Designing multi-agent system organisations for flexible runtime behaviour
- Keogh, Kathleen, Sonenberg, Liz
- Authors: Keogh, Kathleen , Sonenberg, Liz
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 10, no. 15 (2020), p.
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- Description: We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by agents in a MAS organisation and from this analysis describe the OJAzzIC meta-model and an associated design method. We present results from simulation scenarios, varying both problem complexity and the level of organisational support provided in the design, to show that increasing design time guidance in the organisation specification can enable runtime flexibility afforded to agents and improve performance. Hence the results demonstrate the usefulness of the constructs captured in the OJAzzIC meta-model. © 2020 by the authors.
- Authors: Keogh, Kathleen , Sonenberg, Liz
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 10, no. 15 (2020), p.
- Full Text:
- Reviewed:
- Description: We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by agents in a MAS organisation and from this analysis describe the OJAzzIC meta-model and an associated design method. We present results from simulation scenarios, varying both problem complexity and the level of organisational support provided in the design, to show that increasing design time guidance in the organisation specification can enable runtime flexibility afforded to agents and improve performance. Hence the results demonstrate the usefulness of the constructs captured in the OJAzzIC meta-model. © 2020 by the authors.
Spatial modelling of bacterial diversity over the selected regions in Bangladesh by next-generation sequencing : role of water temperature
- Akter, Nabila, Wahiduzzaman, Md, Yeasmin, Alea, Islam, Kazi, Luo, Jing-Jia
- Authors: Akter, Nabila , Wahiduzzaman, Md , Yeasmin, Alea , Islam, Kazi , Luo, Jing-Jia
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 10, no. 7 (2020), p.
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- Description: In this study, a spatial model has been developed to investigate the role of water temperature to the distribution of bacteria over the selected regions in the Bay of Bengal, located in the southern region of Bangladesh using next-generation sequencing. Bacterial concentration, quantitative polymerase chain reactions, and sequencing were performed on water samples and identified Acidobacteria, Actinobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, Proteobacteria, and Verrucomicrobia. The spatial model tessellated the parts of the Bay of Bengal with hexagons and analyzed the relationship between the distribution of bacteria and water temperature. A geographically weighted regression was used to observe whether water temperature contributed strongly or weakly to the distribution of bacteria. The residuals were examined to assess the model's fitness. The spatial model has the potential to predict the bacterial diversity in the selected regions of Bangladesh. © 2020 by the authors.
- Authors: Akter, Nabila , Wahiduzzaman, Md , Yeasmin, Alea , Islam, Kazi , Luo, Jing-Jia
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 10, no. 7 (2020), p.
- Full Text:
- Reviewed:
- Description: In this study, a spatial model has been developed to investigate the role of water temperature to the distribution of bacteria over the selected regions in the Bay of Bengal, located in the southern region of Bangladesh using next-generation sequencing. Bacterial concentration, quantitative polymerase chain reactions, and sequencing were performed on water samples and identified Acidobacteria, Actinobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, Proteobacteria, and Verrucomicrobia. The spatial model tessellated the parts of the Bay of Bengal with hexagons and analyzed the relationship between the distribution of bacteria and water temperature. A geographically weighted regression was used to observe whether water temperature contributed strongly or weakly to the distribution of bacteria. The residuals were examined to assess the model's fitness. The spatial model has the potential to predict the bacterial diversity in the selected regions of Bangladesh. © 2020 by the authors.
From general language understanding to noisy text comprehension
- Kasthuriarachchy, Buddhika, Chetty, Madhu, Shatte, Adrian, Walls, Darren
- Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 17 (2021), p.
