Test-retest measurement invariance of the nine-item internet gaming disorder scale in two countries : a preliminary longitudinal study
- Stavropoulos, Vasileios, Bamford, Luke, Beard, Charlotte, Gomez, Rapson, Griffiths, Mark
- Authors: Stavropoulos, Vasileios , Bamford, Luke , Beard, Charlotte , Gomez, Rapson , Griffiths, Mark
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Mental Health and Addiction Vol. 19, no. 6 (2021), p. 2003-2020
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- Description: The reliable longitudinal assessment of Internet Gaming Disorder (IGD) behaviors is viewed by many as a pivotal clinical and research priority. The present study is the first to examine the test-retest measurement invariance of IGD ratings, as assessed using the short-form nine-item Internet Gaming Disorder Scale (IGDS9-SF) over an approximate period of 3 months, across two normative national samples. Differences referring to the mode of the data collection (face-to-face [FtF] vs. online) were also considered. Two sequences of successive multiple group confirmatory factor analyses (CFAs) were calculated to longitudinally assess the psychometric properties of the IGDS9-SF using emergent adults, gamers from (i) the United States of America (USA; N = 120, 18–29 years, Meanage = 22.35, 51.6% male) assessed online and; and (ii) Australia (N = 61, 18–31 years, Meanage = 23.02, 75.4% male) assessed FtF. Configural invariance was established across both samples, and metric and scalar invariances were supported for the USA sample. Interestingly, only partial metric (factor loadings for Items 2 and 3 non-invariant) and partial scalar invariance (i.e., all thresholds of Items 1 and 2, and thresholds 1, 3, for Items 4, 6, 8, and 9 non-invariant) were established for the Australian sample. Findings are discussed in the light of using IGDS9-SF to assess and monitor IGD behaviors over time in both in clinical and non-clinical settings. © 2019, The Author(s).
- Authors: Stavropoulos, Vasileios , Bamford, Luke , Beard, Charlotte , Gomez, Rapson , Griffiths, Mark
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Mental Health and Addiction Vol. 19, no. 6 (2021), p. 2003-2020
- Full Text:
- Reviewed:
- Description: The reliable longitudinal assessment of Internet Gaming Disorder (IGD) behaviors is viewed by many as a pivotal clinical and research priority. The present study is the first to examine the test-retest measurement invariance of IGD ratings, as assessed using the short-form nine-item Internet Gaming Disorder Scale (IGDS9-SF) over an approximate period of 3 months, across two normative national samples. Differences referring to the mode of the data collection (face-to-face [FtF] vs. online) were also considered. Two sequences of successive multiple group confirmatory factor analyses (CFAs) were calculated to longitudinally assess the psychometric properties of the IGDS9-SF using emergent adults, gamers from (i) the United States of America (USA; N = 120, 18–29 years, Meanage = 22.35, 51.6% male) assessed online and; and (ii) Australia (N = 61, 18–31 years, Meanage = 23.02, 75.4% male) assessed FtF. Configural invariance was established across both samples, and metric and scalar invariances were supported for the USA sample. Interestingly, only partial metric (factor loadings for Items 2 and 3 non-invariant) and partial scalar invariance (i.e., all thresholds of Items 1 and 2, and thresholds 1, 3, for Items 4, 6, 8, and 9 non-invariant) were established for the Australian sample. Findings are discussed in the light of using IGDS9-SF to assess and monitor IGD behaviors over time in both in clinical and non-clinical settings. © 2019, The Author(s).
