Bluebird AR

"What is Bluebird AR? Bluebird AR was the ABC's alternate reality drama set around the leak of Bluebird, a clandestine geoengineering initiative created by eco-billionaire Harrison Wyld. Proposing a fictional scenario set against a backdrop of real world possibilities, Bluebird AR took some of the conventions of the well-established alternate reality game (ARG) genre and pulled them into the relatively new area of online drama, to create a hybrid entertainment form best described as 'participatory drama'. With Bluebird AR's interactive narrative centred on the experimental science of geoengineering, the deliberate manipulation of the Earth's atmosphere to counteract global warming, the events and characters in the Bluebird story were entirely fictional but fused with reality online. Inhabiting a mixture of third party social media spaces and websites created by the ABC, the story incorporated real online articles, scientific journals, media and debate around geoengineering. In an Australian first, ABC Innovation launched Bluebird AR on 27 April 2010, with a 6 week live phase. Audience members were invited to play collectively to help 'unlock the drama' and push forward the emerging narrative, or passively watch the story unfold in real-time across the internet. Bluebird AR subverted ARG conventions with the high quality of its production and assets, and raised the stakes for online drama with its level of audience participation." © 2014 ABC "Introduction One of the most exciting creative challenges of producing Bluebird AR was formulating the broad array of visual styles and treatments required for the project's diverse range of content. Many assets also needed to translate well not only online but across other media, including television and print. With the project's producers keen to create a visually rich narrative with high production values from the outset, inspiration for the production design for various aspects of the Bluebird story began in the earliest pitching phase in September 2008. Particular visual treatments and styles for Bluebird's characters, their web spaces and real world possessions were formulated concurrently with the creation of their profiles. Ideas around how various clues and gameplay spaces might look and feel were also explored at this early stage. Bluebird AR's small but tight creative team produced 7 website designs and brands, motion graphics for title sequences and logo animations, rotoscope animation, 3D compositing and animation, 3D wireframes and schematics, countless Photoshop composites, and a vast array of character assets for the DC (including Kyle's Bluebird Labs security pass and resignation letter, Kruger's American and Russia passports and birth certificate, Harrison's divorce papers, and more)…" © 2014 ABC

Enhancing sense of recovery and self-reflectivity in people with schizophrenia : a pilot study of Metacognitive Narrative Psychotherapy

Objective This study investigated the effectiveness of an innovative, manualized psychotherapy aimed at enhancing recovery and self-experience in people with schizophrenia, Metacognitive Narrative Psychotherapy. Design Treatment effects were assessed using a mixed methodology. Data were quantitatively assessed using a single sample, pre- and post-therapy design and qualitatively assessed using a case-study methodology. Methods Eleven patients diagnosed with schizophrenia received Metacognitive Narrative Psychotherapy over the course of 11 to 26 months. Therapists were seven supervised postgraduate psychology students. On average patients attended 49 sessions over the course of therapy. Patients completed interview-based and self-report measures for general and treatment-specific outcomes at pre-, mid-, and post-treatment. Results Quantitative analyses showed that patients significantly improved on the general outcome of subjective recovery, as well as the treatment-specific outcome of self-reflectivity, with medium to large effect sizes. Case-study evidence also showed improvements for some patients in symptom severity, and narrative coherence and complexity. Conclusions These results are consistent with previous case-study evidence and suggest that this manualized version of Metacognitive Narrative Psychotherapy produces general and approach-specific improvements for people with schizophrenia. Replication is needed to ascertain its effectiveness with a larger sample size and within a controlled design.

Triumphs and tribulations in the psychotherapy of schizophrenia : reflections on a pilot study of metacognitive narrative psychotherapy

Schizophrenia results in a profound disruption of one’s capacity to make sense of mental states, coherently narrate self-experiences, and meaningfully relate to others. While current treatment options for people with schizophrenia tend to be symptom-focused, experience in designing and implementing a study focusing on enhancing sense of self demonstrates the feasibility of developing and implementing models of treatment that prioritize the subjective distress and self-experience of people with schizophrenia. There is emerging research evidence, based upon dialogical theory of self, that posits the potential of people with deficits of self to engage in meaningful therapeutic relationships and work toward greater integrity of self and degrees of recovery. The challenge is to translate these ideas into a research methodology that can be successfully applied within therapeutic contexts with people who meet the diagnostic criteria for schizophrenia. Based upon dialogical theory, we developed a principle-based manual for metacognitive narrative psychotherapy: a psychological approach to the treatment of people with schizophrenia, which aims to enhance metacognitive capacity and ability to narrate self-experiences. Five phases of treatment were identified: (1) developing a therapeutic relationship, (2) eliciting narratives, (3) enhancing metacognitive capacity, (4) enriching narratives, and (5) living enriched stories. Proscribed practices were also identified. We then implemented the manual within a university clinic context. Six therapists were trained to implement the model and, in turn, provided therapy to 11 patients who completed 12 to 24 months of treatment. Participants were assessed on metacognitive capacity, narrative coherence, narrative richness, self-reported recovery, and symptomatology at three points in time over the course of therapy. Contrary to expectations, participants were highly engaged in the therapeutic process, with minimal dropout. Overall, over 75% of participants evidenced improvement in their level of recovery over the course of therapy. The manualization and outcome findings demonstrate the feasibility of applying such interventions to a broader clinical population.