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- Description: Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The proposed research establishes effective methods for improving the comprehension of noisy texts. For this, we propose a new generic methodology to derive a diverse set of sentence vectors combining and extracting various linguistic characteristics from latent representations of multi-layer, pre-trained language models. Further, we clearly establish how BERT, a state-of-the-art pre-trained language model, comprehends the linguistic attributes of Tweets to identify appropriate sentence representations. Five new probing tasks are developed for Tweets, which can serve as benchmark probing tasks to study noisy text comprehension. Experiments are carried out for classification accuracy by deriving the sentence vectors from GloVe-based pre-trained models and Sentence-BERT, and by using different hidden layers from the BERT model. We show that the initial and middle layers of BERT have better capability for capturing the key linguistic characteristics of noisy texts than its latter layers. With complex predictive models, we further show that the sentence vector length has lesser importance to capture linguistic information, and the proposed sentence vectors for noisy texts perform better than the existing state-of-the-art sentence vectors. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 17 (2021), p.
- Full Text:
- Reviewed:
- Description: Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The proposed research establishes effective methods for improving the comprehension of noisy texts. For this, we propose a new generic methodology to derive a diverse set of sentence vectors combining and extracting various linguistic characteristics from latent representations of multi-layer, pre-trained language models. Further, we clearly establish how BERT, a state-of-the-art pre-trained language model, comprehends the linguistic attributes of Tweets to identify appropriate sentence representations. Five new probing tasks are developed for Tweets, which can serve as benchmark probing tasks to study noisy text comprehension. Experiments are carried out for classification accuracy by deriving the sentence vectors from GloVe-based pre-trained models and Sentence-BERT, and by using different hidden layers from the BERT model. We show that the initial and middle layers of BERT have better capability for capturing the key linguistic characteristics of noisy texts than its latter layers. With complex predictive models, we further show that the sentence vector length has lesser importance to capture linguistic information, and the proposed sentence vectors for noisy texts perform better than the existing state-of-the-art sentence vectors. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Indoor emission sources detection by pollutants interaction analysis
- Pang, Shaoning, Song, Lei, Sarrafzadeh, Abdolhossein, Coulson, Guy, Longley, Ian, Olivares, Gustavo
- Authors: Pang, Shaoning , Song, Lei , Sarrafzadeh, Abdolhossein , Coulson, Guy , Longley, Ian , Olivares, Gustavo
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 16 (2021), p.
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- Description: This study employs the correlation coefficients technique to support emission sources detection for indoor environments. Unlike existing methods analyzing merely primary pollution, we consider alternatively the secondary pollution (i.e., chemical reactions between pollutants in addition to pollutant level), and calculate intra pollutants correlation coefficients for characterizing and distinguishing emission events. Extensive experiments show that seven major indoor emission sources are identified by the proposed method, including (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session. Furthermore, our method improves the detection accuracy by a support vector machine compared to without data filtering and applying typical feature extraction methods such as PCA and LDA. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Pang, Shaoning , Song, Lei , Sarrafzadeh, Abdolhossein , Coulson, Guy , Longley, Ian , Olivares, Gustavo
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 16 (2021), p.
- Full Text:
- Reviewed:
- Description: This study employs the correlation coefficients technique to support emission sources detection for indoor environments. Unlike existing methods analyzing merely primary pollution, we consider alternatively the secondary pollution (i.e., chemical reactions between pollutants in addition to pollutant level), and calculate intra pollutants correlation coefficients for characterizing and distinguishing emission events. Extensive experiments show that seven major indoor emission sources are identified by the proposed method, including (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session. Furthermore, our method improves the detection accuracy by a support vector machine compared to without data filtering and applying typical feature extraction methods such as PCA and LDA. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Effectiveness of mouth rinsing versus ingesting pickle juice for alleviating electrically induced cramp in physically active adults
- Georgieva, Julia, Brade, Carly, Ducker, Kagan, Davey, Paul, Jacques, Angela, Ohno, Masato, Lavender, Andrew
- Authors: Georgieva, Julia , Brade, Carly , Ducker, Kagan , Davey, Paul , Jacques, Angela , Ohno, Masato , Lavender, Andrew
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 24 (2021), p.