A framework for cardiac arrhythmia detection from IoT-based ECGs
- He, Jinyuan, Rong, Jia, Sun, Le, Wang, Hua, Zhang, Yanchun, Ma, Jiangang
- Authors: He, Jinyuan , Rong, Jia , Sun, Le , Wang, Hua , Zhang, Yanchun , Ma, Jiangang
- Date: 2020
- Type: Text , Journal article
- Relation: World Wide Web Vol. 23, no. 5 (2020), p. 2835-2850
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- Description: Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12% of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
- Authors: He, Jinyuan , Rong, Jia , Sun, Le , Wang, Hua , Zhang, Yanchun , Ma, Jiangang
- Date: 2020
- Type: Text , Journal article
- Relation: World Wide Web Vol. 23, no. 5 (2020), p. 2835-2850
- Full Text:
- Reviewed:
- Description: Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12% of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
A survey on context awareness in big data analytics for business applications
- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
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- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
- Full Text:
- Reviewed:
- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
An approach to map geography mark-up language data to resource description framework schema
- Faqir, Ammara, Mahmood, Aqsa, Qazi, Kiran, Malik, Saleem
- Authors: Faqir, Ammara , Mahmood, Aqsa , Qazi, Kiran , Malik, Saleem
- Date: 2020
- Type: Text , Conference paper
- Relation: 2nd International Conference on Intelligent Technologies and Applications, INTAP 2019 Vol. 1198, p. 343-354
- Full Text:
- Reviewed:
- Description: GML serves as premier modeling language used to represent data of geographic information related to geography locations. However, a problem of GML is its ability to integrate with a variety of geographical and GPS applications. Since, GML saves data in coordinates and in topology for the purpose to integrate data with variety of applications on semantic web, data be mapped to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). An approach of mapping GML metadata to RDFS is presented in this paper. This study focuses on the methodology to convert GML data in semantics to represent in extended and enriched form such as RDFS as representation in RDF is not sufficient over semantic web. Firstly, we have GML script from case study and parse it using GML parser and get XML file. XML file parse using Java and get text file to extract GML features and then get a graph form of these features. After that we designed methodology of prototype tool to map GML features to RDFS. Tool performed features by features mapping and extracted results are represented in the tabular form of mapping GML metadata to RDFS. © 2020, Springer Nature Singapore Pte Ltd.
- Description: E1
- Authors: Faqir, Ammara , Mahmood, Aqsa , Qazi, Kiran , Malik, Saleem
- Date: 2020
- Type: Text , Conference paper
- Relation: 2nd International Conference on Intelligent Technologies and Applications, INTAP 2019 Vol. 1198, p. 343-354
- Full Text:
- Reviewed:
- Description: GML serves as premier modeling language used to represent data of geographic information related to geography locations. However, a problem of GML is its ability to integrate with a variety of geographical and GPS applications. Since, GML saves data in coordinates and in topology for the purpose to integrate data with variety of applications on semantic web, data be mapped to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). An approach of mapping GML metadata to RDFS is presented in this paper. This study focuses on the methodology to convert GML data in semantics to represent in extended and enriched form such as RDFS as representation in RDF is not sufficient over semantic web. Firstly, we have GML script from case study and parse it using GML parser and get XML file. XML file parse using Java and get text file to extract GML features and then get a graph form of these features. After that we designed methodology of prototype tool to map GML features to RDFS. Tool performed features by features mapping and extracted results are represented in the tabular form of mapping GML metadata to RDFS. © 2020, Springer Nature Singapore Pte Ltd.
- Description: E1
An existence result for quasi-equilibrium problems via Ekeland’s variational principle
- Cotrina, John, Théra, Michel, Zúñiga, Javier
- Authors: Cotrina, John , Théra, Michel , Zúñiga, Javier
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Optimization Theory and Applications Vol. 187, no. 2 (2020), p. 336-355
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- Description: This paper deals with the existence of solutions to equilibrium and quasi-equilibrium problems without any convexity assumption. Coverage includes some equivalences to the Ekeland variational principle for bifunctions and basic facts about transfer lower continuity. An application is given to systems of quasi-equilibrium problems. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
- Description: Research of M. Théra is supported by the Australian Research Council (ARC) Grant DP160100854 and benefited from the support of the FMJH Program PGMO and from the support of EDF. http://purl.org/au-research/grants/arc/DP160100854
- Authors: Cotrina, John , Théra, Michel , Zúñiga, Javier
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Optimization Theory and Applications Vol. 187, no. 2 (2020), p. 336-355
- Full Text:
- Reviewed:
- Description: This paper deals with the existence of solutions to equilibrium and quasi-equilibrium problems without any convexity assumption. Coverage includes some equivalences to the Ekeland variational principle for bifunctions and basic facts about transfer lower continuity. An application is given to systems of quasi-equilibrium problems. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
- Description: Research of M. Théra is supported by the Australian Research Council (ARC) Grant DP160100854 and benefited from the support of the FMJH Program PGMO and from the support of EDF. http://purl.org/au-research/grants/arc/DP160100854
Application of thermal fragmentation in Australian hard rock underground narrow-vein mining
- Drake, Bradley, Koroznikova, Larissa, Tuck, Michael, Durkin, Steve
- Authors: Drake, Bradley , Koroznikova, Larissa , Tuck, Michael , Durkin, Steve
- Date: 2020
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 37, no. 1 (2020), p. 219-229
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- Description: This paper presents the results from the investigation of the application of thermal fragmentation in Australian hard rock underground narrow-vein mining. Two geologically similar samples from an underground narrow-vein hard rock gold mine were collected to obtain a measure of the technology’s ability to recover ore by the creation of large thermal openings to assess the applicability of the thermal method. Particle size distribution showed a higher generation of fine product, − 2 mm, by thermal fragmentation compared with selective blasting by 31%. The Bond work index for thermal ore (12.62 kWh/t) is half to that of the blasted ore value (25.32 kWh/t). The average grindability obtained for the thermal ore sample was greater than the blasted sample by a factor of 2.44, a higher value indicating a decrease in the energy required to grind. The thermal fragmentation method generates product with higher dissolution of gold in cyanide, by 14% for the − 9.5 + 2 mm size fraction samples. Additionally, the thermal fragmentation results in higher production of − 9.5 + 2 mm material by 15 % compared with selective blasting. © 2019, Society for Mining, Metallurgy & Exploration Inc.
