis of selected unit manufacturing processes and their sub-processes with the aim of proposing a methodology for determining science-based measurements of the manufacturing processes affecting these KPIs. The theoretical foundations established are then used to develop a model that could evaluate sustainability of selected manufacturing processes and their respective process plans providing a basis for inter-process comparison and selection of the most sustainable process plan. The proposed framework is presented in form of a manufacturing planning computer-based package which is designed to to consider different influencing factors such as product information, part geometry, material related physical and processing properties and the manufacturing equipment operating data. The thesis presents a number of case studies which have been published in international journals. The case studies present estimates of the manufacturing sustainability KPIs for a number of production methods. These estimates have been verified with available shop floor data. The work in the thesis makes it possible to establish manufacturing industry equipped to deal the challenges of the future when sustainability will be the major factor up on which the quality of success will be determined.
- Description: Doctor of Philosophy
- Authors: Singh, Karmjit
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: Sustainable manufacturing methods make it possible to develop products in ways which minimize negative environmental impacts, conserve energy and save natural resources whilst being economically sound. The concepts of sustainability in manufacturing being are still fairly broad, in scope, and need to be more focused and firmly established at the process, machine or factory levels. This project proposes a structure for manufacturing with a main objective to develop a sustainability framework which encompasses various production processes. Structured information models for the seamless flow of information across the design and manufacturing domains, for selected manufacturing processes, are defined. The thesis work identifies key performance indicators (KPIs) for the assessment of manufacturing sustainability and performs analysis of selected unit manufacturing processes and their sub-processes with the aim of proposing a methodology for determining science-based measurements of the manufacturing processes affecting these KPIs. The theoretical foundations established are then used to develop a model that could evaluate sustainability of selected manufacturing processes and their respective process plans providing a basis for inter-process comparison and selection of the most sustainable process plan. The proposed framework is presented in form of a manufacturing planning computer-based package which is designed to to consider different influencing factors such as product information, part geometry, material related physical and processing properties and the manufacturing equipment operating data. The thesis presents a number of case studies which have been published in international journals. The case studies present estimates of the manufacturing sustainability KPIs for a number of production methods. These estimates have been verified with available shop floor data. The work in the thesis makes it possible to establish manufacturing industry equipped to deal the challenges of the future when sustainability will be the major factor up on which the quality of success will be determined.
- Description: Doctor of Philosophy
Authentication using volatile composition : a proof-of-concept study on the volatile profiles of fourteen queensland ciders
- Wilson, Arron, Johnson, Joel, Batley, Ryan, Lal, Pawan, Wakeling, Lara, Naiker, Mani
- Authors: Wilson, Arron , Johnson, Joel , Batley, Ryan , Lal, Pawan , Wakeling, Lara , Naiker, Mani
- Date: 2021
- Type: Text , Journal article
- Relation: Beverages Vol. 7, no. 2 (2021), p.
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- Description: Although relatively small, the Australian cider industry has experienced significant growth in recent years. One of the current challenges in the industry is the lack of research specific to Australian ciders. Establishing baseline volatile organic compound (VOC) profiles of Australian cider is paramount to developing a better understanding of the industry. This understanding may ultimately be utilized for both the categorization and authentication of existing ciders, and the targeted modification of cider volatiles for the development and improvement of cider quality. This study utilized gas chromatography, coupled with mass spectrometry, to identify key VOCs present in 14 ciders sourced from four different manufacturers in Queensland, Australia. A total of 40 VOCs were identified across the ciders, with significant variation depending on the flavor and manufacturer. Principal component analysis indicated that the ciders were well-separated based on the manufacturer, supporting the prospect of using the volatile composition to discriminate between cider manufacturers. Furthermore, hierarchical cluster analysis highlighted the commonalities and differences in cider composition between different manufacturers, which may be indicative of the varying ingredients and manufacturing processes used to create the ciders. Future studies profiling the volatile composition of larger numbers of Australian ciders are recommended to support the use of this analytical technique for authentication purposes. Likewise, exploration of the relationship between specific processes and VOCs is recommended to fortify an understanding of how to optimize cider production to improve consumer satisfaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Wilson, Arron , Johnson, Joel , Batley, Ryan , Lal, Pawan , Wakeling, Lara , Naiker, Mani
- Date: 2021
- Type: Text , Journal article
- Relation: Beverages Vol. 7, no. 2 (2021), p.
