Wicked Problems in Interface Design: Reflections on the Theories and Practices of Remediation

This project sets out to investigate the ways in which media technology designers might be helped through employing frameworks of understanding from the field of media studies. The overarching goal of this study is to probe the contributory matrix of factors that play a role in the development of a technology interface and its usage. This goal is worked upon by tracing the diverse factors that come into play while remediating a pre-existing medium like oral storytelling onto a digital media interface. Drawing from relevant media theories on determinism and remediation, the transactional linkage between media technology evolution and socio-cultural conventions is established through the juxtaposition of technological determinism and social-constructivism. This linkage is thereafter used to question the conventional paradigm in design practices where the underlying role of pre-existing media technologies and media cultures in the shaping of a new media interface and its usage is overlooked. This assertion about design practices is then explored through an interface development initiative where oral storytelling practices that employed orality as the dominant medium were remediated into a digital interface through developing a virtual 3D avatar who tells a story to the user. Both eye-tracking technology and questionnaires were used as measurement tools to gauge the users’ response to the interface. Exposing the underlying complexity inherent in the process of remediation was guided by the paradigm of ‘wicked problems’ in design thinking. The methodological stance for this study also drew inspiration from the idea of social-constructivism which suggests chronicling the underlying socio- cultural factors that affect the development and use of a new media technology. The features of orality, literacy and virtuality media cultures as laid out by medium theorists were understood as cultural variables whose underlying effects on the development and the reception of the new media interface were explored. The findings of the study indicate that the conventional assumptions about ‘immersion’ and ‘distraction’ in a real-life oral storytelling performance are inadequate to explain the user responses when the oral storyteller is remediated into a digital interface. The wickedness in remediation design problems is compounded by the fact that user reception of a given media technology and the content that is conveyed through it may vary with time. The reception of a media technology interface is shaped indirectly by the emerging conventions of the virtual culture and also by the pre-existing cultures. Media technology designers need to use a broader matrix of analysis where their assumptions and results are positioned within a continuum of media evolution that has multiple socio-technical dimensions as contributory factors.Thesis (Ph.D.) -- University of Adelaide, School of Humanities, 201

Analytical detection of stationary and dynamic patterns in a prey-predator model with reproductive Allee effect in prey growth

Allee effect in population dynamics has a major impact in suppressing the paradox of enrichment through global bifurcation, and it can generate highly complex dynamics. The influence of the reproductive Allee effect, incorporated in the prey's growth rate of a prey-predator model with Beddington-DeAngelis functional response, is investigated here. Preliminary local and global bifurcations are identified of the temporal model. Existence and non-existence of heterogeneous steady-state solutions of the spatio-temporal system are established for suitable ranges of parameter values. The spatio-temporal model satisfies Turing instability conditions, but numerical investigation reveals that the heterogeneous patterns corresponding to unstable Turing eigen modes acts as a transitory pattern. Inclusion of the reproductive Allee effect in the prey population has a destabilising effect on the coexistence equilibrium. For a range of parameter values, various branches of stationary solutions including mode-dependent Turing solutions and localized pattern solutions are identified using numerical bifurcation technique. The model is also capable to produce some complex dynamic patterns such as travelling wave, moving pulse solution, and spatio-temporal chaos for certain range of parameters and diffusivity along with appropriate choice of initial conditions Judicious choices of parametrization for the Beddington-DeAngelis functional response help us to infer about the resulting patterns for similar prey-predator models with Holling type-II functional response and ratio-dependent functional response

The Causal Nexus between Public Debt and Economic Growth, A Multivariate Time Series Analysis: Experience from a SAARC Nation

This research paper enquires about the topicality of the dependence of Bangladesh s economy on public debt Several authors examined the bearings of public debt and economic growth in different counties and they provided mixed results about the direction of the relation This study is conducted to find out the causal relationship between public debt and growth from the perspective of Bangladesh s economy and we use export as a control variable We excerpted annual time series data from the World Bank website WDI IMF and fiscal year 1986 to2018 data were gathered One can treat budget deficit as the mother of public debt because the incarnation of the former usually precedents to the creation of the later Several econometric tools have been behaved as Augmented Dickey-Fuller ADF Phillips Peron PP Johansen co integration Vector error correction model and Granger casualty to explore short-run causality of public debt on growt

Short Run and Long Run Associationship between Real Exchange Rate and Trade Balance: Empirical Evidence from Bangladesh (Johansen Approach and Vector Error Correction Model)

Several studies have tested the j curve phenomenon for Australia Japan South Korea New Zealand and many other countries using non stationary time series data and have provided mixed results They not only suffer from the aggregation bias problem but also the spurious regression problem To overcome this problem in this paper we investigate the short run and long-run effects of real depreciation of the Bangladeshi taka to the trade balance between Bangladesh and her trading partners In this article first we check the stationary of data set and find the stationary applying the Augmented Dickey Fuller test then applying the Johansen co integration test in order to find out the long run co integrated equations and last of all try to investigate the short run and long run relationship among the variables while we use the VECM vector error correction model and found that there is long run associations among the variables and short-run coefficients are statistically insignificant But for Bangladesh j curve concept have not been tested yet That s why we have chosen this topic and we incorporated several others variables to test the linkages on trade balance such as GNI as a proxy of GDP inflation rate NODA net officials development assistance and we have given more priory on the variable real exchange rat