- Authors: Drake, Bradley , Koroznikova, Larissa , Tuck, Michael , Durkin, Steve
- Date: 2020
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 37, no. 1 (2020), p. 219-229
- Full Text:
- Reviewed:
- Description: This paper presents the results from the investigation of the application of thermal fragmentation in Australian hard rock underground narrow-vein mining. Two geologically similar samples from an underground narrow-vein hard rock gold mine were collected to obtain a measure of the technology’s ability to recover ore by the creation of large thermal openings to assess the applicability of the thermal method. Particle size distribution showed a higher generation of fine product, − 2 mm, by thermal fragmentation compared with selective blasting by 31%. The Bond work index for thermal ore (12.62 kWh/t) is half to that of the blasted ore value (25.32 kWh/t). The average grindability obtained for the thermal ore sample was greater than the blasted sample by a factor of 2.44, a higher value indicating a decrease in the energy required to grind. The thermal fragmentation method generates product with higher dissolution of gold in cyanide, by 14% for the − 9.5 + 2 mm size fraction samples. Additionally, the thermal fragmentation results in higher production of − 9.5 + 2 mm material by 15 % compared with selective blasting. © 2019, Society for Mining, Metallurgy & Exploration Inc.
Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
- Chen, Wusi, Khandelwal, Manoj, Murlidhar, Bhatawdekar, Bui, Dieu, Tahir, Mahmood, Katebi, Javad
- Authors: Chen, Wusi , Khandelwal, Manoj , Murlidhar, Bhatawdekar , Bui, Dieu , Tahir, Mahmood , Katebi, Javad
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 36, no. 2 (2020), p. 783-793
- Full Text:
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- Description: In this study, evaluation and prediction of rock cohesion is assessed using multiple regression as well as group method of data handling (GMDH). It is a well-known fact that cohesion is the most crucial rock shear strength parameter, which is a key parameter for the stability evaluation of some geotechnical structures such as rock slope. To fulfill the aim of this study, a database of three model input parameters, i.e., p wave velocity, uniaxial compressive strength and Brazilian tensile strength and one model output, which is cohesion of limestone samples was prepared and utilized by GMDH. Different GMDH models with neurons and layers and selection pressure were tested and assessed. It was found that GMDH model number 4 (with 8 layers) shows the best performance among all of tested models between the input and output parameters for the prediction and assessment of rock cohesion with coefficient of determination (R2) values of 0.928 and 0.929, root mean square error values of 0.3545 and 0.3154 for training and testing datasets, respectively. Multiple regression analysis was also performed on the same database and R2 values were obtained as 0.8173 and 0.8313 between input and output parameters for the training and testing of the models, respectively. The GMDH technique developed in this study is introduced as a new model in field of rock shear strength parameters. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Chen, Wusi , Khandelwal, Manoj , Murlidhar, Bhatawdekar , Bui, Dieu , Tahir, Mahmood , Katebi, Javad
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 36, no. 2 (2020), p. 783-793
- Full Text:
- Reviewed:
- Description: In this study, evaluation and prediction of rock cohesion is assessed using multiple regression as well as group method of data handling (GMDH). It is a well-known fact that cohesion is the most crucial rock shear strength parameter, which is a key parameter for the stability evaluation of some geotechnical structures such as rock slope. To fulfill the aim of this study, a database of three model input parameters, i.e., p wave velocity, uniaxial compressive strength and Brazilian tensile strength and one model output, which is cohesion of limestone samples was prepared and utilized by GMDH. Different GMDH models with neurons and layers and selection pressure were tested and assessed. It was found that GMDH model number 4 (with 8 layers) shows the best performance among all of tested models between the input and output parameters for the prediction and assessment of rock cohesion with coefficient of determination (R2) values of 0.928 and 0.929, root mean square error values of 0.3545 and 0.3154 for training and testing datasets, respectively. Multiple regression analysis was also performed on the same database and R2 values were obtained as 0.8173 and 0.8313 between input and output parameters for the training and testing of the models, respectively. The GMDH technique developed in this study is introduced as a new model in field of rock shear strength parameters. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