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- Description: (1) Background: Stimulating oropharyngeal transient receptor potential (TRP) channels in-hibits muscle cramping by triggering a supraspinal reflex to reduce α-motor neuron hyperexcitability. This study investigated whether the longer stimulation of the TRP channels via mouth rinsing with PJ is more effective than drinking PJ at inhibiting an electrically induced muscle cramp (EIMC). Both conditions were compared to the control (water). (2) Methods: The tibial nerves in 11 cramp-prone adults were percutaneously stimulated to elicit an EIMC of the flexor hallucis brevis in three trials that took place one week apart from each other. At cramp onset, the participants received mouth rinsing and expelling PJ (25 mL), ingesting PJ (1 mL·kg−1 body-mass (BM)), or ingesting water (1 mL·kg−1 BM). Cramp onset and offset were induced by electromyography, and the severity of discomfort was recorded using a visual analogue scale (VAS). (3) Results: The median time to cramp cessation as a percentage of water was 82.8 ± 14.63% and 68.6 ± 47.78% for PJ ingestion and PJ mouth rinsing, respectively. These results had large variability, and no statistically significant differences were observed. There were also no differences in perceived cramp discomfort between conditions, despite the hazard ratios for the time taken to reach VAS = 0, which was higher than water (control) for PJ ingestion (22%) and mouth rinsing (35%) (p = 0.66 and 0.51, respectively). (4) Conclusions: The data suggest no difference in cramp duration and perceived discomfort between PJ and water. © 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 affiliate “Andrew Lavender” is provided in this record**
- Authors: Georgieva, Julia , Brade, Carly , Ducker, Kagan , Davey, Paul , Jacques, Angela , Ohno, Masato , Lavender, Andrew
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 24 (2021), p.
- Full Text:
- Reviewed:
- Description: (1) Background: Stimulating oropharyngeal transient receptor potential (TRP) channels in-hibits muscle cramping by triggering a supraspinal reflex to reduce α-motor neuron hyperexcitability. This study investigated whether the longer stimulation of the TRP channels via mouth rinsing with PJ is more effective than drinking PJ at inhibiting an electrically induced muscle cramp (EIMC). Both conditions were compared to the control (water). (2) Methods: The tibial nerves in 11 cramp-prone adults were percutaneously stimulated to elicit an EIMC of the flexor hallucis brevis in three trials that took place one week apart from each other. At cramp onset, the participants received mouth rinsing and expelling PJ (25 mL), ingesting PJ (1 mL·kg−1 body-mass (BM)), or ingesting water (1 mL·kg−1 BM). Cramp onset and offset were induced by electromyography, and the severity of discomfort was recorded using a visual analogue scale (VAS). (3) Results: The median time to cramp cessation as a percentage of water was 82.8 ± 14.63% and 68.6 ± 47.78% for PJ ingestion and PJ mouth rinsing, respectively. These results had large variability, and no statistically significant differences were observed. There were also no differences in perceived cramp discomfort between conditions, despite the hazard ratios for the time taken to reach VAS = 0, which was higher than water (control) for PJ ingestion (22%) and mouth rinsing (35%) (p = 0.66 and 0.51, respectively). (4) Conclusions: The data suggest no difference in cramp duration and perceived discomfort between PJ and water. © 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 affiliate “Andrew Lavender” is provided in this record**
Assessing nitrate contamination risks in groundwater : a machine learning approach
- Awais, Muhammad, Aslam, Bilal, Maqsoom, Ahsen, Khalil, Umer, Imran, Muhammad
- Authors: Awais, Muhammad , Aslam, Bilal , Maqsoom, Ahsen , Khalil, Umer , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 21 (2021), p.
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- Description: Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks. © 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 affiliate “Muhammad Imran” is provided in this record**
- Authors: Awais, Muhammad , Aslam, Bilal , Maqsoom, Ahsen , Khalil, Umer , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 21 (2021), p.
- Full Text:
- Reviewed:
- Description: Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks. © 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 affiliate “Muhammad Imran” is provided in this record**
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