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Narrative and multimodality in English language arts curricula : a tale of two nations

In this policy column within this special edition on "The Arts in Language Arts", we critique the current place of multimodality and narratives in research and curriculum policy. This is a vital issue of significance for literacy educators, researchers, and policy makers because the narrative texts that circulate in our everyday lives are multimodal, tied to the ever-broadening range of narratives forms in digital sites of display. Here, we critically evaluate the place of multimodality and narratives in the language arts or English curriculum policies of two nations, the USA and Australia. In particular, we highlight the silence on multimodality within the Common Core State Standards, USA, and the contrasting centrality of multimodality in the National Curriculum: English, Australia.

Hybrid chalcogenide nanoparticles : 2D-WS2 nanocrystals inside nested WS2 fullerenes

The MOCVD assisted formation of nested WS2 inorganic fullerenes (IF-WS2) was performed by enhancing surface diffusion with iodine, and fullerene growth was monitored by taking TEM snapshots of intermediate products. The internal structure of the core-shell nanoparticles was studied using scanning electron microscopy (SEM) after cross-cutting with a focused ion beam (FIB). Lamellar reaction intermediates were found occluded in the fullerene particles. In contrast to carbon fullerenes, layered metal chalcogenides prefer the formation of planar, plate-like structures where the dangling bonds at the edges are stabilized by excess S atoms. The effects of the reaction and annealing temperatures on the composition and morphology of the final product were investigated, and the strength of the WS2 shell was measured by intermittent contact-mode AFM. The encapsulated lamellar structures inside the hollow spheres may lead to enhanced tribological activities.

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

Partial shading and rapidly changing irradiance conditions significantly impact on the performance of photovoltaic (PV) systems. These impacts are particularly severe in tropical regions where the climatic conditions result in very large and rapid changes in irradiance. In this paper, a hybrid maximum power point (MPP) tracking (MPPT) technique for PV systems operating under partially shaded conditions witapid irradiance change is proposed. It combines a conventional MPPT and an artificial neural network (ANN)-based MPPT. A low cost method is proposed to predict the global MPP region when expensive irradiance sensors are not available or are not justifiable for cost reasons. It samples the operating point on the stairs of I–V curve and uses a combination of the measured current value at each stair to predict the global MPP region. The conventional MPPT is then used to search within the classified region to get the global MPP. The effectiveness of the proposed MPPT is demonstrated using both simulations and an experimental setup. Experimental comparisons with four existing MPPTs are performed. The results show that the proposed MPPT produces more energy than the other techniques and can effectively track the global MPP with a fast tracking speed under various shading patterns.

Hybrid CuTCNQ/AgTCNQ metal-organic charge transfer complexes via galvanic replacement vs. corrosion recrystallization

This study reports a hybrid of two metal-organic semiconductors that are based on organic charge transfer complexes of 7,7,8,8-tetracyanoquinodimethane (TCNQ). It is shown that the spontaneous reaction between semiconducting microrods of CuTCNQ with Ag+ ions leads to the formation of a CuTCNQ/AgTCNQ hybrid, both in aqueous solution and acetonitrile, albeit with completely different reaction mechanisms. In an aqueous environment, the reaction proceeds by a complex galvanic replacement (GR) mechanism, wherein in addition to AgTCNQ nanowires, Ag0 nanoparticles and Cu(OH)2 crystals decorate the surface of CuTCNQ microrods. Conversely, in acetonitrile, a GR mechanism is found to be thermodynamically unfavorable and instead a corrosion-recrystallization mechanism leads to the decoration of CuTCNQ microrods with AgTCNQ nanoplates, resulting in a pure CuTCNQ/AgTCNQ hybrid metal-organic charge transfer complex. While hybrids of two different inorganic semiconductors are regularly reported, this report pioneers the formation of a hybrid involving two metal-organic semiconductors that will expand the scope of TCNQ-based charge transfer complexes for improved catalysis, sensing, electronics and biological applications.

A hybrid quality function deployment and cybernetic analytic network process model for project manager selection

Identifying appropriate decision criteria and making optimal decisions in a structured way is a complex process. This paper presents an approach for doing this in the form of a hybrid Quality Function Deployment (QFD) and Cybernetic Analytic Network Process (CANP) model for project manager selection. This involves the use of QFD to translate the owner's project management expectations into selection criteria and the CANP to weight the expectations and selection criteria. The supermatrix approach then prioritises the candidates with respect to the overall decision-making goal. A case study is used to demonstrate the use of the model in selecting a renovation project manager. This involves the development of 18 selection criteria in response to the owner's three main expectations of time, cost and quality.

A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R2 goodness of fit of 0.9994 and 0.9982 respectively over a 10 h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.