Big data analytics for preventive medicine
- Razzak, Muhammad, Imran, Muhammad, Xu, Guandong
- Authors: Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
- Date: 2020
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 32, no. 9 (2020), p. 4417-4451
- Full Text:
- Reviewed:
- Description: Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
- Date: 2020
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 32, no. 9 (2020), p. 4417-4451
- Full Text:
- Reviewed:
- Description: Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
Characterizations of robust and stable duality for linearly perturbed uncertain optimization problems
- Dinh, Nguyen, Goberna, Miguel, López, Marco, Volle, Michel
- Authors: Dinh, Nguyen , Goberna, Miguel , López, Marco , Volle, Michel
- Date: 2020
- Type: Text , Conference paper
- Relation: Jonathan Borwein Commemorative Conference, JBCC 2017 Vol. 313, p. 43-74
- Relation: http://purl.org/au-research/grants/arc/DP180100602
- Full Text:
- Reviewed:
- Description: We introduce a robust optimization model consisting in a family of perturbation functions giving rise to certain pairs of dual optimization problems in which the dual variable depends on the uncertainty parameter. The interest of our approach is illustrated by some examples, including uncertain conic optimization and infinite optimization via discretization. The main results characterize desirable robust duality relations (as robust zero-duality gap) by formulas involving the epsilon-minima or the epsilon-subdifferentials of the objective function. The two extreme cases, namely, the usual perturbational duality (without uncertainty), and the duality for the supremum of functions (duality parameter vanishing) are analyzed in detail. © Springer Nature Switzerland AG 2020.
- Authors: Dinh, Nguyen , Goberna, Miguel , López, Marco , Volle, Michel
- Date: 2020
- Type: Text , Conference paper
- Relation: Jonathan Borwein Commemorative Conference, JBCC 2017 Vol. 313, p. 43-74
- Relation: http://purl.org/au-research/grants/arc/DP180100602
- Full Text:
- Reviewed:
- Description: We introduce a robust optimization model consisting in a family of perturbation functions giving rise to certain pairs of dual optimization problems in which the dual variable depends on the uncertainty parameter. The interest of our approach is illustrated by some examples, including uncertain conic optimization and infinite optimization via discretization. The main results characterize desirable robust duality relations (as robust zero-duality gap) by formulas involving the epsilon-minima or the epsilon-subdifferentials of the objective function. The two extreme cases, namely, the usual perturbational duality (without uncertainty), and the duality for the supremum of functions (duality parameter vanishing) are analyzed in detail. © Springer Nature Switzerland AG 2020.
Cyberattack triage using incremental clustering for intrusion detection systems
- Taheri, Sona, Bagirov, Adil, Gondal, Iqbal, Brown, Simon
- Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
- Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).
- Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
- Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).
DEFINE: friendship detection based on node enhancement
- Pan, Hanxiao, Guo, Teng, Bedru, Hayat, Qing, Qing, Zhang, Dongyu, Xia, Feng
- Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
- Full Text:
- Reviewed:
- Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
- Description: E1
- Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
- Full Text:
- Reviewed:
- Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
- Description: E1
Directional metric pseudo subregularity of set-valued mappings: a general model
- Van Ngai, Huynh, Tron, Nguyen, Van Vu, Nguyen, Théra, Michel
- Authors: Van Ngai, Huynh , Tron, Nguyen , Van Vu, Nguyen , Théra, Michel
- Date: 2020
- Type: Text , Journal article
- Relation: Set-Valued and Variational Analysis Vol. 28, no. 1 (2020), p. 61-87
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- Description: This paper investigates a new general pseudo subregularity model which unifies some important nonlinear (sub)regularity models studied recently in the literature. Some slope and abstract coderivative characterizations are established. © 2019, Springer Nature B.V.
