Computational modeling aspects of photon transport equations in biological media

This paper presents an overview of modeling light propagation through biological media by solving the photon transport equation. Different variants of the photon transport equation (PTE) are discussed. Several methods for modeling static distributions and the transient response are presented. A discussion on how to mix and match electromagnetic problems with the PTE is also provided.

On the extraction of thematic and dramatic functions of content in educational videos

In this paper, we propose novel computational models for the extraction of high level expressive constructs related to, namely thematic and dramatic functions of the content shown in educational and training videos. Drawing on the existing knowledge of film theory, and media production rules and conventions used by the filmmakers. we hypothesize key aesthetic elements contributing to convey these functions of the content. Computational models to extract them are then formulated and their performance evaluated on a set of ten educational and training videos is presented.

Automated film rhythm extraction for scene analysis

This paper examines film rhythm, an important expressive element in motion pictures, based on our ongoing study to exploit film grammar as a broad computational framework for the task of automated film and video understanding. Of the many, more or less elusive, narrative devices contributing to film rhythm, this paper discusses motion characteristics that form the basis of our analysis, and presents novel computational models for extracting rhythmic patterns induced through a perception of motion. In our rhythm model, motion behaviour is classified as being either nonexistent, fluid or staccato for a given shot. Shot neighbourhoods in movies are then grouped by proportional makeup of these motion behavioural classes to yield seven high-level rhythmic arrangements that prove to be adept at indicating likely scene content (e.g. dialogue or chase sequence) in our experiments. Underlying causes for this level of codification in our approach are postulated from film grammar, and are accompanied by detailed demonstration from real movies for the purposes of clarification.

Reduced motor imagery efficiency is associated with online control difficulties in children with probable developmental coordination disorder

Recent evidence indicates that the ability to correct reaching movements in response to unexpected target changes (i.e., online control) is reduced in children with developmental coordination disorder (DCD). Recent computational modeling of human reaching suggests that these inefficiencies may result from difficulties generating and/or monitoring internal representations of movement. This study was the first to test this putative relationship empirically. We did so by investigating the degree to which the capacity to correct reaching mid-flight could be predicted by motor imagery (MI) proficiency in a sample of children with probable DCD (pDCD). Thirty-four children aged 8 to 12 years (17 children with pDCD and 17 age-matched controls) completed the hand rotation task, a well-validated measure of MI, and a double-step reaching task (DSRT), a protocol commonly adopted to infer one's capacity for correcting reaching online. As per previous research, children with pDCD demonstrated inefficiencies in their ability to generate internal action representations and correct their reaching online, demonstrated by inefficient hand rotation performance and slower correction to the reach trajectory following unexpected target perturbation during the DSRT compared to age-matched controls. Critically, hierarchical moderating regression demonstrated that even after general reaching ability was controlled for, MI efficiency was a significant predictor of reaching correction efficiency, a relationship that was constant across groups. Ours is the first study to provide direct pilot evidence in support of the view that a decreased capacity for online control of reaching typical of DCD may be associated with inefficiencies generating and/or using internal representations of action.

Peptide sequence effects control the single pot reduction, nucleation, and growth of Au nanoparticles

Peptides have demonstrated unique capabilities to fabricate inorganic nanomaterials of numerous compositions through noncovalent binding of the growing surface in solution. In this contribution, we demonstrate that these biomolecules can control all facets of Au nanoparticle fabrication, including Au3+ reduction, without the use of secondary reagents. In this regard using the AuBP1 peptide, the N-terminal tryptophan residue is responsible for driving Au3+ reduction to generate Au nanoparticles passivated by the oxidized peptide in solution, where localized residue context effects control the reducing strength of the biomolecule. The process was fully monitored by both time-resolved monitoring of the growth of the localized surface plasmon resonance and transmission electron microscopy. Nanoparticle growth occurs by a unique disaggregation of nanoparticle aggregates in solution. Computational modeling demonstrated that the oxidized residue of the peptide sequence does not impact the biomolecule's ability to bind the inorganic surface, as compared to the parent peptide, confirming that the biomolecule can be exploited for all steps in the nanoparticle fabrication process. Overall, these results expand the utility of peptides for the fabrication of inorganic nanomaterials, more strongly mimicking their use in nature via biomineralization processes. Furthermore, these capabilities enhance the simplicity of nanoparticle production and could find rapid use in the generation of complex multicomponent materials or nanoparticle assembly.