- Full Text:
- Reviewed:
- Description: Although relatively small, the Australian cider industry has experienced significant growth in recent years. One of the current challenges in the industry is the lack of research specific to Australian ciders. Establishing baseline volatile organic compound (VOC) profiles of Australian cider is paramount to developing a better understanding of the industry. This understanding may ultimately be utilized for both the categorization and authentication of existing ciders, and the targeted modification of cider volatiles for the development and improvement of cider quality. This study utilized gas chromatography, coupled with mass spectrometry, to identify key VOCs present in 14 ciders sourced from four different manufacturers in Queensland, Australia. A total of 40 VOCs were identified across the ciders, with significant variation depending on the flavor and manufacturer. Principal component analysis indicated that the ciders were well-separated based on the manufacturer, supporting the prospect of using the volatile composition to discriminate between cider manufacturers. Furthermore, hierarchical cluster analysis highlighted the commonalities and differences in cider composition between different manufacturers, which may be indicative of the varying ingredients and manufacturing processes used to create the ciders. Future studies profiling the volatile composition of larger numbers of Australian ciders are recommended to support the use of this analytical technique for authentication purposes. Likewise, exploration of the relationship between specific processes and VOCs is recommended to fortify an understanding of how to optimize cider production to improve consumer satisfaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis
- Abu-Siada, Ahmed, Hmood, Sdood, Islam, Syed
- Authors: Abu-Siada, Ahmed , Hmood, Sdood , Islam, Syed
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 20, no. 6 (2013), p. 2343-2349
- Full Text:
- Reviewed:
- Description: Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.
- Authors: Abu-Siada, Ahmed , Hmood, Sdood , Islam, Syed
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 20, no. 6 (2013), p. 2343-2349
- Full Text:
- Reviewed:
- Description: Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.
Germination ecology of Chloris truncata and its implication for weed management
- Chauhan, Bhagirath, Manalil, Sudheesh, Florentine, Singarayer, Jha, Prashant
- Authors: Chauhan, Bhagirath , Manalil, Sudheesh , Florentine, Singarayer , Jha, Prashant
- Date: 2018
- Type: Text , Journal article
- Relation: PLoS ONE Vol. 13, no. 7 (2018), p. 1-13
- Full Text:
- Reviewed:
- Description: Chloris truncata is a significant weed in summer crops in the subtropical region of Australia. A study was conducted to evaluate the effect of environmental factors on germination and emergence of two populations of C. truncata. Overall, germination was not affected by the populations. Seeds germinated at a wide range of alternating day/night temperatures, suggesting that seeds can germinate throughout the spring, winter and autumn seasons. Seed germination was stimulated by the presence of light; however, 51 to 71% of these seeds still germinated in the dark. The sodium chloride concentration and osmotic potential required to inhibit germination of 50% of the population were 179 mM and -0.52 MPa, respectively. A high proportion of seeds germinated over a wide pH range (4 to 10). Seeds placed on the soil surface had greatest germination (67%) and a burial depth of 3 cm resulted in complete inhibition of emergence. The sorghum residue amount required to reduce emergence by 50% was 1.8 t ha-1. The results suggest that, although this weed will be favored in no-till systems, residue retention on the soil surface will help in reducing its infestation. Seed bank buildup can be managed by burying seeds below the depth of emergence. © 2018 Chauhan 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: Chauhan, Bhagirath , Manalil, Sudheesh , Florentine, Singarayer , Jha, Prashant
- Date: 2018
- Type: Text , Journal article
- Relation: PLoS ONE Vol. 13, no. 7 (2018), p. 1-13
- Full Text:
- Reviewed:
- Description: Chloris truncata is a significant weed in summer crops in the subtropical region of Australia. A study was conducted to evaluate the effect of environmental factors on germination and emergence of two populations of C. truncata. Overall, germination was not affected by the populations. Seeds germinated at a wide range of alternating day/night temperatures, suggesting that seeds can germinate throughout the spring, winter and autumn seasons. Seed germination was stimulated by the presence of light; however, 51 to 71% of these seeds still germinated in the dark. The sodium chloride concentration and osmotic potential required to inhibit germination of 50% of the population were 179 mM and -0.52 MPa, respectively. A high proportion of seeds germinated over a wide pH range (4 to 10). Seeds placed on the soil surface had greatest germination (67%) and a burial depth of 3 cm resulted in complete inhibition of emergence. The sorghum residue amount required to reduce emergence by 50% was 1.8 t ha-1. The results suggest that, although this weed will be favored in no-till systems, residue retention on the soil surface will help in reducing its infestation. Seed bank buildup can be managed by burying seeds below the depth of emergence. © 2018 Chauhan 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.
Spatial epidemiological investigation of sport and leisure injuries in Victoria, Australia
- Authors: Singh, Himalaya
- Date: 2018
- Type: Text , Thesis , PhD
- Full Text:
- Description: Sport and leisure injuries are recognised as a public health issue in Australia. Despite the many health benefits associated with sport and leisure participation, there is a risk of sustaining injury during participation. To keep Australia active, there is a critical need to prevent injury occurrence. Epidemiological investigations in sport and leisure injuries have been largely examined by grouping of sports, age groups, sex and level of play. In addition, intrinsic (person-level) factors have been considered, such as strength, flexibility or previous injury history. These factors may not be sufficient to identify injury burden or prevent an increase in injury incidences. In the broader injury literature (e.g., road traffic crashes or drowning), it is known that injuries often cluster within specific places (i.e., road intersections or bodies of water). These specific geographic locations may also relate to sport and leisure injuries (e.g., sports grounds or facilities). Similarly, population-level factors such as socio-economic status or cultural groups within an area could influence the types of sports and leisure activities people participate in and consequently, the injuries that occur. A review presented in this PhD thesis revealed that there is very limited sport and leisure injury epidemiological information from a geographical perspective. To address this gap, and determine whether there is a spatial pattern in sport/leisure injuries, the aim of this PhD was to examine the geospatial distribution of sport/leisure injury hospitalisations and their association with a broad range of social and economic characteristics. This thesis uses spatial epidemiological methods to answer questions such as ‘Where do sports and leisure injuries occur?’ and ‘In whom do sports/leisure injuries occur?’ The main chapters present the results of the application of spatial epidemiological methods to describe the problem, to test hypotheses and to explore associations with possible explanatory variables. The findings showed a significant variation across metropolitan, regional and rural areas in the pattern and clustering of injuries when examining different sports, age groups and other variables such as education level. A secondary aim of this thesis was to consider the dissemination of sport and injury epidemiological data. As emphasised in the literature, there is limited spatial epidemiological information available to decision-makers and key stakeholders. At best, descriptive maps might be included in a report or research paper. However, these are static and limited to the results that the author chooses to present. Therefore, an important output from this PhD is a web-GIS application that has been specifically built to enable the exploratory analysis of sport/leisure injuries in Victoria. Sport and leisure injury prevention strategies and policy development relies on information about where, when, to whom and how sport/leisure injuries occur. This thesis demonstrates that a spatial epidemiological approach is an important and novel way to address epidemiological questions from a geographical perspective.