- Authors: Van Ngai, Huynh , Tron, Nguyen , Van Vu, Nguyen , Théra, Michel
- Date: 2020
- Type: Text , Journal article
- Relation: Set-Valued and Variational Analysis Vol. 28, no. 1 (2020), p. 61-87
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- Description: This paper investigates a new general pseudo subregularity model which unifies some important nonlinear (sub)regularity models studied recently in the literature. Some slope and abstract coderivative characterizations are established. © 2019, Springer Nature B.V.
Ethnobotany, rattan agroforestry, and conservation of ecosystem services in Central Kalimantan, Indonesia
- Afentina, McShane, Paul, Wright, Wendy
- Authors: Afentina , McShane, Paul , Wright, Wendy
- Date: 2020
- Type: Text , Journal article
- Relation: Agroforestry Systems Vol. 94, no. 2 (2020), p. 639-650
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- Description: Rattan agroforestry is an important land use system in Central Kalimantan, Indonesia, providing a wide range of products for subsistence communities. The ethnobotanical importance of rattan includes heritage values reflecting traditional ecological knowledge. This traditional forestry practice is consistent with necessary conservation of biodiversity and ecosystem services currently threatened by expansion of oil palm plantations. We examined species composition and morphology (including life stages) of vegetation associated with rattan agroforests in the Katingan district, Central Kalimantan. An examination of harvested rattan plots revealed 101 species of vegetation of which 90% are considered to be useful (food, construction materials, medicines) and most (97%) were native species, typical of lowland tropical forest vegetation. Vegetation in the rattan agroforests was dominated by trees (in terms of species richness). There were 80 species of trees, representing 79% of the plants surveyed. Vitex pubescens (kaluan) had the highest importance value as it occupied more space, was represented by more individuals and was most frequently found in rattan gardens. These trees in general have a relatively open canopy with strong branches; properties considered ideal to support rattan. Canopy forming species are actively managed to provide for growth of useful understory vegetation (including rattan) important in the livelihoods of village communities. Rattan agroforests also provide cultural services reflecting traditional use (e.g. a sense of belonging and ancestral linkages for local forest-dependent communities). The importance of ethnobotanical approaches to rattan cultivation includes the socio-economic evaluation of land use and the promotion of sustainable land use policies in Indonesia. This is important in the context of oil palm expansion which has a demonstrably adverse impact on ecosystem services. © 2019, Springer Nature B.V.
- Authors: Afentina , McShane, Paul , Wright, Wendy
- Date: 2020
- Type: Text , Journal article
- Relation: Agroforestry Systems Vol. 94, no. 2 (2020), p. 639-650
- Full Text:
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- Description: Rattan agroforestry is an important land use system in Central Kalimantan, Indonesia, providing a wide range of products for subsistence communities. The ethnobotanical importance of rattan includes heritage values reflecting traditional ecological knowledge. This traditional forestry practice is consistent with necessary conservation of biodiversity and ecosystem services currently threatened by expansion of oil palm plantations. We examined species composition and morphology (including life stages) of vegetation associated with rattan agroforests in the Katingan district, Central Kalimantan. An examination of harvested rattan plots revealed 101 species of vegetation of which 90% are considered to be useful (food, construction materials, medicines) and most (97%) were native species, typical of lowland tropical forest vegetation. Vegetation in the rattan agroforests was dominated by trees (in terms of species richness). There were 80 species of trees, representing 79% of the plants surveyed. Vitex pubescens (kaluan) had the highest importance value as it occupied more space, was represented by more individuals and was most frequently found in rattan gardens. These trees in general have a relatively open canopy with strong branches; properties considered ideal to support rattan. Canopy forming species are actively managed to provide for growth of useful understory vegetation (including rattan) important in the livelihoods of village communities. Rattan agroforests also provide cultural services reflecting traditional use (e.g. a sense of belonging and ancestral linkages for local forest-dependent communities). The importance of ethnobotanical approaches to rattan cultivation includes the socio-economic evaluation of land use and the promotion of sustainable land use policies in Indonesia. This is important in the context of oil palm expansion which has a demonstrably adverse impact on ecosystem services. © 2019, Springer Nature B.V.
Geometric and metric characterizations of transversality properties
- Bui, Hoa, Cuong, Nguyen, Kruger, Alexander
- Authors: Bui, Hoa , Cuong, Nguyen , Kruger, Alexander
- Date: 2020
- Type: Text , Journal article
- Relation: Vietnam Journal of Mathematics Vol. 48, no. 2 (2020), p. 277-297
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- Description: This paper continues the study of ‘good arrangements’ of collections of sets near a point in their intersection. We clarify quantitative relations between several geometric and metric characterizations of the transversality properties of collections of sets and the corresponding regularity properties of set-valued mappings. We expose all the parameters involved in the definitions and characterizations and establish relations between them. This allows us to classify the quantitative geometric and metric characterizations of transversality and regularity, and subdivide them into two groups with complete exact equivalences between the parameters within each group and clear relations between the values of the parameters in different groups. © 2020, Vietnam Academy of Science and Technology (VAST) and Springer Nature Singapore Pte Ltd.
