title: Editorial

authors: Wright, Noeline

subjects: primary schools

abstract: An introduction is presented in which the editor discusses various reports within the issue on topics including electronic learning tools, mathematical tasks of children in primary school settings, and teacher education.

text: Editorial An introduction is presented in which the editor discusses various reports within the issue on topics including electronic learning tools, mathematical tasks of children in primary school settings, and teacher education.

year: 2011

title: Impact of Concomitant Surgical Atrial Fibrillation Ablation in Patients Undergoing Aortic Valve Replacement

authors: Yoo, Jae Suk

subjects: Science & Technology

abstract: Background: The clinical benefit of concomitant atrial fibrillation (AF) ablation at the time of aortic valve replacement (AVR) is uncertain.
Methods and Results: A total of 124 patients with AF who underwent AVR with (n=50) or without (n=74) a concomitant maze procedure, between 2000 and 2011, were evaluated. There were no significant differences in early postoperative outcomes. During a median clinical follow-up of 18.1 months (interquartile range: 6.9–47.8 months), 19 late deaths (15.3%) and 33 valve-related complications (26.6%) occurred, but the differences between groups were not statistically significant. Major event-free survival at 5 years was 60.9±9.9% vs. 57.0±10.3% (P=0.41). After adjustment, the maze group demonstrated similar risks for major adverse cardiac events (hazard ratio, 1.18; 95% confidence interval, 0.56–2.49; P=0.67). However, the rate of sinus rhythm restoration at 4 years was significantly higher in the maze group (80.6% vs. 3.6%, P<0.001). Left atrial dimension was smaller (46.9 vs. 50.4mm, P=0.017), and the ejection fraction was higher (60.6% vs. 58.0%, P=0.059) in the maze group. The rate of postoperative anticoagulation was also lower in the maze group (53.1% vs. 89.2%, P<0.001).
Conclusions: Concomitant AF ablation in patients undergoing AVR resulted in increased sinus rhythm restoration, better echocardiographic results, and decreased anticoagulation requirement, without increasing surgical morbidity or mortality.

text: Impact of Concomitant Surgical Atrial Fibrillation Ablation in Patients Undergoing Aortic Valve Replacement Background: The clinical benefit of concomitant atrial fibrillation (AF) ablation at the time of aortic valve replacement (AVR) is uncertain.
Methods and Results: A total of 124 patients with AF who underwent AVR with (n=50) or without (n=74) a concomitant maze procedure, between 2000 and 2011, were evaluated. There were no significant differences in early postoperative outcomes. During a median clinical follow-up of 18.1 months (interquartile range: 6.9–47.8 months), 19 late deaths (15.3%) and 33 valve-related complications (26.6%) occurred, but the differences between groups were not statistically significant. Major event-free survival at 5 years was 60.9±9.9% vs. 57.0±10.3% (P=0.41). After adjustment, the maze group demonstrated similar risks for major adverse cardiac events (hazard ratio, 1.18; 95% confidence interval, 0.56–2.49; P=0.67). However, the rate of sinus rhythm restoration at 4 years was significantly higher in the maze group (80.6% vs. 3.6%, P<0.001). Left atrial dimension was smaller (46.9 vs. 50.4mm, P=0.017), and the ejection fraction was higher (60.6% vs. 58.0%, P=0.059) in the maze group. The rate of postoperative anticoagulation was also lower in the maze group (53.1% vs. 89.2%, P<0.001).
Conclusions: Concomitant AF ablation in patients undergoing AVR resulted in increased sinus rhythm restoration, better echocardiographic results, and decreased anticoagulation requirement, without increasing surgical morbidity or mortality.

year: 2014-06

title: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources?

authors: Death, Russell G.

abstract: Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

text: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources? Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

year: 2017