An approach to search and exploration through mixed-initiative

Generative design environments need support for human intervention as well as sound computational formalisms. A systematic approach to integrating the two, formal generation and the exploratory, is lacking. In this paper, we posit the possibility of a design support system that combines formal search with user driven exploration. Our approach is to cast the interaction between the user and the generative formalism as agent collaboration in a mixed-initiative environment. We describe the role of interaction and agency in an experimental mixed-initiative design support system, FOLDS and demonstrate its application.

Using bioinformatics techniques for gene identification in drug discovery and development

As more and more evidence has become available, the link between gene and emergent disease has been made including cancer, heart disease and parkinsonism. Analyzing the diseases and designing drugs with respect to the gene and protein level obviously help to find the underlying causes of the diseases, and to improve their rate of cure. The development of modern molecular biology, biochemistry, data collection and analysis techniques provides the scientists with a large amount of gene data. To draw a link between genes and their relation to disease outcomes and drug discovery is a big challenge: How to analyze large datasets and extract useful knowledge? Combining bioinformatics with drug discovery is a promising method to tackle this issue. Most techniques of bioinformatics are used in the first two phases of drug discovery to extract interesting information and find important genes and/or proteins for speeding the process of drug discovery, enhancing the accuracy of analysis and reducing the cost. Gene identification is a very fundamental and important technique among them. In this paper, we have reviewed gene identification algorithms and discussed their usage, relationships and challenges in drug discovery and development.

A model of price discovery and market design : theory and empirical evidence

An essential function of derivative markets is price discovery. A model is proposed to incorporate a comprehensive dynamic interaction between price size coordinates of orders and trades. An example of application of the model and its effect on price discovery is discussed.

Study and exploration on inorganic nanomaterials as additive in lubricant application.

 In this thesis, the application of planetary ball milling for the efficient production of nanomaterials is systematically studied. Three inorganic materials: calcium carbonate (CaCO3), molybdenum disulphide (MoS2) and hexagonal-boron nitride (h-BN) are chosen as model systems.

Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

The spectrum nature and heterogeneity within autism spectrum disorders (ASD) pose as a challenge for treatment. Personalisation of syllabus for children with ASD can improve the efficacy of learning by adjusting the number of opportunities and deciding the course of syllabus. We research the data-motivated approach in an attempt to disentangle this heterogeneity for personalisation of syllabus. With the help of technology and a structured syllabus, collecting data while a child with ASD masters the skills is made possible. The performance data collected are, however, growing and contain missing elements based on the pace and the course each child takes while navigating through the syllabus. Bayesian nonparametric methods are known for automatically discovering the number of latent components and their parameters when the model involves higher complexity. We propose a nonparametric Bayesian matrix factorisation model that discovers learning patterns and the way participants associate with them. Our model is built upon the linear Poisson gamma model (LPGM) with an Indian buffet process prior and extended to incorporate data with missing elements. In this paper, for the first time we have presented learning patterns deduced automatically from data mining and machine learning methods using intervention data recorded for over 500 children with ASD. We compare the results with non-negative matrix factorisation and K-means, which being parametric, not only require us to specify the number of learning patterns in advance, but also do not have a principle approach to deal with missing data. The F1 score observed over varying degree of similarity measure (Jaccard Index) suggests that LPGM yields the best outcome. By observing these patterns with additional knowledge regarding the syllabus it may be possible to observe the progress and dynamically modify the syllabus for improved learning.

Industrial lab-on-a-chip: design, applications and scale-up for drug discovery and delivery

Microfluidics is an emerging and promising interdisciplinary technology which offers powerful platforms for precise production of novel functional materials (e.g., emulsion droplets, microcapsules, and nanoparticles as drug delivery vehicles- and drug molecules) as well as high-throughput analyses (e.g., bioassays, detection, and diagnostics). In particular, multiphase microfluidics is a rapidly growing technology and has beneficial applications in various fields including biomedicals, chemicals, and foods. In this review, we first describe the fundamentals and latest developments in multiphase microfluidics for producing biocompatible materials that are precisely controlled in size, shape, internal morphology and composition. We next describe some microfluidic applications that synthesize drug molecules, handle biological substances and biological units, and imitate biological organs. We also highlight and discuss design, applications and scale up of droplet- and flow-based microfluidic devices used for drug discovery and delivery.

Price discovery and asset pricing

This paper tests the hypothesis that price discovery influences asset pricing. Our innovations are twofold. First, we estimate time-varying price discovery for a large number (21) of Islamic stock portfolios. Second, we test using a predictive regression model whether or not price discovery predicts stock excess returns. We find from both in-sample and out-of-sample tests that all 21 portfolio excess returns are predictable. We show that a mean-variance investor by tracking price discovery is able to devise profitable trading strategies.

Jungle, desert, ice

Between 1884 and 1920 the Society was part of an intense period of international geographical interest, imperialism and nationalism. This empirical history shows that the Society sought to explore New Guinea, Northern and Central Australia, and Antarctica, pursuing issues of land use, labour and commercial development of these regions.