- Authors: Bui, Hoa , Cuong, Nguyen , Kruger, Alexander
- Date: 2020
- Type: Text , Journal article
- Relation: Vietnam Journal of Mathematics Vol. 48, no. 2 (2020), p. 277-297
- Full Text:
- Reviewed:
- Description: This paper continues the study of ‘good arrangements’ of collections of sets near a point in their intersection. We clarify quantitative relations between several geometric and metric characterizations of the transversality properties of collections of sets and the corresponding regularity properties of set-valued mappings. We expose all the parameters involved in the definitions and characterizations and establish relations between them. This allows us to classify the quantitative geometric and metric characterizations of transversality and regularity, and subdivide them into two groups with complete exact equivalences between the parameters within each group and clear relations between the values of the parameters in different groups. © 2020, Vietnam Academy of Science and Technology (VAST) and Springer Nature Singapore Pte Ltd.
Inattention and disordered gaming : does culture matter?
- Stavropoulos, Vasileios, Baynes, Kyi, O’Farrel, Dominic, Gomez, Rapson, Mueller, Astrid, Yucel, Murat, Griffiths, Mark
- Authors: Stavropoulos, Vasileios , Baynes, Kyi , O’Farrel, Dominic , Gomez, Rapson , Mueller, Astrid , Yucel, Murat , Griffiths, Mark
- Date: 2020
- Type: Text , Journal article
- Relation: Psychiatric Quarterly Vol. 91, no. 2 (2020), p. 333-348
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- Description: Problematic gaming has emerged as a contemporary concern, leading to the introduction of the diagnostic term ‘Internet Gaming Disorder’ (IGD; American Psychiatric Association). The present study aims to empirically assess the association between inattention and IGD, in the light of variable levels of vertical-individualism that reflects cultural inclinations towards independence, competitiveness, and hierarchy. The participants (N = 1032) comprised a normative cohort of Massively Multiplayer Online (MMO) gamers (Mage = 24 years; 48.7% male). IGD was measured with the nine-item short-form IGD Scale (IGD9-SF), inattention with the Attention Deficit Hyperactivity Disorder (ADHD) Self-Report Scale, and vertical individualism with the Individualism-Collectivism Questionnaire. Complex hierarchical and moderated regressions were employed. Findings demonstrated an association between IGD and inattention, and additionally showed that this association was exacerbated by a more vertically-individualistic cultural orientation without significant gender differences. The need of differentially addressing IGD risk among inattentive gamers of diverse cultural orientation is highlighted. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
- Authors: Stavropoulos, Vasileios , Baynes, Kyi , O’Farrel, Dominic , Gomez, Rapson , Mueller, Astrid , Yucel, Murat , Griffiths, Mark
- Date: 2020
- Type: Text , Journal article
- Relation: Psychiatric Quarterly Vol. 91, no. 2 (2020), p. 333-348
- Full Text:
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- Description: Problematic gaming has emerged as a contemporary concern, leading to the introduction of the diagnostic term ‘Internet Gaming Disorder’ (IGD; American Psychiatric Association). The present study aims to empirically assess the association between inattention and IGD, in the light of variable levels of vertical-individualism that reflects cultural inclinations towards independence, competitiveness, and hierarchy. The participants (N = 1032) comprised a normative cohort of Massively Multiplayer Online (MMO) gamers (Mage = 24 years; 48.7% male). IGD was measured with the nine-item short-form IGD Scale (IGD9-SF), inattention with the Attention Deficit Hyperactivity Disorder (ADHD) Self-Report Scale, and vertical individualism with the Individualism-Collectivism Questionnaire. Complex hierarchical and moderated regressions were employed. Findings demonstrated an association between IGD and inattention, and additionally showed that this association was exacerbated by a more vertically-individualistic cultural orientation without significant gender differences. The need of differentially addressing IGD risk among inattentive gamers of diverse cultural orientation is highlighted. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Long-term athletic training does not alter age-associated reductions of left-ventricular mid-diastolic lengthening or expansion at rest
- Beaumont, Alexander, Campbell, Amy, Unnithan, Viswanath, Grace, Fergal, Knox, Allan, Sculthorpe, Nicholas
- Authors: Beaumont, Alexander , Campbell, Amy , Unnithan, Viswanath , Grace, Fergal , Knox, Allan , Sculthorpe, Nicholas
- Date: 2020
- Type: Text , Journal article
- Relation: European Journal of Applied Physiology Vol. 120, no. 9 (2020), p. 2059-2073
