WaterWorks: a decision support tool for irrigation infrastructure decisions at farm level

Increasing water scarcity, climate change and pressure to provide water for environmental flows urge irrigators to be more efficient. In Australia, ongoing water reforms and most recent National Water Security Plan offer incentives to irrigators to adjust their farming practices by adopting water saving irrigation infrastructures to match with soils, crop and climatic conditions. WaterWorks is a decision support tool to facilitate irrigators to make long and short term irrigation infrastructure investment decision at the farm level. It assists irrigators to improve the economic efficiency, water use efficiency and environmental performance of their farm businesses. The WaterWorks has been tested, validated and accepted by the irrigation community and reachers in NSW. The interface of WaterWorks is user-friendly and flexible. The simulation and optimisation module in WaterWorks provides an opportunity to evaluate infrastructure investment decisions to suit their seasonal or long-term water availability. The sensitivity analysis allows substantiating the impact of major variables. Net present value, internal rate of return, benefit cost ratio and payback period are used to analyse the costs and benefits of modern irrigation technology. Application of WaterWorks using a whole farm-level case study indicates its effectiveness in making long term and short term investment decisions. The WaterWorks can be easily integrated into commercial software such as spreadsheets, GIS, real time data acquisition and control systems to further enhance its usability. The WaterWorks can also be used in regional development plannin

Removing commissions does not restore impartial advice

Zhuoqiong Chen, PhD student in Business Economics, experimentally analyses that removing commissions in the financial services does not restore impartial advice

apk2vec: Semi-supervised multi-view representation learning for profiling Android applications

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semi-supervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for largescale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.Comment: International Conference on Data Mining, 201

Promoting the Use of Online Social Technology as a Case-based Learning Tool

Social technology is proliferating and influencing different aspects of society. However, very few studies have examined the use of such a technology for a case-based learning pedagogy. This preliminary study investigates the use of social technology as a case-based learning tool to improve the effectiveness of case-based learning in the classroom. A total of 116 students in a public university in Thailand were formed into teams and spent two weeks discussing a Harvard business case via the social technology Edmodo. After the experiment, an online survey was conducted with these participants to assess the efficacy of using Edmodo for solving this case. The Task-Technology Fit (TTF) theory was used to assess the impact of case-based learning and the tasks that the students had to carry out. The findings of this preliminary study suggest that the TTF theory could be used as an effective theory to help better understand not only the user behaviour but also the usefulness of online social technology as a case-based learning tool. However, the theory may not be able to fully capture the complexity of online social technology adoption in the case-based learning context. Theoretical and practical implications are drawn from the findings of this preliminary study

The Efficacy of Social Learning Technology on Case-based Learning: A Task-Technology Perspective

The use of social technology in classroom has shown various results. This paper focuses on using social technology as a case-based learning tool. A total of 116 students in a public university in Thailand were formed into teams, and spent two weeks in discussing a Harvard business case via the social technology; Edmodo. After the experiment, an online survey is conducted with these participants to assess the individual learning performance in case-based learning via social technology. Task-technology fit (TTF) was also used to assess the impact on learning performance and the tasks that the students perform by using Edmodo as a learning tool. Our findings suggest that social technology be used as a fit learning tool to improve students’ understanding of business cases. We concluded that the higher perceived task-technology fit for the social technology, the better learning performance in both near and far knowledge for the students

Improving the Trust of Users on Social Networking Sites via Self-Construal Traits

The ability to remove trust concerns for online users is crucial for sustainable online development, especially relating to social networking sites. This study examines independent self-construal and interdependent self-construal as pertinent factors to increase trust on social networking sites. The classification of trust broken down into calculation-, familiarity-, structural assurance-, and situational normality-based trust was adopted in this study. Data was collected from 398 members of the leading social network site: Facebook. Regression analysis was adopted to test the data against the casual relationship among the four trust constructs. Data analysis indicates that the constructs of interdependent self-construal and independent selfconstrual individually, and together, can account for the increase of trust on a social networking site; however interdependent self-construal has the largest explanatory power. These results suggest that social networking sites continuously increase the degree of interdependence of users and develop new applications to engage users to stay longer for each visit. As a result of these measures, social networking sites can sustain the trust of users

Structural and functional investigation of the trabecular outflow pathway

Primary open-angle glaucoma (POAG) is a leading cause of blindness in the world. A primary risk factor for POAG is elevated intraocular pressure (IOP), caused by increased aqueous humor outflow resistance. Currently, lowering the IOP is the only effective way of treating glaucoma; however, the cause of increased outflow resistance remains unclear. This thesis will present a series of studies which investigated structures of the trabecular outflow pathway, including Schlemm’s canal endothelium, juxtacanalicular tissue, and trabecular beams, and their roles in regulating aqueous outflow resistance. The studies were conducted in both human and animal models using ex vivo ocular perfusion as well as in vitro microfluidic systems. In the first study, we investigated the effects of Y27632, a derivative of Rho-kinase inhibitor that is being developed as next generation glaucoma drug with unclear IOP lowering mechanism, on aqueous humor outflow dynamics and associated morphological changes in normal human eyes and laser-induced ocular hypertensive monkey eyes. In the second study, we developed and validated a novel three-dimensional microfluidic system using lymphatic microvascular endothelial cells. The microfluidic system can be used to study Schlemm’s canal endothelial cell dynamics and aqueous humor transport mechanism in the future. In the last study, we characterized the morphological structure, distribution, and thickness of the endothelial glycocalyx in the aqueous humor outflow pathway of human and bovine eyes. Together these studies will help define new directions for therapy that will help control IOP and preserve vision throughout a normal life span

Length of Cloud Application Use on Functionality Expectation, Usability, Privacy, and Security: A Case of Google Docs

Background: Newcomers to cloud applications typically have to overcome concerns of privacy (confidentiality control) and security (safekeeping). On the one hand, end-users may be accustomed to cloud applications’ privacy and security (habituation). On the other hand, these applications quickly adapt to end-user needs on those concerns (reciprocal habituation). Does the old proverb “Custom makes all things easy” apply to privacy and security concerns about cloud application use? Method: This study focuses on Google Docs as an example of standardized, common cloud applications and collects data from 211 of its users. Results: The results show that length of use has significant associations with better usability perception and increased functionality expectation. In turn, improved usability perception leads to decreased security risk concern, while increased functionality expectation increases privacy concerns. Interestingly, usefulness perception is not influenced by privacy concern. Conclusions: Overall, the length of Google Docs use is associated with higher usefulness and increased adoption through greater usability and decreased security concern. Thus, when it comes to standardized, common cloud applications, the old proverb is valid with some exception. Also, “custom” is mutual between cloud applications and their users. Available at: https://aisel.aisnet.org/pajais/vol11/iss3/2

Affective and Social Factors Influencing the Continuance Intention of Using Social Technology for the Case-based Learning

The proliferation of social technology poses both a threat and an opportunity for the delivery of traditional case method learning in business schools. This paper extends the expectation confirmation model (ECM) to examine the possibility of delivering the case method learning via social technology. A total of 90 students in a public university in Thailand were formed into teams, and spent two weeks in discussing a Harvard business case via the social technology Edmodo. After the experiment, an online survey is conducted with these participants to assess the influence of affective and social factors on their continuance intention. Our regression analysis shows that in addition to affective factors the social factor of information and knowledge sharing can help improve the accuracy of predicting a student’s continuance intention of using social technology in case method learning. The analysis result leads to theoretical and empirical findings for business schools to consider adopting social technology as the next-generation tool for case method teaching