- Description: Doctor of Philosophy
- Authors: Singh, Himalaya
- Date: 2018
- Type: Text , Thesis , PhD
- Full Text:
- Description: Sport and leisure injuries are recognised as a public health issue in Australia. Despite the many health benefits associated with sport and leisure participation, there is a risk of sustaining injury during participation. To keep Australia active, there is a critical need to prevent injury occurrence. Epidemiological investigations in sport and leisure injuries have been largely examined by grouping of sports, age groups, sex and level of play. In addition, intrinsic (person-level) factors have been considered, such as strength, flexibility or previous injury history. These factors may not be sufficient to identify injury burden or prevent an increase in injury incidences. In the broader injury literature (e.g., road traffic crashes or drowning), it is known that injuries often cluster within specific places (i.e., road intersections or bodies of water). These specific geographic locations may also relate to sport and leisure injuries (e.g., sports grounds or facilities). Similarly, population-level factors such as socio-economic status or cultural groups within an area could influence the types of sports and leisure activities people participate in and consequently, the injuries that occur. A review presented in this PhD thesis revealed that there is very limited sport and leisure injury epidemiological information from a geographical perspective. To address this gap, and determine whether there is a spatial pattern in sport/leisure injuries, the aim of this PhD was to examine the geospatial distribution of sport/leisure injury hospitalisations and their association with a broad range of social and economic characteristics. This thesis uses spatial epidemiological methods to answer questions such as ‘Where do sports and leisure injuries occur?’ and ‘In whom do sports/leisure injuries occur?’ The main chapters present the results of the application of spatial epidemiological methods to describe the problem, to test hypotheses and to explore associations with possible explanatory variables. The findings showed a significant variation across metropolitan, regional and rural areas in the pattern and clustering of injuries when examining different sports, age groups and other variables such as education level. A secondary aim of this thesis was to consider the dissemination of sport and injury epidemiological data. As emphasised in the literature, there is limited spatial epidemiological information available to decision-makers and key stakeholders. At best, descriptive maps might be included in a report or research paper. However, these are static and limited to the results that the author chooses to present. Therefore, an important output from this PhD is a web-GIS application that has been specifically built to enable the exploratory analysis of sport/leisure injuries in Victoria. Sport and leisure injury prevention strategies and policy development relies on information about where, when, to whom and how sport/leisure injuries occur. This thesis demonstrates that a spatial epidemiological approach is an important and novel way to address epidemiological questions from a geographical perspective.
- Description: Doctor of Philosophy
Firm growth by women-owned Small and Medium Enterprises in a developing economy setting
- Authors: Jomaraty, Mosfeka
- Date: 2015
- Type: Text , Thesis , PhD
- Full Text:
- Description: The growth experiences of women-owned Small and Medium Enterprises (SMEs) in the context of a developing economy are examined through the lens of pragmatism. This approach views a businesswoman’s ‘belief’, ‘habit’ and ‘doubt’ as critical for researching gender related issues in entrepreneurship. This study explains the growth aspects of women-owned manufacturing and services SMEs of Bangladesh with the aim of addressing two neglected research issues. One is the scarcity of studies on growth oriented women entrepreneurs in developing countries. The other is the lack of focus on very successful high-growth women-owned firms in the context of a strong male-dominated economy. This study adopts a framework developed out of the Diana International Project to evaluate the factors influencing the growth of these successful, growing, Bangladeshi women-owned businesses. In order to evaluate the growth process itself, this framework was then modified with growth resources and actions as explained by Edith Penrose in her 1959 seminal book The Theory of Growth of the Firm. This allows for the investigation of the effects of managerial and entrepreneurial abilities in growth, and the identification of how firms achieve growth. A multiple-case design is adopted, covering sixteen successful growth-oriented firms in the manufacturing and services sector. SMEs were studied as the basis for firm growth from initial venture creation, while the sector concentration on manufacturing and services reflects the urban nature of the study in examining firms that exist in the capital city of Dhaka. Data from in-depth interviews and supporting documents were used for the case studies and integrated with the theoretical framework. Themes were categorised and patterns compared against the framework. The results of this research suggest that SME growth is a process which is gradual and iterative, comprising a series of growth strategies and approaches. The framework identifies interactive connection between different growth variables and highlights how industry sector and the national context of a growing economy facilitate growth of women-owned SMEs. The case study based research seeks to advance scholarship in relation to women’s entrepreneurship globally and contribute to the understanding of growth oriented women’s entrepreneurship. Building upon existing knowledge, this research endeavours to generate new insights and advance theoretical discourse by providing richness and subtlety to the knowledge of growth process and opening up new avenues for future research.