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- Description: Purpose: The interaction of ageing and exercise training status on left-ventricular (LV) peak strain is unclear. Additionally, strain analysis across the entire cardiac cycle facilitates a more detailed assessment of deformation, yet this has not been implemented to characterize the ageing LV and in association with training status. This study investigated healthy ageing and training status on LV systolic and diastolic strain utilizing novel echocardiographic applications. Methods: Forty healthy males were included and allocated into four groups; young recreationally active (YRA,n = 9; 28 ± 5 years), old recreationally active (ORA, n = 10; 68 ± 6), young trained (YT,n = 10; 27 ± 6 years), and old trained (OT, n = 11, 64 ± 4 years) groups. Two-dimensional speckle-tracking echocardiography was performed to ascertain peak LV longitudinal and circumferential strain (base and apex) strain within each myocardial layer and at 5% increments across the cardiac cycle. Results: Older groups had lower diastolic longitudinal lengthening and circumferential expansion between 40–85% mid-diastole, regardless of training status (P < 0.05). Whereas, strain throughout systole was similar between groups (P > 0.05). Longitudinal and circumferential (base and apex) peak and layer-specific strain did not differ between groups (P > 0.05). Conclusion: Novel applications of diastolic strain revealed lower age-associated LV longitudinal lengthening and circumferential expansion in older age. Yet, diastolic strain profiles did not differ based on chronic habits of exercise training and, thus, older trained men did not demonstrate an attenuation of age-associated differences in mid-diastolic LV strain. © 2020, The Author(s).
- Authors: Beaumont, Alexander , Campbell, Amy , Unnithan, Viswanath , Grace, Fergal , Knox, Allan , Sculthorpe, Nicholas
- Date: 2020
- Type: Text , Journal article
- Relation: European Journal of Applied Physiology Vol. 120, no. 9 (2020), p. 2059-2073
- Full Text:
- Reviewed:
- Description: Purpose: The interaction of ageing and exercise training status on left-ventricular (LV) peak strain is unclear. Additionally, strain analysis across the entire cardiac cycle facilitates a more detailed assessment of deformation, yet this has not been implemented to characterize the ageing LV and in association with training status. This study investigated healthy ageing and training status on LV systolic and diastolic strain utilizing novel echocardiographic applications. Methods: Forty healthy males were included and allocated into four groups; young recreationally active (YRA,n = 9; 28 ± 5 years), old recreationally active (ORA, n = 10; 68 ± 6), young trained (YT,n = 10; 27 ± 6 years), and old trained (OT, n = 11, 64 ± 4 years) groups. Two-dimensional speckle-tracking echocardiography was performed to ascertain peak LV longitudinal and circumferential strain (base and apex) strain within each myocardial layer and at 5% increments across the cardiac cycle. Results: Older groups had lower diastolic longitudinal lengthening and circumferential expansion between 40–85% mid-diastole, regardless of training status (P < 0.05). Whereas, strain throughout systole was similar between groups (P > 0.05). Longitudinal and circumferential (base and apex) peak and layer-specific strain did not differ between groups (P > 0.05). Conclusion: Novel applications of diastolic strain revealed lower age-associated LV longitudinal lengthening and circumferential expansion in older age. Yet, diastolic strain profiles did not differ based on chronic habits of exercise training and, thus, older trained men did not demonstrate an attenuation of age-associated differences in mid-diastolic LV strain. © 2020, The Author(s).
Navigating changing times : exploring teacher educator experiences of resilience
- McDonough, Sharon, Papatraianou, Lisa, Strangeways, Al, Mansfield, Caroline, Beutel, Denise
- Authors: McDonough, Sharon , Papatraianou, Lisa , Strangeways, Al , Mansfield, Caroline , Beutel, Denise
- Date: 2020
- Type: Text , Book chapter
- Relation: Cultivating Teacher Resilience: International Approaches, Applications and Impact Chapter 17 p. 279–294
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- Description: While there exists notable research in Australia and internationally on the ways pre-service and early career teachers develop and maintain resilience, there is a paucity of literature examining the resilience of teacher educators. The teacher education landscape has a dynamic nature, and in the Australian context, there have been multiple changes to policy and accreditation that have impacted on the work of teacher educators, including: the introduction of literacy and numeracy testing and a teaching performance assessment for teacher education students; and strict regulatory controls for providers. This context, combined with the intensification of academic work in higher education settings, has led us to investigate the personal and contextual factors that enable or constrain teacher educators’ resilience. In this chapter, we draw on a social ecological model of resilience to explore the factors that sustain and challenge teacher educators in their work, and use the findings to highlight implications for the field of teacher education.