Machine learning for user modeling

At first blush, user modeling appears to be a prime candidate for straightforward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labeled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.

Modeling dialogue with mixed initiative in design space exploration

Exploration with a generative formalism must necessarily account for the nature of interaction between humans and the design space explorer. Established accounts of design interaction are made complicated by two propositions in Woodbury and Burrow's Keynote on design space exploration. First, the emphasis on the primacy of the design space as an ordered collection of partial designs (version, alternatives, extensions). Few studies exist in the design interaction literature on working with multiple threads simultaneously. Second, the need to situate, aid, and amplify human design intentions using computational tools. Although specific research and practice tools on amplification (sketching, generation, variation) have had success, there is a lack of generic, flexible, interoperable, and extensible representation to support amplification. This paper addresses the above, working with design threads and computer-assisted design amplification through a theoretical model of dialogue based on Grice's model of rational conversation. Using the concept of mixed initiative, the paper presents a visual notation for representing dialogue between designer and design space formalism through abstract examples of exploration tasks and dialogue integration.

Identifying opportunities and promoting discourse by modeling future development initiatives

Based on the development of case study research into the proposed rapid expansion of the Deakin University waterfront campus, this paper presents a computational approach to identifying and modelling future development initiatives within the urban context. The aim of the model is to augment cross-discipline discourse by generating an array of permutations that extend the range of potential design scenarios beyond the Iimits of an ad-hoc, piece-meal or discipline specific approach.

Microclimatic modeling of the urban thermal environment of Singapore to mitigate urban heat island

This study investigates the urban heat island effect in Singapore and examines the key factors causing this effect. The possibilities of improving heat extraction rate by optimizing air flow in selected hot spots were explored. The effect of building geometry, façade materials and the location of air-conditioning condensers on the outdoor air temperature was explored using computational fluid dynamics (CFD) simulations. It was found that at very low wind speeds, the effect of façade materials and their colours was very significant and the temperature at the middle of a narrow canyon increased up to 2.5 °C with the façade material having lower albedo. It was also found that strategically placing a few high-rise towers will enhance the air flow inside the canyon thereby reducing the air temperature. Adopting an optimum H/W ratio for the canyons increased the velocity by up to 35% and reduced the corresponding temperature by up to 0.7 °C.

Modeling of advanced high strength steels with the realistic microstructure–strength relationships

The objective of the work is to consider the first-order effects of the realistic microstructure morphology in the macroscale modeling of the multiphase Advanced High Strength Steels (AHSS). Instead of using constitutive equations at macroscale, the strength–microstructure relationship is studied in the forms of micromechanical and multiscale models that do not make considerable simplifications with regard to the microscale geometry and topology. The trade-off between the higher computational time and the higher accuracy has been offset with a stochastic approach in the construction of the microscale models. The multiphase composite effects of AHSS microstructure is considered in realistic microstructural models that are stochastically built from AHSS micrographs. Computational homogenization routines are used to couple micro and macroscale and resultant stress–strain relations are compared for models built with the simplified and idealized geometries of the microstructure. The results from this study show that using a realistic representation of the microstructure, either for DP or TRIP steel, could improve the accuracy of the predicted stress and strain distribution. The resultant globally averaged effective stress and strain fields from realistic microstructure model were able to accurately capture the onset of the plastic instability in the DP steel. It is shown that the macroscale mechanical behavior is directly affected by the level of complexities in the microscale models. Therefore, greater accuracy could be achieved if these stochastic realistic microstructures are used at the microscale models.

Modeling and simulation of a periodic grating coupled configuration for surface plasmon excitation

The deficiencies in the design of surface plasmon resonance (SPR) systems that are reported in numerous published works consistently identify the optics assembly as the main problem in the miniaturization of SPR sensors for integration into biosensor systems. This paper presents a novel design of a grating coupled optical waveguide surface plasmon (SP) excitation mechanism, investigated with the intention of addressing the problems associated with using the traditional prism input-output light coupling approach. Computational multiphysics modeling and simulation of the design is carried out. The results are presented and discussed.