Impact of Biological Factors Related to Maternal Aging: Risk of Childbirth with Down Syndrome

Maternal aging and different biological factors play an important role in the birth of Down syndrome baby. Hormones play a crucial role for the maintenance of female sex cycle and oocyte maturation. Disparity in the level of these hormones during menstrual cycle has profound effect on female reproductive system. Hormonal imbalance also affects meiotic process and integrity of spindle structure and leads to nondisjunction of chromosome. Follicle-stimulating hormone (FSH), anti-Müllerian hormone (AMH) and luteinizing hormone (LH) play a crucial role in ovarian aging and nondisjunction of chromosomes. FSH stands as a hormonal indicator for ovarian aging, and its high level is responsible for aneuploid birth. Advanced chronological age of mother, ovarian aging, environmental factors and accelerated telomere shortening at older reproductive age are found to be risk factors for the birth of trisomy 21 Down syndrome

Authentic input to motivate learners to develop listening skill: benefits, drawbacks of using podcast

This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Arts in English, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (page 50-55).In Bangladesh, mobile devices are distinctly gaining the importance in education section. While CD-ROMs, multimedia computer labs, the World Wide Web, e-mail, and SMS still play an important part in language learning, research into the use of Web 2.0 technologies and Mobile Assisted Language Learning (MALL) have become increasingly common (Fitzgerald et al., 2009 p. 19 ). As the curriculum does not provide a lot of opportunities to develop listening skill to the learners in their secondary and higher secondary level of education, in their higher education they have to face so many difficulties. As classrooms are mainly conducted in Bangla and students do not have much opportunities to communicate with native speakers of their target language, podcast can fulfill this gap with a very low cost. The popularization of portable devices in language teaching means that there is a wealth of language learning resources like podcasts are freely available to learners who may download them and use them anywhere at any time. However, before effectively integration of podcast in listening skill development, it is important to concern about necessary authentic input for motivation, benefits and drawbacks of using podcasts. This study conducted among 40 students of different colleges of Bangladesh to find out motivational factor, benefits and drawbacks of using podcasts. The data gathered from the learners showed that the podcast can motivate learners to develop their listening skill and podcasts can provide huge opportunities to develop listening skill and other skills simultaneously.Subrata Chandra DeyM.A. in Englis

PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data

With calls for increasing transparency, governments are releasing greater amounts of data in multiple domains including finance, education and healthcare. The efficient exploratory analysis of healthcare data constitutes a significant challenge. Key concerns in public health include the quick identification and analysis of trends, and the detection of outliers. This allows policies to be rapidly adapted to changing circumstances. We present an efficient outlier detection technique, termed PIKS (Pruned iterative-k means searchlight), which combines an iterative k-means algorithm with a pruned searchlight based scan. We apply this technique to identify outliers in two publicly available healthcare datasets from the New York Statewide Planning and Research Cooperative System, and California's Office of Statewide Health Planning and Development. We provide a comparison of our technique with three other existing outlier detection techniques, consisting of auto-encoders, isolation forests and feature bagging. We identified outliers in conditions including suicide rates, immunity disorders, social admissions, cardiomyopathies, and pregnancy in the third trimester. We demonstrate that the PIKS technique produces results consistent with other techniques such as the auto-encoder. However, the auto-encoder needs to be trained, which requires several parameters to be tuned. In comparison, the PIKS technique has far fewer parameters to tune. This makes it advantageous for fast, "out-of-the-box" data exploration. The PIKS technique is scalable and can readily ingest new datasets. Hence, it can provide valuable, up-to-date insights to citizens, patients and policy-makers. We have made our code open source, and with the availability of open data, other researchers can easily reproduce and extend our work. This will help promote a deeper understanding of healthcare policies and public health issues

Building predictive models of healthcare costs with open healthcare data

Due to rapidly rising healthcare costs worldwide, there is significant interest in controlling them. An important aspect concerns price transparency, as preliminary efforts have demonstrated that patients will shop for lower costs, driving efficiency. This requires the data to be made available, and models that can predict healthcare costs for a wide range of patient demographics and conditions. We present an approach to this problem by developing a predictive model using machine-learning techniques. We analyzed de-identified patient data from New York State SPARCS (statewide planning and research cooperative system), consisting of 2.3 million records in 2016. We built models to predict costs from patient diagnoses and demographics. We investigated two model classes consisting of sparse regression and decision trees. We obtained the best performance by using a decision tree with depth 10. We obtained an R-square value of 0.76 which is better than the values reported in the literature for similar problems.Comment: 2020 IEEE International Conference on Healthcare Informatics (ICHI