Costumes by Computer

Thanks to a new form of computer technology, Iowa State\u27s theatre productions this year have been one step ahead of Broadway. Joseph Kowalski, an Iowa State design professor, is using a new program to design costumes for the stage

Reinforcement Learning and Game Theory for Smart Grid Security

This dissertation focuses on one of the most critical and complicated challenges facing electric power transmission and distribution systems which is their vulnerability against failure and attacks. Large scale power outages in Australia (2016), Ukraine (2015), India (2013), Nigeria (2018), and the United States (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. These incidents clearly indicate the necessity of extensive research efforts to protect the power system from external intrusion and to reduce the damages from post-attack effects. We analyze the vulnerability of smart power grids to cyber and physical attacks and failures, design different gametheoretic approaches to identify the critical components vulnerable to attack and propose their associated defense strategy, and utilizes machine learning techniques to solve the game-theoretic problems in adversarial and collaborative adversarial power grid environment. Our contributions can be divided into three major parts:Vulnerability identification: Power grid outages have disastrous impacts on almost every aspect of modern life. Despite their inevitability, the effects of failures on power grids’ performance can be limited if the system operator can predict and identify the vulnerable elements of power grids. To enable these capabilities we study machine learning algorithms to identify critical power system elements adopting a cascaded failure simulator as a threat and attack model. We use generation loss, time to reach a certain percentage of line outage/generation loss, number of line outages, etc. as evaluation metrics to evaluate the consequences of threat and attacks on the smart power grid.Adversarial gaming in power system: With the advancement of the technologies, the smart attackers are deploying different techniques to supersede the existing protection scheme. In order to defend the power grid from these smart attackers, we introduce an adversarial gaming environment using machine learning techniques which is capable of replicating the complex interaction between the attacker and the power system operators. The numerical results show that a learned defender successfully narrows down the attackers’ attack window and reduce damages. The results also show that considering some crucial factors, the players can independently execute actions without detailed information about each other.Deep learning for adversarial gaming: The learning and gaming techniques to identify vulnerable components in the power grid become computationally expensive for large scale power systems. The power system operator needs to have the advanced skills to deal with the large dimensionality of the problem. In order to aid the power system operator in finding and analyzing vulnerability for large scale power systems, we study a deep learning technique for adversary game which is capable of dealing with high dimensional power system state space with less computational time and increased computational efficiency. Overall, the results provided in this dissertation advance power grids’ resilience and security by providing a better understanding of the systems’ vulnerability and by developing efficient algorithms to identify vulnerable components and appropriate defensive strategies to reduce the damages of the attack

Getting evidence into practice: an investigation of the use and understanding of evidence-based practice by general dental practitioners in the West Midlands

OBJECTIVES: To determine what factors cause dental practitioners to change their practice To investigate the barriers to the successful application of research evidence to dental health care. To make recommendations for future action in light of the results of this study. DESIGN: Qualitative analysis of semi-structured interviews. In-depth interviews using a topic guide were utilised to identify current levels of knowledge and use of evidence-based dental practice amongst dental practitioners. SUBJECTS: A purposive sample of 31 primary dental care practitioners in the West Midlands. RESULTS: Advice from colleagues and respected teachers (“trusted sources”) were drivers for changing practice along with clinical guidelines from respected sources. It was clear that understanding of concepts in evidence-based dentistry (EBD) was limited. There appears to be a need to improve accessibility of evidence and to provide this in a format that practitioners find easily digestible. Common barriers to application of EBD included self-confidence in dentists own skills, NHS legislation and policy, organisational constraints and a lack of knowledge of critical appraisal CONCLUSIONS: Responses highlight a relative gap between the evangelism of evidence-based dentistry and its impact at a grass-roots level. It appears necessary to change the format and availability of evidence if dental practitioners are to maintain contemporary practice with evidence based interventions. The current climate in primary dental care does not appear to favour an evidence-based approach to determining patients’ dental care

Information, Employment, and Settlement of Immigrants: Exploring the Role of Information Behaviour in the Settlement of Bangladesh Immigrants in Canada

Immigrants shape Canada’s future in terms of innovation, population, and economic growth. Immigrants need information before and after arrival to make informed decisions about their move and for satisfactory settlement. Although Canada regularly welcomes immigrants with diverse socioeconomic backgrounds, very little is known about the settlement information behaviour of immigrants. This doctoral study investigates the transitional information behaviour of Bangladeshi immigrants in Canada. It uses mixed methods to explore the information experiences of Bangladeshi immigrants in pre- and post-arrival contexts and features the role information plays in newcomers’ employment. Bangladeshi immigrants who arrived in Canada between 1971 and 2017 were recruited for 60 semi-structured interviews and 205 surveys. Participants reported requiring a broad array of information in pre- and post-arrival contexts and they consulted various information sources to gather information about their host country. Pre-arrival assumptions about life in Canada shaped participants’ transitional information behaviour, sometimes resulting in a profound mismatch between expectations and the reality of their new lives. Employment is a central settlement concern and there is evidence that purposeful, strategic information seeking can mitigate much anxiety about post-arrival job-seeking and employment. My study also explores a paradoxical finding regarding the role of immigrants’ social networks revealing that when some immigrants consult their most trusted sources – friends, family, and ethnic community members – there are not always good outcomes. I put forward two new concepts: information sharing fear and information intelligence. Information sharing fear describes the phenomenon in which immigrants do not share information about the reality of life in Canada, including its challenges, for fear of being perceived to be discouraging. Information intelligence describes the ways in which some newcomers cultivate and use their various informational, social, and emotional competencies to gather a comprehensive picture of life before arrival resulting in better settlement preparations and experiences. Overall, the study highlights the information behaviour of newcomers in a new country with a particular focus on the role of information in settlement processes. It ends with a call for further research on exploring the complex, culturally situated information behaviour of immigrants

P04. Settlement Information Needs and Services: A Pilot Study with Bangladeshi Immigrant Women in Canada

Background: This study was conducted to gain a deeper understanding of the information seeking behaviour of Bangladeshi immigrant women in Canada. Methods: In February 2015, semi-structured interviews were conducted with twenty-two women residing in the Greater Toronto Area to investigate the everyday life information practices, settlement information needs, and barriers faced accessing and using various information. Results: Participants needed various pre-arrival and post-arrival information including information about important documents to bring, mental preparedness, employment information, childcare, and health. There was evidence of a high dependency on informal information networks (e.g., family members, friends) to meet diverse information needs. The study highlights the importance of offering a program of need-based settlement information services to these newcomers of Canada. The study presents a preliminary model on the connection between everyday life information practices of immigrants and their social integration and settlement. Discussion and Conclusion: Despite the broad spectrum of services and programs available to the research participants, their accounts of immigration and settlement are filled with gaps in knowledge – gaps that could be readily filled with the provision of timely, need-based information that is accessible at the point of need. The pilot study further suggests that the provision of everyday information to help women “get on with their lives” has profound consequences for quality of life in Canada. Interdisciplinary Reflection: The burgeoning interdisciplinary research on migration, immigration and settlement would benefit from consideration of the contributions information studies makes to understanding how newcomers, immigrants, and refugees deal with information in their lives

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

Expectations of Canadian Life, Actual Post-Arrival Experience, and Pre-Arrival Information Seeking: Results from a Study on Bangladeshi Immigrants in Canada

In this poster, I present findings from my doctoral study on the transitional information practices of Bangladeshi immigrants in Ontario, focusing on the tensions between pre-arrival expectations and actual experiences of Canadian life and on pre-arrival information practices. I conducted surveys (n=205) and semi-structured interviews (n=58) to understand my participants’ settlement information practices. It is evident in my study that there is a significant gap in Bangladeshi immigrants’ expectations of Canadian life and actual post-arrival experience that can be analyzed in terms of information experience, especially in terms of employment expectations and mental preparedness for changes in life circumstances