- Authors: McDonough, Sharon , Papatraianou, Lisa , Strangeways, Al , Mansfield, Caroline , Beutel, Denise
- Date: 2020
- Type: Text , Book chapter
- Relation: Cultivating Teacher Resilience: International Approaches, Applications and Impact Chapter 17 p. 279–294
- Full Text:
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- Description: While there exists notable research in Australia and internationally on the ways pre-service and early career teachers develop and maintain resilience, there is a paucity of literature examining the resilience of teacher educators. The teacher education landscape has a dynamic nature, and in the Australian context, there have been multiple changes to policy and accreditation that have impacted on the work of teacher educators, including: the introduction of literacy and numeracy testing and a teaching performance assessment for teacher education students; and strict regulatory controls for providers. This context, combined with the intensification of academic work in higher education settings, has led us to investigate the personal and contextual factors that enable or constrain teacher educators’ resilience. In this chapter, we draw on a social ecological model of resilience to explore the factors that sustain and challenge teacher educators in their work, and use the findings to highlight implications for the field of teacher education.
Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques
- Rezaeineshat, Afsaneh, Monjezi, Masoud, Mehrdanesh, Amirhossein, Khandelwal, Manoj
- Authors: Rezaeineshat, Afsaneh , Monjezi, Masoud , Mehrdanesh, Amirhossein , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 2 (2020), p.
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- Description: Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG.
- Authors: Rezaeineshat, Afsaneh , Monjezi, Masoud , Mehrdanesh, Amirhossein , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 2 (2020), p.
- Full Text:
- Reviewed:
- Description: Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG.
Polytopes close to being simple
- Pineda-Villavicencio, Guillermo, Ugon, Julien, Yost, David
- Authors: Pineda-Villavicencio, Guillermo , Ugon, Julien , Yost, David
- Date: 2020
- Type: Text , Journal article
- Relation: Discrete and Computational Geometry Vol. 64, no. 1 (2020), p. 200-215
- Relation: http://purl.org/au-research/grants/arc/DP180100602
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- Description: It is known that polytopes with at most two nonsimple vertices are reconstructible from their graphs, and that d-polytopes with at most d- 2 nonsimple vertices are reconstructible from their 2-skeletons. Here we close the gap between 2 and d- 2 , showing that certain polytopes with more than two nonsimple vertices are reconstructible from their graphs. In particular, we prove that reconstructibility from graphs also holds for d-polytopes with d+ k vertices and at most d- k+ 3 nonsimple vertices, provided k
- Authors: Pineda-Villavicencio, Guillermo , Ugon, Julien , Yost, David
- Date: 2020
- Type: Text , Journal article
- Relation: Discrete and Computational Geometry Vol. 64, no. 1 (2020), p. 200-215
- Relation: http://purl.org/au-research/grants/arc/DP180100602
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- Description: It is known that polytopes with at most two nonsimple vertices are reconstructible from their graphs, and that d-polytopes with at most d- 2 nonsimple vertices are reconstructible from their 2-skeletons. Here we close the gap between 2 and d- 2 , showing that certain polytopes with more than two nonsimple vertices are reconstructible from their graphs. In particular, we prove that reconstructibility from graphs also holds for d-polytopes with d+ k vertices and at most d- k+ 3 nonsimple vertices, provided k
Pre-service teacher perceptions of LANTITE : complexity theory in action?
- Burke, Jenene, Sellings, Peter, Nelson, Naomi
- Authors: Burke, Jenene , Sellings, Peter , Nelson, Naomi
- Date: 2020
- Type: Text , Book chapter
- Relation: Teacher Education in Globalised Times Chapter 8 p. 139-157
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- Authors: Burke, Jenene , Sellings, Peter , Nelson, Naomi
- Date: 2020
- Type: Text , Book chapter
- Relation: Teacher Education in Globalised Times Chapter 8 p. 139-157
- Full Text:
- Reviewed: