An evidence theoretic approach for traffic signal intrusion detection
- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Das, Rajkumar, Newaz, Shah
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Das, Rajkumar , Newaz, Shah
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 10 (2023), p. 4646
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- Description: The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Das, Rajkumar , Newaz, Shah
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 10 (2023), p. 4646
- Full Text:
- Reviewed:
- Description: The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band
- Datta, Dristi, Paul, Manoranjan, Murshed, Manzur, Teng, Shyh, Schmidtke, Leigh
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh , Schmidtke, Leigh
- Date: 2023
- Type: Text , Journal article
- Relation: Environments Vol. 10, no. 5 (2023), p. 77
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- 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 , 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.
Exploring CBD retail performance, recovery and resilience of a smart city following COVID-19
- Fieger, Peter, Prayag, Girish, Dyason, David, Rice, John, Hall, C. Michael
- Authors: Fieger, Peter , Prayag, Girish , Dyason, David , Rice, John , Hall, C. Michael
- Date: 2023
- Type: Text , Journal article
- Relation: Sustainability Vol. 15, no. 10 (2023), p. 8300
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- Description: The city of Christchurch, New Zealand, incurred significant damage due to a series of earthquakes in 2010 and 2011. The city had, by the late 2010s, regained economic and social normalcy after a sustained period of rebuilding and economic recovery. Through the concerted rebuilding effort, a modern central business district (CBD) with redesigned infrastructure and amenities was developed. The Christchurch rebuild was underpinned by a commitment of urban planners to an open and connected city, including the use of innovative technologies to gather, use and share data. As was the case elsewhere, the COVID-19 pandemic brought about significant disruptions to social and economic life in Christchurch. Border closures, lockdowns, trading limitations and other restrictions on movement led to changes in traditional consumer behaviors and affected the retail sector’s resilience. In this study, we used CBD pedestrian traffic data gathered from various locations to predict changes in retail spending and identify recovery implications through the lens of retail resilience. We found that the COVID-19 pandemic and its related lockdowns have driven a substantive change in the behavioral patterns of city users. The implications for resilient retail, sustainable policy and further research are explored.
- Authors: Fieger, Peter , Prayag, Girish , Dyason, David , Rice, John , Hall, C. Michael
- Date: 2023
- Type: Text , Journal article
- Relation: Sustainability Vol. 15, no. 10 (2023), p. 8300
- Full Text:
- Reviewed:
- Description: The city of Christchurch, New Zealand, incurred significant damage due to a series of earthquakes in 2010 and 2011. The city had, by the late 2010s, regained economic and social normalcy after a sustained period of rebuilding and economic recovery. Through the concerted rebuilding effort, a modern central business district (CBD) with redesigned infrastructure and amenities was developed. The Christchurch rebuild was underpinned by a commitment of urban planners to an open and connected city, including the use of innovative technologies to gather, use and share data. As was the case elsewhere, the COVID-19 pandemic brought about significant disruptions to social and economic life in Christchurch. Border closures, lockdowns, trading limitations and other restrictions on movement led to changes in traditional consumer behaviors and affected the retail sector’s resilience. In this study, we used CBD pedestrian traffic data gathered from various locations to predict changes in retail spending and identify recovery implications through the lens of retail resilience. We found that the COVID-19 pandemic and its related lockdowns have driven a substantive change in the behavioral patterns of city users. The implications for resilient retail, sustainable policy and further research are explored.
Impact of traditional and embedded image denoising on CNN-based deep learning
- Kaur, Roopdeep, Karmakar, Gour, Imran, Muhammad
- Authors: Kaur, Roopdeep , Karmakar, Gour , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: Applied sciences Vol. 13, no. 20 (2023), p.
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- Description: In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.
- Authors: Kaur, Roopdeep , Karmakar, Gour , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: Applied sciences Vol. 13, no. 20 (2023), p.
- Full Text:
- Reviewed:
- Description: In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.
IoT-based emergency vehicle services in intelligent transportation system
- Chowdhury, Abdullahi, Kaisar, Shahriar, Khoda, Mahbub, Naha, Ranesh, Khoshkholghi, Mohammad, Aiash, Mahdi
- Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Khoda, Mahbub , Naha, Ranesh , Khoshkholghi, Mohammad , Aiash, Mahdi
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 11 (2023), p. 5324
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- Description: Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.
- Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Khoda, Mahbub , Naha, Ranesh , Khoshkholghi, Mohammad , Aiash, Mahdi
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 11 (2023), p. 5324
- Full Text:
- Reviewed:
- Description: Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.
Machine learning-based agoraphilic navigation algorithm for use in dynamic environments with a moving goal
- Hewawasam, Hasitha, Kahandawa, Gayan, Ibrahim, Yousef
- Authors: Hewawasam, Hasitha , Kahandawa, Gayan , Ibrahim, Yousef
- Date: 2023
- Type: Text , Journal article
- Relation: Machines Vol. 11, no. 5 (2023), p. 513
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- Description: This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.
- Authors: Hewawasam, Hasitha , Kahandawa, Gayan , Ibrahim, Yousef
- Date: 2023
- Type: Text , Journal article
- Relation: Machines Vol. 11, no. 5 (2023), p. 513
- Full Text:
- Reviewed:
- Description: This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.
Obfuscated memory malware detection in resource-constrained iot devices for smart city applications
- Shafin, Sakib, Karmakar, Gour, Mareels, Iven
- Authors: Shafin, Sakib , Karmakar, Gour , Mareels, Iven
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 11 (2023), p. 5348
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- Description: Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware.
- Authors: Shafin, Sakib , Karmakar, Gour , Mareels, Iven
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 11 (2023), p. 5348
- Full Text:
- Reviewed:
- Description: Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware.
A global review of the woody invasive alien species mimosa pigra (giant sensitive plant): Its biology and management implications
- Welgama, Amali, Florentine, Singarayer, Roberts, Jason
- Authors: Welgama, Amali , Florentine, Singarayer , Roberts, Jason
- Date: 2022
- Type: Text , Journal article
- Relation: Plants Vol. 11, no. 18 (2022), p. 2366
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- Description: Populations of invasive alien plants create disruptive plant communities that are extremely adaptable, imposing severe ecological impacts on agriculture, biodiversity and human activities. To minimise these impacts, prevention and effective weed management strategies are urgently required, including the identification of satellite populations before they invade new areas. This is a critical element that allows weed management practices to become both successful and cost-effective. Mimosa pigra L. (Giant sensitive plant) is an invasive weed that has spread across various environments around the world and is considered one of the world’s top 100 most invasive plant species. Being adaptable to a wide range of soil types, in addition to its woody protective prickles and low palatability, M. pigra has quickly spread and established itself in a range of habitats. Current control methods of this species include biological, chemical and physical methods, together with attempts of integrated application. Reports suggest that integrated management appears to be the most effective means of controlling M. pigra since the use of any single method has not yet proved suitable. In this regard, this review synthesises and explores the available global literature and current research gaps relating to the biology, distribution, impacts and management of M. pigra. The contribution of this work will help guide land managers to design appropriate and sustainable management programs to control M. pigra.
- Authors: Welgama, Amali , Florentine, Singarayer , Roberts, Jason
- Date: 2022
- Type: Text , Journal article
- Relation: Plants Vol. 11, no. 18 (2022), p. 2366
- Full Text:
- Reviewed:
- Description: Populations of invasive alien plants create disruptive plant communities that are extremely adaptable, imposing severe ecological impacts on agriculture, biodiversity and human activities. To minimise these impacts, prevention and effective weed management strategies are urgently required, including the identification of satellite populations before they invade new areas. This is a critical element that allows weed management practices to become both successful and cost-effective. Mimosa pigra L. (Giant sensitive plant) is an invasive weed that has spread across various environments around the world and is considered one of the world’s top 100 most invasive plant species. Being adaptable to a wide range of soil types, in addition to its woody protective prickles and low palatability, M. pigra has quickly spread and established itself in a range of habitats. Current control methods of this species include biological, chemical and physical methods, together with attempts of integrated application. Reports suggest that integrated management appears to be the most effective means of controlling M. pigra since the use of any single method has not yet proved suitable. In this regard, this review synthesises and explores the available global literature and current research gaps relating to the biology, distribution, impacts and management of M. pigra. The contribution of this work will help guide land managers to design appropriate and sustainable management programs to control M. pigra.
Determinants of the intention to adopt digital-only banks in Malaysia: The extension of environmental concern
- Saif, Mashaal A. M., Hussin, Nazimah, Husin, Maizaitulaidawati Md, Alwadain, Ayed, Chakraborty, Ayon
- Authors: Saif, Mashaal A. M. , Hussin, Nazimah , Husin, Maizaitulaidawati Md , Alwadain, Ayed , Chakraborty, Ayon
- Date: 2022
- Type: Text , Journal article
- Relation: Sustainability (Basel, Switzerland) Vol. 14, no. 17 (2022), p. 11043
- Full Text:
- Reviewed:
- Description: Digital-only banks have not achieved adoption expectations despite being one of the latest innovations in fintech. Several digital-only banks in the United States and Japan have gone bankrupt, and others continue to operate at a loss. Therefore, it is imperative to conduct this study in Malaysia to understand customers’ behavior, particularly regarding the adoption of digital-only banks. With climate change, environmental-friendly behavior, which has been ignored in digital-only bank literature, is becoming increasingly pertinent. This study addresses the lack of an integrated model that investigates the effect of external factors (i.e., critical mass, number of services, and environmental concerns), customer self-determination factors (i.e., trust), and mental perceptions of technology adoption (i.e., convenience, economic efficiency, functional and security risks, as well as perceived value) on the intention to adopt digital-only banks. Data were collected through an online survey targeting Klang Valley residents in the prime age range of 25–54 years old using stratified random sampling. The data was analyzed using structural equation modeling by performing confirmatory factor analysis (CFA) and SEM path analysis in AMOS.v26 software. The results show that convenience, economic efficiency, number of services, trust, perceived value, and environmental concern all have positive significant relationships with the intention to adopt digital-only banks. Further, environmental concern is the strongest indicator of behavioral intention. In contrast, functional and security risks have a negative but non-significant relationship with the intention to adopt digital-only banks. Finally, critical mass has a positive but non-significant effect on the behavioral intention. This study is among the first to examine the influence of environmental concern on behavioral intentions in a digital-only banking context. It also contributes to an expanding body of research investigating environmental sustainability by presenting empirical results in the context of digital-only banks.
- Authors: Saif, Mashaal A. M. , Hussin, Nazimah , Husin, Maizaitulaidawati Md , Alwadain, Ayed , Chakraborty, Ayon
- Date: 2022
- Type: Text , Journal article
- Relation: Sustainability (Basel, Switzerland) Vol. 14, no. 17 (2022), p. 11043
- Full Text:
- Reviewed:
- Description: Digital-only banks have not achieved adoption expectations despite being one of the latest innovations in fintech. Several digital-only banks in the United States and Japan have gone bankrupt, and others continue to operate at a loss. Therefore, it is imperative to conduct this study in Malaysia to understand customers’ behavior, particularly regarding the adoption of digital-only banks. With climate change, environmental-friendly behavior, which has been ignored in digital-only bank literature, is becoming increasingly pertinent. This study addresses the lack of an integrated model that investigates the effect of external factors (i.e., critical mass, number of services, and environmental concerns), customer self-determination factors (i.e., trust), and mental perceptions of technology adoption (i.e., convenience, economic efficiency, functional and security risks, as well as perceived value) on the intention to adopt digital-only banks. Data were collected through an online survey targeting Klang Valley residents in the prime age range of 25–54 years old using stratified random sampling. The data was analyzed using structural equation modeling by performing confirmatory factor analysis (CFA) and SEM path analysis in AMOS.v26 software. The results show that convenience, economic efficiency, number of services, trust, perceived value, and environmental concern all have positive significant relationships with the intention to adopt digital-only banks. Further, environmental concern is the strongest indicator of behavioral intention. In contrast, functional and security risks have a negative but non-significant relationship with the intention to adopt digital-only banks. Finally, critical mass has a positive but non-significant effect on the behavioral intention. This study is among the first to examine the influence of environmental concern on behavioral intentions in a digital-only banking context. It also contributes to an expanding body of research investigating environmental sustainability by presenting empirical results in the context of digital-only banks.
Does destination, relationship type, or migration status of the host impact vfr travel?
- Zentveld, Elisa, Yousuf, Mohammad
- Authors: Zentveld, Elisa , Yousuf, Mohammad
- Date: 2022
- Type: Text , Journal article
- Relation: Tourism and hospitality (Basel) Vol. 3, no. 3 (2022), p. 589-605
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- Reviewed:
- Description: Visiting friends and relatives (VFR) travel hosts play a key role in influencing the trip characteristics of their VFR travel parties and the decisions and activities within those travel parties. However, how those trips are shaped in terms of travel decisions and activities is not well understood. This is the first quantitative study examining the hosting of VFRs by examining how migration, relationship type (VF versus VR), and destination type impact the characteristics and activities of VFR travel parties. The objective was to examine the extent of influence of different characteristics of VFR hosts on individual VFR travel decisions and activities. Estimation models were developed and tested through regression analysis to examine the impact that the characteristics of hosts have on decisions and activities within VFR travel. Such findings have provided a systematic framework for examining the multifaceted role of VFR hosts. The generalisability of the estimation models developed and tested in this study can be replicated and adapted in future studies.
- Authors: Zentveld, Elisa , Yousuf, Mohammad
- Date: 2022
- Type: Text , Journal article
- Relation: Tourism and hospitality (Basel) Vol. 3, no. 3 (2022), p. 589-605
- Full Text:
- Reviewed:
- Description: Visiting friends and relatives (VFR) travel hosts play a key role in influencing the trip characteristics of their VFR travel parties and the decisions and activities within those travel parties. However, how those trips are shaped in terms of travel decisions and activities is not well understood. This is the first quantitative study examining the hosting of VFRs by examining how migration, relationship type (VF versus VR), and destination type impact the characteristics and activities of VFR travel parties. The objective was to examine the extent of influence of different characteristics of VFR hosts on individual VFR travel decisions and activities. Estimation models were developed and tested through regression analysis to examine the impact that the characteristics of hosts have on decisions and activities within VFR travel. Such findings have provided a systematic framework for examining the multifaceted role of VFR hosts. The generalisability of the estimation models developed and tested in this study can be replicated and adapted in future studies.
Opportunities and barriers for FinTech in SAARC and ASEAN Countries
- Imam, Tasadduq, McInnes, Angelique, Colombage, Sisira, Grose, Robert
- Authors: Imam, Tasadduq , McInnes, Angelique , Colombage, Sisira , Grose, Robert
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of risk and financial management Vol. 15, no. 2 (2022), p. 77
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- Description: This article assesses the opportunities and challenges for different categories of FinTechs in the SAARC and ASEAN regions. We consider the global financial inclusion data released by the World Bank and map the responses to gain insights into the opportunities and challenges for FinTechs in the respective regions. We develop a new index, termed the FinTech Opportunity Index (FOI), to conceptualise the opportunities and barriers based on individual savings, borrowings, purchasing behaviour, and payment preferences. We note that FinTech services have potential opportunities for expansion in the ASEAN regions but less so in the SAARC regions. The need for different types of FinTech services varies between regions. Services such as crowdfunding, neobanks, and InsurTech have potential in the ASEAN regions, especially with the positive attitude towards entrepreneurship and asset investments. In the SAARC regions, InsurTechs linked to health care has potential along with LendTechs and neobanks. We further note that males, and the young are more likely adopters of FinTechs in both regions. The analysis suggests the need for innovative promotions and education to motivate the more sceptical, especially women and the elderly population, to adopt FinTech services.
- Authors: Imam, Tasadduq , McInnes, Angelique , Colombage, Sisira , Grose, Robert
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of risk and financial management Vol. 15, no. 2 (2022), p. 77
- Full Text:
- Reviewed:
- Description: This article assesses the opportunities and challenges for different categories of FinTechs in the SAARC and ASEAN regions. We consider the global financial inclusion data released by the World Bank and map the responses to gain insights into the opportunities and challenges for FinTechs in the respective regions. We develop a new index, termed the FinTech Opportunity Index (FOI), to conceptualise the opportunities and barriers based on individual savings, borrowings, purchasing behaviour, and payment preferences. We note that FinTech services have potential opportunities for expansion in the ASEAN regions but less so in the SAARC regions. The need for different types of FinTech services varies between regions. Services such as crowdfunding, neobanks, and InsurTech have potential in the ASEAN regions, especially with the positive attitude towards entrepreneurship and asset investments. In the SAARC regions, InsurTechs linked to health care has potential along with LendTechs and neobanks. We further note that males, and the young are more likely adopters of FinTechs in both regions. The analysis suggests the need for innovative promotions and education to motivate the more sceptical, especially women and the elderly population, to adopt FinTech services.
Reclamation of salt-affected land: A review
- Shaygan, Mandana, Baumgartl, Thomas
- Authors: Shaygan, Mandana , Baumgartl, Thomas
- Date: 2022
- Type: Text , Journal article
- Relation: Soil systems Vol. 6, no. 3 (2022), p. 61
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- Description: Reclamation of salt-affected soil has been identified by the FAO as being critical to meet the needs to increase agricultural productivity. This paper reviews commonly used reclamation methods for salt-affected soils, and provides critical identifiers for an effective reclamation practice of salt-affected soil. There are widely used methods to reduce salinity and sodicity of salt-affected soils, including salt leaching, addition of amendments, revegetation using halophytes and salt scrapping. Not all reclamation techniques are suitable for salt-affected land. The reclamation strategy must be tailored to the site, and based on understanding the soil, plant and climate interactions. On some occasions, a combination of techniques may be required for reclamation. This can include salt scrapping to remove salts from the surface soil, the addition of physical amendments to improve soil pore systems and enhance salt leaching, followed by amelioration of soil by chemical amendments to preserve soil physical conditions, and then halophyte establishment to expand the desalinization zone. This study reveals that soil hydro-geochemical models are effective predictive tools to ascertain the best reclamation practice tailored to salt-affected land. However, models need to be calibrated and validated to the conditions of the land before being applied as a tool to combat soil salinity.
- Authors: Shaygan, Mandana , Baumgartl, Thomas
- Date: 2022
- Type: Text , Journal article
- Relation: Soil systems Vol. 6, no. 3 (2022), p. 61
- Full Text:
- Reviewed:
- Description: Reclamation of salt-affected soil has been identified by the FAO as being critical to meet the needs to increase agricultural productivity. This paper reviews commonly used reclamation methods for salt-affected soils, and provides critical identifiers for an effective reclamation practice of salt-affected soil. There are widely used methods to reduce salinity and sodicity of salt-affected soils, including salt leaching, addition of amendments, revegetation using halophytes and salt scrapping. Not all reclamation techniques are suitable for salt-affected land. The reclamation strategy must be tailored to the site, and based on understanding the soil, plant and climate interactions. On some occasions, a combination of techniques may be required for reclamation. This can include salt scrapping to remove salts from the surface soil, the addition of physical amendments to improve soil pore systems and enhance salt leaching, followed by amelioration of soil by chemical amendments to preserve soil physical conditions, and then halophyte establishment to expand the desalinization zone. This study reveals that soil hydro-geochemical models are effective predictive tools to ascertain the best reclamation practice tailored to salt-affected land. However, models need to be calibrated and validated to the conditions of the land before being applied as a tool to combat soil salinity.
Subgraph adaptive structure-aware graph contrastive learning
- Chen, Zhikui, Peng, Yin, Yu, Shuo, Cao, Chen, Xia, Feng
- Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
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- Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
- Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
- Full Text:
- Reviewed:
- Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
A comparative study on the role of polyvinylpyrrolidone molecular weight on the functionalization of various carbon nanotubes and their composites
- Namasivayam, Muthuraman, Andersson, Mats, Shapter, Joseph
- Authors: Namasivayam, Muthuraman , Andersson, Mats , Shapter, Joseph
- Date: 2021
- Type: Text , Journal article
- Relation: Polymers Vol. 13, no. 15 (2021), p.
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- Description: Polyvinylidene fluoride (PVDF) nanocomposites filled with polyvinylpyrrolidone (PVP) wrapped carbon nanotubes were prepared via a solution casting technique. The effect of the molecular weight (polymer chain length) of the PVP on the ability to wrap different nanotube structures and its impact towards nanotube dispersibility in the polymer matrix was explored. The study was conducted with PVP of four different molecular weights and nanotubes of three different structures. The composites that exhibit an effective nanotube dispersion lead to a nanotube network that facilitates improved thermal, electrical, and mechanical properties. It was observed that nanotubes of different structures exhibit stable dispersions in the polymer matrix though PVP functionalization of different molecular weights, but the key is achieving an effective nanotube dispersion at low PVP concentrations. This is observed in MWNT and AP-SWNT based composites with PVP of low molecular weight, leading to a thermal conductivity enhancement of 147% and 53%, respectively, while for P3-SWNT based composites, PVP of high molecular weight yields an enhancement of 25% in thermal conductivity compared to the non-functionalized CNT-PVDF composite. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Namasivayam, Muthuraman , Andersson, Mats , Shapter, Joseph
- Date: 2021
- Type: Text , Journal article
- Relation: Polymers Vol. 13, no. 15 (2021), p.
- Full Text:
- Reviewed:
- Description: Polyvinylidene fluoride (PVDF) nanocomposites filled with polyvinylpyrrolidone (PVP) wrapped carbon nanotubes were prepared via a solution casting technique. The effect of the molecular weight (polymer chain length) of the PVP on the ability to wrap different nanotube structures and its impact towards nanotube dispersibility in the polymer matrix was explored. The study was conducted with PVP of four different molecular weights and nanotubes of three different structures. The composites that exhibit an effective nanotube dispersion lead to a nanotube network that facilitates improved thermal, electrical, and mechanical properties. It was observed that nanotubes of different structures exhibit stable dispersions in the polymer matrix though PVP functionalization of different molecular weights, but the key is achieving an effective nanotube dispersion at low PVP concentrations. This is observed in MWNT and AP-SWNT based composites with PVP of low molecular weight, leading to a thermal conductivity enhancement of 147% and 53%, respectively, while for P3-SWNT based composites, PVP of high molecular weight yields an enhancement of 25% in thermal conductivity compared to the non-functionalized CNT-PVDF composite. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
A Method for the design of concrete with combined steel and basalt fiber
- Dvorkin, Leonid, Bordiuzhenko, Oleh, Tekle, Biruk, Ribakov, Yuri
- Authors: Dvorkin, Leonid , Bordiuzhenko, Oleh , Tekle, Biruk , Ribakov, Yuri
- Date: 2021
- Type: Text , Journal article
- Relation: Applied sciences Vol. 11, no. 19 (2021), p. 8850
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- Description: Combining different fiber types may improve the mechanical properties of fiber reinforced concrete. The present study is focused on investigating hybrid fiber reinforced concrete (HFRC) with steel and basalt fiber. Mechanical properties of fiber reinforced fine-grained concrete are investigated. The results demonstrate that using optimal steel and basalt fiber reinforcement ratios avoids concrete mixtures’ segregation and improves their homogeneity. Concrete with hybrid steel and basalt fiber reinforcement has higher strength. Effective methodology for proper design of HFRC compositions was proposed. It is based on the mathematical experiments planning method. The proposed method enables optimal mix proportioning of high-strength fine-grained concrete with hybrid steel and basalt fiber reinforcement.
- Authors: Dvorkin, Leonid , Bordiuzhenko, Oleh , Tekle, Biruk , Ribakov, Yuri
- Date: 2021
- Type: Text , Journal article
- Relation: Applied sciences Vol. 11, no. 19 (2021), p. 8850
- Full Text:
- Reviewed:
- Description: Combining different fiber types may improve the mechanical properties of fiber reinforced concrete. The present study is focused on investigating hybrid fiber reinforced concrete (HFRC) with steel and basalt fiber. Mechanical properties of fiber reinforced fine-grained concrete are investigated. The results demonstrate that using optimal steel and basalt fiber reinforcement ratios avoids concrete mixtures’ segregation and improves their homogeneity. Concrete with hybrid steel and basalt fiber reinforcement has higher strength. Effective methodology for proper design of HFRC compositions was proposed. It is based on the mathematical experiments planning method. The proposed method enables optimal mix proportioning of high-strength fine-grained concrete with hybrid steel and basalt fiber reinforcement.
A preliminary investigation of the effect of ethical labeling and moral self-image on the expected and perceived flavor and aroma of beer
- Doorn, George, Ferguson, Rose, Watson, Shaun, Timora, Justin, Berends, Dylan, Moore, Chris
- Authors: Doorn, George , Ferguson, Rose , Watson, Shaun , Timora, Justin , Berends, Dylan , Moore, Chris
- Date: 2021
- Type: Text , Journal article
- Relation: Beverages Vol. 7, no. 2 (2021), p.
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- Description: Ethical labelling has been shown to influence taste/flavour perception. Across two experiments, the present study examined how ethical labelling and moral self-image influenced both the expected (Experiment One) and perceived (Experiment Two) taste/flavour characteristics of beer. In Experiment One, 170 participants read either a ‘moral’ or ‘control’ label describing a brewery, after which they were presented with an image of a beer. Participants then completed a Beer Taste Perception Questionnaire and the Moral Self-Image Scale. In Experiment Two, 59 participants were exposed to either the moral or control label before tasting a beer and completing the same questionnaires from Experiment One. The results of Experiment One indicated that label type moderated the relationship between moral self-image and the intensity ratings of the beer. Specifically, in the presence of a control label, the expected intensity of the beer’s flavour increased as moral self-image increased. Experiment Two found no evidence that the moral label influenced the perceived taste of the beer. However, the results showed that as moral self-image became more positive the perceived refreshingness of the beer increased. This study provides novel evidence of the potential relationship between an individual’s moral self-image and the expected and perceived taste/flavour characteristics of beer. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Doorn, George , Ferguson, Rose , Watson, Shaun , Timora, Justin , Berends, Dylan , Moore, Chris
- Date: 2021
- Type: Text , Journal article
- Relation: Beverages Vol. 7, no. 2 (2021), p.
- Full Text:
- Reviewed:
- Description: Ethical labelling has been shown to influence taste/flavour perception. Across two experiments, the present study examined how ethical labelling and moral self-image influenced both the expected (Experiment One) and perceived (Experiment Two) taste/flavour characteristics of beer. In Experiment One, 170 participants read either a ‘moral’ or ‘control’ label describing a brewery, after which they were presented with an image of a beer. Participants then completed a Beer Taste Perception Questionnaire and the Moral Self-Image Scale. In Experiment Two, 59 participants were exposed to either the moral or control label before tasting a beer and completing the same questionnaires from Experiment One. The results of Experiment One indicated that label type moderated the relationship between moral self-image and the intensity ratings of the beer. Specifically, in the presence of a control label, the expected intensity of the beer’s flavour increased as moral self-image increased. Experiment Two found no evidence that the moral label influenced the perceived taste of the beer. However, the results showed that as moral self-image became more positive the perceived refreshingness of the beer increased. This study provides novel evidence of the potential relationship between an individual’s moral self-image and the expected and perceived taste/flavour characteristics of beer. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
A study on the corrosion characteristics of internal combustion engine materials in second-generation jatropha curcas biodiesel
- Shahabuddin, M., Mofijur, M., Shuvho, Md Bengir, Chowdhury, M., Kalam, Md Abul, Masjuki, Haji, Chowdhury, Mohammad
- Authors: Shahabuddin, M. , Mofijur, M. , Shuvho, Md Bengir , Chowdhury, M. , Kalam, Md Abul , Masjuki, Haji , Chowdhury, Mohammad
- Date: 2021
- Type: Text , Journal article
- Relation: Energies Vol. 14, no. 14 (2021), p.
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- Description: The corrosiveness of biodiesel affects the fuel processing infrastructure and different parts of an internal combustion (IC) engine. The present study investigates the corrosion behaviour of automotive materials such as stainless steel, aluminium, cast iron, and copper in 20% (B20) and 30% (B30) by volume second-generation Jatropha biodiesel using an immersion test. The results were compared with petro-diesel (B0). Various fuel properties such as the viscosity, density, water con-tent, total acid number (TAN), and oxidation stability were investigated after the immersion test using ASTM D341, ASTM D975, ASTM D445, and ASTM D6751 standards. The morphology of the corroded materials was investigated using optical microscopy and scanning electron microscopy SEM), whereas the elemental analysis was carried out using energy-dispersive X-ray spectroscopy (EDS). The highest corrosion using biodiesel was detected in copper, while the lowest was detected in stainless steel. Using B20, the rate of corrosion in copper and stainless steel was 17% and 14% higher than when using diesel, which further increased to 206% and 86% using B30. After the immersion test, the viscosity, water content, and TAN of biodiesel were increased markedly compared to petro-diesel. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Shahabuddin, M. , Mofijur, M. , Shuvho, Md Bengir , Chowdhury, M. , Kalam, Md Abul , Masjuki, Haji , Chowdhury, Mohammad
- Date: 2021
- Type: Text , Journal article
- Relation: Energies Vol. 14, no. 14 (2021), p.
- Full Text:
- Reviewed:
- Description: The corrosiveness of biodiesel affects the fuel processing infrastructure and different parts of an internal combustion (IC) engine. The present study investigates the corrosion behaviour of automotive materials such as stainless steel, aluminium, cast iron, and copper in 20% (B20) and 30% (B30) by volume second-generation Jatropha biodiesel using an immersion test. The results were compared with petro-diesel (B0). Various fuel properties such as the viscosity, density, water con-tent, total acid number (TAN), and oxidation stability were investigated after the immersion test using ASTM D341, ASTM D975, ASTM D445, and ASTM D6751 standards. The morphology of the corroded materials was investigated using optical microscopy and scanning electron microscopy SEM), whereas the elemental analysis was carried out using energy-dispersive X-ray spectroscopy (EDS). The highest corrosion using biodiesel was detected in copper, while the lowest was detected in stainless steel. Using B20, the rate of corrosion in copper and stainless steel was 17% and 14% higher than when using diesel, which further increased to 206% and 86% using B30. After the immersion test, the viscosity, water content, and TAN of biodiesel were increased markedly compared to petro-diesel. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Addition of activated carbon into a cattle diet to mitigate GHG emissions and improve production
- Al-Azzawi, Mohammed, Bowtell, Les, Hancock, Kerry, Preston, Sarah
- Authors: Al-Azzawi, Mohammed , Bowtell, Les , Hancock, Kerry , Preston, Sarah
- Date: 2021
- Type: Text , Journal article
- Relation: Sustainability (Switzerland) Vol. 13, no. 15 (2021), p.
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- Description: Globally, the most problematic greenhouse gas (GHG) emissions of ruminant livestock is methane (CH4), with a global warming potential 25 times that of carbon dioxide. This work considers the emissions and production effects of powdered activated carbon (PAC) at 0.5% by dry matter (DM) on methanogenic rumen flora as the major source of dairy cattle enteric methane emissions. In total, 180 dairy cattle located in Brymaroo, Queensland (QLD), Australia, were studied in a three-cycle repeated measures ANOVA format with a 4 week primary interval. Emissions eructated during milking and in faecal deposits were measured, and in addition, 16S rRNA gene sequencing was performed to determine the collective populations of prokaryotic bacteria and archaea as well methanogenic communities for each treatment. Moreover, 0.5% PAC addition reduced CH4 emissions by 30-40% and CO2 emissions by 10%, while improving daily milk production by 3.43%, milk protein by 2.63% and milk fat by 6.32%, on average for the herd (p < 0.001 in all cases). rRNA gene sequencing showed populations of methanogenic flora decreased by 30% on average with a corresponding increase in the nonmethanogenic species. We strongly advocate further on-farm trials with the dietary addition of PAC in ruminant diets to mitigate emissions while maintaining or improving productivity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Al-Azzawi, Mohammed , Bowtell, Les , Hancock, Kerry , Preston, Sarah
- Date: 2021
- Type: Text , Journal article
- Relation: Sustainability (Switzerland) Vol. 13, no. 15 (2021), p.
- Full Text:
- Reviewed:
- Description: Globally, the most problematic greenhouse gas (GHG) emissions of ruminant livestock is methane (CH4), with a global warming potential 25 times that of carbon dioxide. This work considers the emissions and production effects of powdered activated carbon (PAC) at 0.5% by dry matter (DM) on methanogenic rumen flora as the major source of dairy cattle enteric methane emissions. In total, 180 dairy cattle located in Brymaroo, Queensland (QLD), Australia, were studied in a three-cycle repeated measures ANOVA format with a 4 week primary interval. Emissions eructated during milking and in faecal deposits were measured, and in addition, 16S rRNA gene sequencing was performed to determine the collective populations of prokaryotic bacteria and archaea as well methanogenic communities for each treatment. Moreover, 0.5% PAC addition reduced CH4 emissions by 30-40% and CO2 emissions by 10%, while improving daily milk production by 3.43%, milk protein by 2.63% and milk fat by 6.32%, on average for the herd (p < 0.001 in all cases). rRNA gene sequencing showed populations of methanogenic flora decreased by 30% on average with a corresponding increase in the nonmethanogenic species. We strongly advocate further on-farm trials with the dietary addition of PAC in ruminant diets to mitigate emissions while maintaining or improving productivity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Advances in the theory of compact groups and pro-lie groups in the last quarter century
- Hofmann, Karl, Morris, Sidney
- Authors: Hofmann, Karl , Morris, Sidney
- Date: 2021
- Type: Text , Journal article , Review
- Relation: Axioms Vol. 10, no. 3 (2021), p.
- Full Text:
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- Description: This article surveys the development of the theory of compact groups and pro-Lie groups, contextualizing the major achievements over 125 years and focusing on some progress in the last quarter century. It begins with developments in the 18th and 19th centuries. Next is from Hilbert’s Fifth Problem in 1900 to its solution in 1952 by Montgomery, Zippin, and Gleason and Yamabe’s important structure theorem on almost connected locally compact groups. This half century included profound contributions by Weyl and Peter, Haar, Pontryagin, van Kampen, Weil, and Iwasawa. The focus in the last quarter century has been structure theory, largely resulting from extending Lie Theory to compact groups and then to pro-Lie groups, which are projective limits of finite-dimensional Lie groups. The category of pro-Lie groups is the smallest complete category containing Lie groups and includes all compact groups, locally compact abelian groups, and connected locally compact groups. Amongst the structure theorems is that each almost connected pro-Lie group G is homeomorphic to RI × C for a suitable set I and some compact subgroup C. Finally, there is a perfect generalization to compact groups G of the age-old natural duality of the group algebra R[G] of a finite group G to its representation algebra R(G, R), via the natural duality of the topological vector space RI to the vector space R(I), for any set I, thus opening a new approach to the Hochschild-Tannaka duality of compact groups. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Hofmann, Karl , Morris, Sidney
- Date: 2021
- Type: Text , Journal article , Review
- Relation: Axioms Vol. 10, no. 3 (2021), p.
- Full Text:
- Reviewed:
- Description: This article surveys the development of the theory of compact groups and pro-Lie groups, contextualizing the major achievements over 125 years and focusing on some progress in the last quarter century. It begins with developments in the 18th and 19th centuries. Next is from Hilbert’s Fifth Problem in 1900 to its solution in 1952 by Montgomery, Zippin, and Gleason and Yamabe’s important structure theorem on almost connected locally compact groups. This half century included profound contributions by Weyl and Peter, Haar, Pontryagin, van Kampen, Weil, and Iwasawa. The focus in the last quarter century has been structure theory, largely resulting from extending Lie Theory to compact groups and then to pro-Lie groups, which are projective limits of finite-dimensional Lie groups. The category of pro-Lie groups is the smallest complete category containing Lie groups and includes all compact groups, locally compact abelian groups, and connected locally compact groups. Amongst the structure theorems is that each almost connected pro-Lie group G is homeomorphic to RI × C for a suitable set I and some compact subgroup C. Finally, there is a perfect generalization to compact groups G of the age-old natural duality of the group algebra R[G] of a finite group G to its representation algebra R(G, R), via the natural duality of the topological vector space RI to the vector space R(I), for any set I, thus opening a new approach to the Hochschild-Tannaka duality of compact groups. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Amaranthus retroflexus L (redroot pigweed) : effects of elevated CO2 and soil moisture on growth and biomass and the effect of radiant heat on seed germination
- Weller, Sandra, Florentine, Singarayer, Welgama, Amali, Chadha, Aakansha, Turville, Christopher
- Authors: Weller, Sandra , Florentine, Singarayer , Welgama, Amali , Chadha, Aakansha , Turville, Christopher
- Date: 2021
- Type: Text , Journal article
- Relation: Agronomy Vol. 11, no. 4 (2021), p.
- Full Text:
- Reviewed:
- Description: Amaranthus retroflexus L. (Amaranthaceae), Redroot pigweed, is native to North America, but has become a weed of agriculture worldwide. Previous research into competition with food crops found it significantly reduces yields. Additionally, taxonomy, biomass allocation, physiological responses to light intensity, water stress, elevated CO2, and herbicide resistance have been inves-tigated. To extend other research findings, we investigated growth and biomass yield in response to (i) soil moisture stress, and (ii) drought and elevated CO2. Additionally, we investigated seed germination rates following exposure to three elevated temperatures for two different time periods. Overall, moisture stress reduced plant height, stem diameter, and number of leaves. Elevated CO2 (700 ppm) appeared to reduce negative impacts of drought on biomass productivity. Heating seeds at 120◦C and above for either 180 or 300 s significantly reduced germination rate. These results inform an understanding of potential responses of A. retroflexus to future climate change and will be used to predict future occurrence of this weed. The finding that exposing seeds to high temperatures retards germination suggests fire could be used to prevent seed germination from soil seed banks, particularly in no-till situations, and therefore may be used to address infestations or prevent further spread of this weed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliates “Sandra Weller, Singarayer Florentine, Amali Welgama, Aakansha Chadha, Chrisopher Turville" are provided in this record**
- Authors: Weller, Sandra , Florentine, Singarayer , Welgama, Amali , Chadha, Aakansha , Turville, Christopher
- Date: 2021
- Type: Text , Journal article
- Relation: Agronomy Vol. 11, no. 4 (2021), p.
- Full Text:
- Reviewed:
- Description: Amaranthus retroflexus L. (Amaranthaceae), Redroot pigweed, is native to North America, but has become a weed of agriculture worldwide. Previous research into competition with food crops found it significantly reduces yields. Additionally, taxonomy, biomass allocation, physiological responses to light intensity, water stress, elevated CO2, and herbicide resistance have been inves-tigated. To extend other research findings, we investigated growth and biomass yield in response to (i) soil moisture stress, and (ii) drought and elevated CO2. Additionally, we investigated seed germination rates following exposure to three elevated temperatures for two different time periods. Overall, moisture stress reduced plant height, stem diameter, and number of leaves. Elevated CO2 (700 ppm) appeared to reduce negative impacts of drought on biomass productivity. Heating seeds at 120◦C and above for either 180 or 300 s significantly reduced germination rate. These results inform an understanding of potential responses of A. retroflexus to future climate change and will be used to predict future occurrence of this weed. The finding that exposing seeds to high temperatures retards germination suggests fire could be used to prevent seed germination from soil seed banks, particularly in no-till situations, and therefore may be used to address infestations or prevent further spread of this weed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliates “Sandra Weller, Singarayer Florentine, Amali Welgama, Aakansha Chadha, Chrisopher Turville" are provided in this record**