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.
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:
- 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.
- 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.
A comparative assessment of models to predict monthly rainfall in Australia
- Bagirov, Adil, Mahmood, Arshad
- Authors: Bagirov, Adil , Mahmood, Arshad
- Date: 2018
- Type: Text , Journal article
- Relation: Water Resources Management Vol. 32, no. 5 (2018), p. 1777-1794
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
- Reviewed:
- Description: Accurate rainfall prediction is a challenging task. It is especially challenging in Australia where the climate is highly variable. Australia’s climatic zones range from high rainfall tropical regions in the north to the driest desert region in the interior. The performance of prediction models may vary depending on climatic conditions. It is, therefore, important to assess and compare the performance of these models in different climatic zones. This paper examines the performance of data driven models such as the support vector machines for regression, the multiple linear regression, the k-nearest neighbors and the artificial neural networks for monthly rainfall prediction in Australia depending on climatic conditions. Rainfall data with five meteorological variables over the period of 1970–2014 from 24 geographically diverse weather stations are used for this purpose. The prediction performance of each model was evaluated by comparing observed and predicted rainfall using various measures for prediction accuracy. © 2018, Springer Science+Business Media B.V., part of Springer Nature.
Prediction of monthly rainfall in Victoria, Australia : Clusterwise linear regression approach
- Bagirov, Adil, Mahmood, Arshad, Barton, Andrew
- Authors: Bagirov, Adil , Mahmood, Arshad , Barton, Andrew
- Date: 2017
- Type: Text , Journal article
- Relation: Atmospheric Research Vol. 188, no. (2017), p. 20-29
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
- Reviewed:
- Description: This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889–2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations. © 2017 Elsevier B.V.
- Authors: Mestrom, Sanne
- Date: 2012
- Type: Text , Visual art work
- Full Text:
A new technique to measure interfacial tension of transformer oil using UV-Vis spectroscopy
- Abu Bakar, Norazhar, Abu-Siada, Ahmed, Islam, Syed, El-Naggar, Mohammed
- Authors: Abu Bakar, Norazhar , Abu-Siada, Ahmed , Islam, Syed , El-Naggar, Mohammed
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 22, no. 2 (2015), p. 1275-1282
- Full Text:
- Reviewed:
- Description: Interfacial tension (IFT) and acid numbers of insulating oil are correlated with the number of years that a transformer has been in service and are used as a signal for transformer oil reclamation. Oil sampling for IFT measurement calls for extra precautions due to its high sensitivity to various oil parameters and environmental conditions. The current used technique to measure IFT of transformer oil is relatively expensive, requires an expert to conduct the test and it takes long time since the extraction of oil sample, sending it to external laboratory and getting the results back. This paper introduces a new technique to estimate the IFT of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. UV-Vis spectral response of transformer oil can be measured instantly with relatively cheap equipment, does not need an expert person to conduct the test and has the potential to be implemented online. Results show that there is a good correlation between oil spectral response and its IFT value. Artificial neural network (ANN) approach is proposed to model this correlation.
- Authors: Abu Bakar, Norazhar , Abu-Siada, Ahmed , Islam, Syed , El-Naggar, Mohammed
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 22, no. 2 (2015), p. 1275-1282
- Full Text:
- Reviewed:
- Description: Interfacial tension (IFT) and acid numbers of insulating oil are correlated with the number of years that a transformer has been in service and are used as a signal for transformer oil reclamation. Oil sampling for IFT measurement calls for extra precautions due to its high sensitivity to various oil parameters and environmental conditions. The current used technique to measure IFT of transformer oil is relatively expensive, requires an expert to conduct the test and it takes long time since the extraction of oil sample, sending it to external laboratory and getting the results back. This paper introduces a new technique to estimate the IFT of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. UV-Vis spectral response of transformer oil can be measured instantly with relatively cheap equipment, does not need an expert person to conduct the test and has the potential to be implemented online. Results show that there is a good correlation between oil spectral response and its IFT value. Artificial neural network (ANN) approach is proposed to model this correlation.
Rainfall prediction in Australia : Clusterwise linear regression approach
- Authors: Mahmood, Arshad
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
- Description: Doctor of Philosophy
- Description: Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbors methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
- Authors: Mahmood, Arshad
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
- Description: Doctor of Philosophy
- Description: Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbors methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
A review on chemical diagnosis techniques for transformer paper insulation degradation
- Abu Bakar, Norazhar, Abu Siada, Ahmed, Islam, Syed
- Authors: Abu Bakar, Norazhar , Abu Siada, Ahmed , Islam, Syed
- Date: 2013
- Type: Text , Conference proceedings , Conference paper
- Relation: 2013 Australasian Universities Power Engineering Conference, AUPEC 2013; Hobart, Australia; 29th September-3rd October 2013 p. 1-6
- Full Text:
- Reviewed:
- Description: Energized parts within power transformer are isolated using paper insulation and are immersed in insulating oil. Hence, transformer oil and paper insulation are essential sources to detect incipient and fast developing power transformer faults. Several chemical diagnoses techniques are developed to examine the condition of paper insulation such as degree of polymerization, carbon oxides, furanic compounds and methanol. The principle and limitation of these diagnoses are discussed and compared in this paper.
- Authors: Abu Bakar, Norazhar , Abu Siada, Ahmed , Islam, Syed
- Date: 2013
- Type: Text , Conference proceedings , Conference paper
- Relation: 2013 Australasian Universities Power Engineering Conference, AUPEC 2013; Hobart, Australia; 29th September-3rd October 2013 p. 1-6
- Full Text:
- Reviewed:
- Description: Energized parts within power transformer are isolated using paper insulation and are immersed in insulating oil. Hence, transformer oil and paper insulation are essential sources to detect incipient and fast developing power transformer faults. Several chemical diagnoses techniques are developed to examine the condition of paper insulation such as degree of polymerization, carbon oxides, furanic compounds and methanol. The principle and limitation of these diagnoses are discussed and compared in this paper.
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
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
- 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%).
- 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 joint SLM and precoding based PAPR reduction scheme for 5G UFMC cellular networks
- Baig, Imran, Farooq, Umer, Hasan, Najam, Zghaibeh, Manaf, Arshad, Muhammad, Imran, Muhammad
- Authors: Baig, Imran , Farooq, Umer , Hasan, Najam , Zghaibeh, Manaf , Arshad, Muhammad , Imran, Muhammad
- Date: 2020
- Type: Text , Conference paper
- Relation: 2020 International Conference on Computing and Information Technology, ICCIT 2020, Tabuk, Saudi Arabia, 9 September to 10 September 2020, 2020 International Conference on Computing and Information Technology, ICCIT 2020
- Full Text: false
- Reviewed:
- Description: Universal Filtered Multi Carrier (UFMC) waveform has been recommended for 5th Generation (5G) cellular networks due to its robustness against synchronization errors and short-packet burst support. However, the UFMC suffers from high Peak-to-Average Power Ratio (PAPR) problem. The high PAPR degrades the efficiency of High Power Amplifier (HPA) and makes the UFMC transmitter inefficient. This paper combines Selective-Mapping (SLM) and Generalized Chirp-Like (GCL) Precoding to minimize the high PAPR of UFMC system. Simulations in MATLAB ® have been carried out to analyze the both parameters PAPR and Symbol Error Rate (SER). Computer simulation results show that the proposed SLM based GCL precoded UFMC (SLM-GCL-UFMC) scheme outperform the GCL precoded UFMC scheme, conventional UFMC scheme and conventional OFDM scheme, respectively available in the literature. © 2020 IEEE.
- Javaid, Nadeem, Karim, Obaida, Sher, Arshad, Imran, Muhammad, Yasar, Ansar, Guizani, Mohsen
- Authors: Javaid, Nadeem , Karim, Obaida , Sher, Arshad , Imran, Muhammad , Yasar, Ansar , Guizani, Mohsen
- Date: 2018
- Type: Text , Conference paper
- Relation: 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018 p. 702-706
- Full Text: false
- Reviewed:
- Description: In energy constraint networks, the utilization of limited node battery is very crucial to enhance the network lifespan. The imbalanced node battery dissipation greatly effects the performance of the network. In this paper, we propose QLearning based energy-efficient and balanced data gathering routing protocol (QL-EEBDG). The effectiveness of a forwarder node is computed based on; residual energy of the source node and group energies of the neighbour nodes. The consideration of energy parameters provides complete control on the forwarder node selection and ensures efficient energy consumptions in the network. Still, due to topology changes, void node occurs which is avoided through adjacent node technique (QL-EEBDG-ADN). This scheme finds an alternate route via neighbor nodes to provide continuous communication among the network nodes. Simulations are performed to validate the effectiveness of proposed schemes against existing scheme based on energy tax, network lifetime. © 2018 IEEE.
Fuzzy logic approach in power transformers management and decision making
- Arshad, Muhammad, Islam, Syed, Khaliq, Abdul
- Authors: Arshad, Muhammad , Islam, Syed , Khaliq, Abdul
- Date: 2014
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 21, no. 5 (2014), p. 2343-2354
- Full Text: false
- Reviewed:
- Description: The degradation of insulation systems is a complex physical process, many parameters act at the same time thus making the interpretation extremely difficult. The insulation is very much responsive in transformer serving closer to design life. Strategic maintenance and operational procedures are best formulated where the condition of existing unit has been accurately assessed. To facilitate asset management and decision making, asset's condition assessment is vital using reliable, non-intrusive diagnostics and monitoring tools together with expert system. Transformer assessment can be carried out effectively by identifying and integrating its criticalities using fuzzy logic technique. In this research, asset management and decision making model has been developed using diagnostics and data interpretation techniques based on fuzzy logic approach. Enhance reliability could be achieved by integrating real time condition monitoring, maintenance, management activities and cost effective optimization techniques. This model facilitates effectively to address criticalities and allow better planning, maintenance approach as well as to predict the remnant life of the asset within a practical accuracy.
Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor
- Riaz, Farhan, Azad, Muhammad, Arshad, Junaid, Imran, Muhammad, Hassan, Ali, Rehman, Saad
- Authors: Riaz, Farhan , Azad, Muhammad , Arshad, Junaid , Imran, Muhammad , Hassan, Ali , Rehman, Saad
- Date: 2019
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 98, no. (2019), p. 120-130
- Full Text:
- Reviewed:
- Description: Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices. © 2019 Elsevier B.V.
- Authors: Riaz, Farhan , Azad, Muhammad , Arshad, Junaid , Imran, Muhammad , Hassan, Ali , Rehman, Saad
- Date: 2019
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 98, no. (2019), p. 120-130
- Full Text:
- Reviewed:
- Description: Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices. © 2019 Elsevier B.V.
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.
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
Strongly regular points of mappings
- Abbasi, Malek, Théra, Michel
- Authors: Abbasi, Malek , Théra, Michel
- Date: 2021
- Type: Text , Journal article
- Relation: Fixed Point Theory and Algorithms for Sciences and Engineering Vol. 2021, no. 1 (Journal article 2021), p.
- Full Text:
- Reviewed:
- Description: In this paper, we use a robust lower directional derivative and provide some sufficient conditions to ensure the strong regularity of a given mapping at a certain point. Then, we discuss the Hoffman estimation and achieve some results for the estimate of the distance to the set of solutions to a system of linear equalities. The advantage of our estimate is that it allows one to calculate the coefficient of the error bound. © 2021, The Author(s).
- Authors: Abbasi, Malek , Théra, Michel
- Date: 2021
- Type: Text , Journal article
- Relation: Fixed Point Theory and Algorithms for Sciences and Engineering Vol. 2021, no. 1 (Journal article 2021), p.
- Full Text:
- Reviewed:
- Description: In this paper, we use a robust lower directional derivative and provide some sufficient conditions to ensure the strong regularity of a given mapping at a certain point. Then, we discuss the Hoffman estimation and achieve some results for the estimate of the distance to the set of solutions to a system of linear equalities. The advantage of our estimate is that it allows one to calculate the coefficient of the error bound. © 2021, The Author(s).
Compliance of smokeless tobacco supply chain actors and products with tobacco control laws in Bangladesh, India and Pakistan : protocol for a multicentre sequential mixed-methods study
- Khan, Zohaib, Huque, Rumana, Sheikh, Aziz, Readshaw, Anne, Eckhardt, Jappe, Jackson, Cath, Kanaan, Mona, Iqbal, Romaina, Akhter, Zohaib, Garg, Suneela, Singh, Mongjam, Ahmad, Fayaz, Abdullah, S.M., Javaid, Arshad, A Khan, Javaid, Han, Lu, Rahman, Muhammad Aziz, Siddiqi, Kamran
- Authors: Khan, Zohaib , Huque, Rumana , Sheikh, Aziz , Readshaw, Anne , Eckhardt, Jappe , Jackson, Cath , Kanaan, Mona , Iqbal, Romaina , Akhter, Zohaib , Garg, Suneela , Singh, Mongjam , Ahmad, Fayaz , Abdullah, S.M. , Javaid, Arshad , A Khan, Javaid , Han, Lu , Rahman, Muhammad Aziz , Siddiqi, Kamran
- Date: 2020
- Type: Text , Journal article
- Relation: BMJ Open Vol. 10, no. 6 (2020), p.
- Full Text:
- Reviewed:
- Description: Introduction South Asia is home to more than 300 million smokeless tobacco (ST) users. Bangladesh, India and Pakistan as signatories to the Framework Convention for Tobacco Control (FCTC) have developed policies aimed at curbing the use of tobacco. The objective of this study is to assess the compliance of ST point-of-sale (POS) vendors and the supply chain with the articles of the FCTC and specifically with national tobacco control laws. We also aim to assess disparities in compliance with tobacco control laws between ST and smoked tobacco products. Methods and analysis The study will be carried out at two sites each in Bangladesh, India and Pakistan. We will conduct a sequential mixed-methods study with five components: (1) mapping of ST POS, (2) analyses of ST samples packaging, (3) observation, (4) survey interviews of POS and (5) in-depth interviews with wholesale dealers/suppliers/manufacturers of ST. We aim to conduct at least 300 POS survey interviews and observations, and 6-10 in-depth interviews in each of the three countries. Data collection will be done by trained data collectors. The main statistical analysis will report the frequencies and proportions of shops that comply with the FCTC and local tobacco control policies, and provide a 95% CI of these estimates. The qualitative in-depth interview data will be analysed using the framework approach. The findings will be connected, each component informing the focus and/or design of the next component. Ethics and dissemination Ethical approvals for the study have been received from the Health Sciences Research Governance Committee at the University of York, UK. In-country approvals were taken from the National Bioethics Committee in Pakistan, the Bangladesh Medical Research Council and the Indian Medical Research Council. Our results will be disseminated via scientific conferences, peer-reviewed research publications and press releases. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.
- Description: National Institute for Health Research, NIHR [ASTRA (Grant Reference Number 17/63/76)].
- Authors: Khan, Zohaib , Huque, Rumana , Sheikh, Aziz , Readshaw, Anne , Eckhardt, Jappe , Jackson, Cath , Kanaan, Mona , Iqbal, Romaina , Akhter, Zohaib , Garg, Suneela , Singh, Mongjam , Ahmad, Fayaz , Abdullah, S.M. , Javaid, Arshad , A Khan, Javaid , Han, Lu , Rahman, Muhammad Aziz , Siddiqi, Kamran
- Date: 2020
- Type: Text , Journal article
- Relation: BMJ Open Vol. 10, no. 6 (2020), p.
- Full Text:
- Reviewed:
- Description: Introduction South Asia is home to more than 300 million smokeless tobacco (ST) users. Bangladesh, India and Pakistan as signatories to the Framework Convention for Tobacco Control (FCTC) have developed policies aimed at curbing the use of tobacco. The objective of this study is to assess the compliance of ST point-of-sale (POS) vendors and the supply chain with the articles of the FCTC and specifically with national tobacco control laws. We also aim to assess disparities in compliance with tobacco control laws between ST and smoked tobacco products. Methods and analysis The study will be carried out at two sites each in Bangladesh, India and Pakistan. We will conduct a sequential mixed-methods study with five components: (1) mapping of ST POS, (2) analyses of ST samples packaging, (3) observation, (4) survey interviews of POS and (5) in-depth interviews with wholesale dealers/suppliers/manufacturers of ST. We aim to conduct at least 300 POS survey interviews and observations, and 6-10 in-depth interviews in each of the three countries. Data collection will be done by trained data collectors. The main statistical analysis will report the frequencies and proportions of shops that comply with the FCTC and local tobacco control policies, and provide a 95% CI of these estimates. The qualitative in-depth interview data will be analysed using the framework approach. The findings will be connected, each component informing the focus and/or design of the next component. Ethics and dissemination Ethical approvals for the study have been received from the Health Sciences Research Governance Committee at the University of York, UK. In-country approvals were taken from the National Bioethics Committee in Pakistan, the Bangladesh Medical Research Council and the Indian Medical Research Council. Our results will be disseminated via scientific conferences, peer-reviewed research publications and press releases. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.
- Description: National Institute for Health Research, NIHR [ASTRA (Grant Reference Number 17/63/76)].
Macadamia nutshell biochar for nitrate removal : Effect of biochar preparation and process parameters
- Bakly, Salam, Al-Juboori, Raed, Bowtell, Les
- Authors: Bakly, Salam , Al-Juboori, Raed , Bowtell, Les
- Date: 2019
- Type: Text , Journal article
- Relation: C-Journal of Carbon Research Vol. 5, no. 3 (2019), p. 1-20
- Full Text:
- Reviewed:
- Description: Agricultural runoff is a major cause of degradation to freshwater sources. Nitrate is of particular interest, due to the abundant use of nitrogen-based fertilizers in agricultural practices globally. This study investigated the nitrate removal of biochar produced from an agricultural waste product, macadamia nutshell (MBC). Kinetic experiments and structural analyses showed that MBC pyrolsed at 900 degrees C exhibited inferior NO3- removal compared to that pyrolsed at 1000 degrees C, which was subsequently used in the column experiments. Concentrations of 5, 10 and 15 mg/L, with flowrates of 2, 5 and 10 mL/min, were examined over a 360 min treatment time. Detailed statistical analyses were applied using 2(3) factorial design. Nitrate removal was significantly affected by flowrate, concentration and their interactions. The highest nitrate removal capacity of 0.11 mg/g MBC was achieved at a NO3- concentration of 15 mg/L and flowrate of 2 mL/min. The more crystalline structure and rough texture of MBC prepared at 1000 degrees C resulted in higher NO3- removal compared to MBC prepared at 900 degrees C. The operating parameters with the highest NO3- removal were used to study the removal capacity of the column. Breakthrough and exhaustion times of the column were 25 and 330 min respectively. Approximately 92% of the column bed was saturated after exhaustion.
- Authors: Bakly, Salam , Al-Juboori, Raed , Bowtell, Les
- Date: 2019
- Type: Text , Journal article
- Relation: C-Journal of Carbon Research Vol. 5, no. 3 (2019), p. 1-20
- Full Text:
- Reviewed:
- Description: Agricultural runoff is a major cause of degradation to freshwater sources. Nitrate is of particular interest, due to the abundant use of nitrogen-based fertilizers in agricultural practices globally. This study investigated the nitrate removal of biochar produced from an agricultural waste product, macadamia nutshell (MBC). Kinetic experiments and structural analyses showed that MBC pyrolsed at 900 degrees C exhibited inferior NO3- removal compared to that pyrolsed at 1000 degrees C, which was subsequently used in the column experiments. Concentrations of 5, 10 and 15 mg/L, with flowrates of 2, 5 and 10 mL/min, were examined over a 360 min treatment time. Detailed statistical analyses were applied using 2(3) factorial design. Nitrate removal was significantly affected by flowrate, concentration and their interactions. The highest nitrate removal capacity of 0.11 mg/g MBC was achieved at a NO3- concentration of 15 mg/L and flowrate of 2 mL/min. The more crystalline structure and rough texture of MBC prepared at 1000 degrees C resulted in higher NO3- removal compared to MBC prepared at 900 degrees C. The operating parameters with the highest NO3- removal were used to study the removal capacity of the column. Breakthrough and exhaustion times of the column were 25 and 330 min respectively. Approximately 92% of the column bed was saturated after exhaustion.