- Description: Doctor of Philosophy
- Authors: Jomaraty, Mosfeka
- Date: 2015
- Type: Text , Thesis , PhD
- Full Text:
- Description: The growth experiences of women-owned Small and Medium Enterprises (SMEs) in the context of a developing economy are examined through the lens of pragmatism. This approach views a businesswoman’s ‘belief’, ‘habit’ and ‘doubt’ as critical for researching gender related issues in entrepreneurship. This study explains the growth aspects of women-owned manufacturing and services SMEs of Bangladesh with the aim of addressing two neglected research issues. One is the scarcity of studies on growth oriented women entrepreneurs in developing countries. The other is the lack of focus on very successful high-growth women-owned firms in the context of a strong male-dominated economy. This study adopts a framework developed out of the Diana International Project to evaluate the factors influencing the growth of these successful, growing, Bangladeshi women-owned businesses. In order to evaluate the growth process itself, this framework was then modified with growth resources and actions as explained by Edith Penrose in her 1959 seminal book The Theory of Growth of the Firm. This allows for the investigation of the effects of managerial and entrepreneurial abilities in growth, and the identification of how firms achieve growth. A multiple-case design is adopted, covering sixteen successful growth-oriented firms in the manufacturing and services sector. SMEs were studied as the basis for firm growth from initial venture creation, while the sector concentration on manufacturing and services reflects the urban nature of the study in examining firms that exist in the capital city of Dhaka. Data from in-depth interviews and supporting documents were used for the case studies and integrated with the theoretical framework. Themes were categorised and patterns compared against the framework. The results of this research suggest that SME growth is a process which is gradual and iterative, comprising a series of growth strategies and approaches. The framework identifies interactive connection between different growth variables and highlights how industry sector and the national context of a growing economy facilitate growth of women-owned SMEs. The case study based research seeks to advance scholarship in relation to women’s entrepreneurship globally and contribute to the understanding of growth oriented women’s entrepreneurship. Building upon existing knowledge, this research endeavours to generate new insights and advance theoretical discourse by providing richness and subtlety to the knowledge of growth process and opening up new avenues for future research.
- Description: Doctor of Philosophy
Efficient data gathering in 3D linear underwater wireless sensor networks using sink mobility
- Akbar, Mariam, Javaid, Nadeem, Khan, Ayesha, Imran, Muhammad, Shoaib, Muhammad, Vasilakos, Athanasios
- Authors: Akbar, Mariam , Javaid, Nadeem , Khan, Ayesha , Imran, Muhammad , Shoaib, Muhammad , Vasilakos, Athanasios
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 3 (2016), p.
- Full Text:
- Reviewed:
- Description: Due to the unpleasant and unpredictable underwater environment, designing an energy-efficient routing protocol for underwater wireless sensor networks (UWSNs) demands more accuracy and extra computations. In the proposed scheme, we introduce a mobile sink (MS), i.e., an autonomous underwater vehicle (AUV), and also courier nodes (CNs), to minimize the energy consumption of nodes. MS and CNs stop at specific stops for data gathering; later on, CNs forward the received data to the MS for further transmission. By the mobility of CNs and MS, the overall energy consumption of nodes is minimized. We perform simulations to investigate the performance of the proposed scheme and compare it to preexisting techniques. Simulation results are compared in terms of network lifetime, throughput, path loss, transmission loss and packet drop ratio. The results show that the proposed technique performs better in terms of network lifetime, throughput, path loss and scalability. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
- Authors: Akbar, Mariam , Javaid, Nadeem , Khan, Ayesha , Imran, Muhammad , Shoaib, Muhammad , Vasilakos, Athanasios
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 3 (2016), p.
- Full Text:
- Reviewed:
- Description: Due to the unpleasant and unpredictable underwater environment, designing an energy-efficient routing protocol for underwater wireless sensor networks (UWSNs) demands more accuracy and extra computations. In the proposed scheme, we introduce a mobile sink (MS), i.e., an autonomous underwater vehicle (AUV), and also courier nodes (CNs), to minimize the energy consumption of nodes. MS and CNs stop at specific stops for data gathering; later on, CNs forward the received data to the MS for further transmission. By the mobility of CNs and MS, the overall energy consumption of nodes is minimized. We perform simulations to investigate the performance of the proposed scheme and compare it to preexisting techniques. Simulation results are compared in terms of network lifetime, throughput, path loss, transmission loss and packet drop ratio. The results show that the proposed technique performs better in terms of network lifetime, throughput, path loss and scalability. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
The molecular epidemiology of influenza in Cambodia
- Authors: Suttie, Annika
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: Avian influenza viruses (AIVs) represent a risk to the health of humans and animals. The prevalence of AIVs in live bird markets in Cambodia is among the highest in the world, being detected in 45.5% of tested poultry in 2015. To better understand the potential risk presented by AIVs, this thesis investigated the genetic characteristics of AIVs circulating in Cambodia between 2014 to 2018; focusing on subtypes that pose the greatest risk to human and animal health (H5, H7 and H9). Highly pathogenic (HP) H5N1 clade 2.3.2.1c viruses and low pathogenic H9N2 BJ/94-like h9-4.2.5 clade viruses were the most frequently detected subtypes, and circulate endemically in Cambodia’s domestic poultry. Co-infections were detected and facilitated the production of two novel reassortant H5N1 AIVs with single genes from H9N2 viruses. Additionally, numerous intrasubtypic reassortment events were detected for H5 and H9 AIVs. This is concerning as reassortment events can rapidly produce novel viruses of public health risk. Phylogenetic analyses showed some genes of the Cambodian H5, H7 and H9 AIVs clustered with zoonotic viruses, suggesting a common origin. There are parallels between H5N1 and H9N2 AIVs detected in Cambodia and Vietnam, likely facilitated through the illegal trade of live poultry and/or the migration of wild birds. Molecular analyses showed H9 AIVs have major markers associated with adaptation to mammals; though during the study period the only human AIV cases were the result of HP H5N1. Molecular markers of resistance to adamantine antivirals was observed in 3% of H5 and 41% of H9 AIVs; however, both subtypes remain susceptible to first line antiviral treatment, neuraminidase inhibitors. The data presented in this thesis demonstrates that circulation of Cambodian AIVs represents a risk for the emergence of novel viruses. Interventions are urgently needed to mitigate the threat posed to poultry and humans.
- Description: Doctor of Philosophy
- Authors: Suttie, Annika
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: Avian influenza viruses (AIVs) represent a risk to the health of humans and animals. The prevalence of AIVs in live bird markets in Cambodia is among the highest in the world, being detected in 45.5% of tested poultry in 2015. To better understand the potential risk presented by AIVs, this thesis investigated the genetic characteristics of AIVs circulating in Cambodia between 2014 to 2018; focusing on subtypes that pose the greatest risk to human and animal health (H5, H7 and H9). Highly pathogenic (HP) H5N1 clade 2.3.2.1c viruses and low pathogenic H9N2 BJ/94-like h9-4.2.5 clade viruses were the most frequently detected subtypes, and circulate endemically in Cambodia’s domestic poultry. Co-infections were detected and facilitated the production of two novel reassortant H5N1 AIVs with single genes from H9N2 viruses. Additionally, numerous intrasubtypic reassortment events were detected for H5 and H9 AIVs. This is concerning as reassortment events can rapidly produce novel viruses of public health risk. Phylogenetic analyses showed some genes of the Cambodian H5, H7 and H9 AIVs clustered with zoonotic viruses, suggesting a common origin. There are parallels between H5N1 and H9N2 AIVs detected in Cambodia and Vietnam, likely facilitated through the illegal trade of live poultry and/or the migration of wild birds. Molecular analyses showed H9 AIVs have major markers associated with adaptation to mammals; though during the study period the only human AIV cases were the result of HP H5N1. Molecular markers of resistance to adamantine antivirals was observed in 3% of H5 and 41% of H9 AIVs; however, both subtypes remain susceptible to first line antiviral treatment, neuraminidase inhibitors. The data presented in this thesis demonstrates that circulation of Cambodian AIVs represents a risk for the emergence of novel viruses. Interventions are urgently needed to mitigate the threat posed to poultry and humans.
- Description: Doctor of Philosophy
Conical averagedness and convergence analysis of fixed point algorithms
- Bartz, Sedi, Dao, Minh, Phan, Hung
- Authors: Bartz, Sedi , Dao, Minh , Phan, Hung
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of Global Optimization Vol. 82, no. 2 (2022), p. 351-373
- Full Text:
- Reviewed:
- Description: We study a conical extension of averaged nonexpansive operators and the role it plays in convergence analysis of fixed point algorithms. Various properties of conically averaged operators are systematically investigated, in particular, the stability under relaxations, convex combinations and compositions. We derive conical averagedness properties of resolvents of generalized monotone operators. These properties are then utilized in order to analyze the convergence of the proximal point algorithm, the forward–backward algorithm, and the adaptive Douglas–Rachford algorithm. Our study unifies, improves and casts new light on recent studies of these topics. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Authors: Bartz, Sedi , Dao, Minh , Phan, Hung
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of Global Optimization Vol. 82, no. 2 (2022), p. 351-373
- Full Text:
- Reviewed:
- Description: We study a conical extension of averaged nonexpansive operators and the role it plays in convergence analysis of fixed point algorithms. Various properties of conically averaged operators are systematically investigated, in particular, the stability under relaxations, convex combinations and compositions. We derive conical averagedness properties of resolvents of generalized monotone operators. These properties are then utilized in order to analyze the convergence of the proximal point algorithm, the forward–backward algorithm, and the adaptive Douglas–Rachford algorithm. Our study unifies, improves and casts new light on recent studies of these topics. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Magic and antimagic labeling of graphs
- Authors: Sugeng, Kiki Ariyanti
- Date: 2005
- Type: Text , Thesis , PhD
- Full Text:
- Description: "A bijection mapping that assigns natural numbers to vertices and/or edges of a graph is called a labeling. In this thesis, we consider graph labelings that have weights associated with each edge and/or vertex. If all the vertex weights (respectively, edge weights) have the same value then the labeling is called magic. If the weight is different for every vertex (respectively, every edge) then we called the labeling antimagic. In this thesis we introduce some variations of magic and antimagic labelings and discuss their properties and provide corresponding labeling schemes. There are two main parts in this thesis. One main part is on vertex labeling and the other main part is on edge labeling."
- Description: Doctor of Philosophy
- Authors: Sugeng, Kiki Ariyanti
- Date: 2005
- Type: Text , Thesis , PhD
- Full Text:
- Description: "A bijection mapping that assigns natural numbers to vertices and/or edges of a graph is called a labeling. In this thesis, we consider graph labelings that have weights associated with each edge and/or vertex. If all the vertex weights (respectively, edge weights) have the same value then the labeling is called magic. If the weight is different for every vertex (respectively, every edge) then we called the labeling antimagic. In this thesis we introduce some variations of magic and antimagic labelings and discuss their properties and provide corresponding labeling schemes. There are two main parts in this thesis. One main part is on vertex labeling and the other main part is on edge labeling."
- Description: Doctor of Philosophy