Model-order selection in Zernike polynomial expansion of corneal surfaces using the efficient detection criterion

Corneal-height data are typically measured with videokeratoscopes and modeled using a set of orthogonal Zernike polynomials. We address the estimation of the number of Zernike polynomials, which is formalized as a model-order selection problem in linear regression. Classical information-theoretic criteria tend to overestimate the corneal surface due to the weakness of their penalty functions, while bootstrap-based techniques tend to underestimate the surface or require extensive processing. In this paper, we propose to use the efficient detection criterion (EDC), which has the same general form of information-theoretic-based criteria, as an alternative to estimating the optimal number of Zernike polynomials. We first show, via simulations, that the EDC outperforms a large number of information-theoretic criteria and resampling-based techniques. We then illustrate that using the EDC for real corneas results in models that are in closer agreement with clinical expectations and provides means for distinguishing normal corneal surfaces from astigmatic and keratoconic surfaces.

XML documents clustering using tensor space model - A preliminary study

A hierarchical structure is used to represent the content of the semi-structured documents such as XML and XHTML. The traditional Vector Space Model (VSM) is not sufficient to represent both the structure and the content of such web documents. Hence in this paper, we introduce a novel method of representing the XML documents in Tensor Space Model (TSM) and then utilize it for clustering. Empirical analysis shows that the proposed method is scalable for a real-life dataset as well as the factorized matrices produced from the proposed method helps to improve the quality of clusters due to the enriched document representation with both the structure and the content information.

Towards an integrated model of information behavior

Information behavior models generally focus on one of many aspects of information behavior, either information finding, conceptualized as information seeking, information foraging or information sense-making, information organizing and information using. This ongoing study is developing an integrated model of information behavior. The research design involves a 2-week-long daily information journal self-maintained by the participants, combined with two interviews, one before, and one after the journal-keeping period. The data from the study will be analyzed using grounded theory to identify when the participants engage in the various behaviors that have already been observed, identified, and defined in previous models, in order to generate useful sequential data and an integrated model.

Extending Alexander’s ecological dominance-social competition (EDSC) evolutional model

Alexander’s Ecological Dominance and Social Competition (EDSC) model currently provides the most comprehensive overview of human traits in the development of a theory of human evolution and sociality (Alexander, 1990; Finn, Geary & Ward, 2005; Irons, 2005). His model provides a basis for explaining the evolution of human socio-cognitive abilities. Our paper examines the extension of Alexander’s model to incorporate the human trait of information behavior in synergy with ecological dominance and social competition as a human socio-cognitive competence. This paper discusses the various interdisciplinary perspectives exploring how evolution has shaped information behavior and why information behavior is emerging as an important human socio-cognitive competence. This paper outlines these issues, including the extension of Spink and Currier’s (2006a,b) evolution of information behavior model towards a more integrated understanding of how information behaviors have evolved (Spink & Cole, 2006).

Extending Alexander’s ecological dominance-social competition model : information behavior adaptation

Alexander’s Ecological Dominance and Social Competition (EDSC) model currently provides the most comprehensive overview of human traits in the development of a theory of human evolution and sociality (Alexander, 1990; Finn, Geary & Ward, 2005; Irons, 2005). His model provides a basis for explaining the evolution of human socio-cognitive abilities. Our paper examines the extension of Alexander’s model to incorporate the human trait of information behavior in synergy with ecological dominance and social competition as a human socio-cognitive competence. This paper discusses the various interdisciplinary perspectives exploring how evolution has shaped information behavior and why information behavior is emerging as an important human socio-cognitive competence. This paper outlines these issues, including the extension of Spink and Currier’s (2006a,b) evolution of information behavior model towards a more integrated understanding of how information behaviors have evolved (Spink & Cole, 2006).

Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend prelim- inary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean.

Cooperative multi-AUV tracking of phytoplankton blooms based on ocean model predictions

In recent years, ocean scientists have started to employ many new forms of technology as integral pieces in oceanographic data collection for the study and prediction of complex and dynamic ocean phenomena. One area of technological advancement in ocean sampling if the use of Autonomous Underwater Vehicles (AUVs) as mobile sensor plat- forms. Currently, most AUV deployments execute a lawnmower- type pattern or repeated transects for surveys and sampling missions. An advantage of these missions is that the regularity of the trajectory design generally makes it easier to extract the exact path of the vehicle via post-processing. However, if the deployment region for the pattern is poorly selected, the AUV can entirely miss collecting data during an event of specific interest. Here, we consider an innovative technology toolchain to assist in determining the deployment location and executed paths for AUVs to maximize scientific information gain about dynamically evolving ocean phenomena. In particular, we provide an assessment of computed paths based on ocean model predictions designed to put AUVs in the right place at the right time to gather data related to the understanding of algal and phytoplankton blooms.

Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

Trajectory design and implementation for multiple autonomous underwater vehicles based on ocean model predictions

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Hydrodynamic and thruster model validation for autonomous underwater vehicles

From Pontryagin’s Maximum Principle to the Duke Kahanamoku Aquatic Complex; we develop the theory and generate implementable time efficient trajectories for a test-bed autonomous underwater vehicle (AUV). This paper is the beginning of the journey from theory to implementation. We begin by considering pure motion trajectories and move into a rectangular trajectory which is a concatenation of pure surge and pure sway. These trajectories are tested using our numerical model and demonstrated by our AUV in the pool. In this paper we demonstrate that the above motions are realizable through our method, and we gain confidence in our numerical model. We conclude that using our current techniques, implementation of time efficient trajectories is likely to succeed.

Engineered silk fibroin protein 3D matrices for in vitro tumor model

3D in vitro model systems that are able to mimic the in vivo microenvironment are now highly sought after in cancer research. Antheraea mylitta silk fibroin protein matrices were investigated as potential biomaterial for in vitro tumor modeling. We compared the characteristics of MDA-MB-231 cells on A. mylitta, Bombyx mori silk matrices, Matrigel, and tissue culture plates. The attachment and morphology of the MDA-MB-231 cell line on A. mylitta silk matrices was found to be better than on B. mori matrices and comparable to Matrigel and tissue culture plates. The cells grown in all 3D cultures showed more MMP-9 activity, indicating a more invasive potential. In comparison to B. mori fibroin, A. mylitta fibroin not only provided better cell adhesion, but also improved cell viability and proliferation. Yield coefficient of glucose consumed to lactate produced by cells on 3D A. mylitta fibroin was found to be similar to that of cancer cells in vivo. LNCaP prostate cancer cells were also cultured on 3D A. mylitta fibroin and they grew as clumps in long term culture. The results indicate that A. mylitta fibroin scaffold can provide an easily manipulated microenvironment system to investigate individual factors such as growth factors and signaling peptides, as well as evaluation of anticancer drugs.

Planning online : a community-based interactive decision-making model

The first use of computing technologies and the development of land use models in order to support decision-making processes in urban planning date back to as early as mid 20th century. The main thrust of computing applications in urban planning is their contribution to sound decision-making and planning practices. During the last couple of decades many new computing tools and technologies, including geospatial technologies, are designed to enhance planners' capability in dealing with complex urban environments and planning for prosperous and healthy communities. This chapter, therefore, examines the role of information technologies, particularly internet-based geographic information systems, as decision support systems to aid public participatory planning. The chapter discusses challenges and opportunities for the use of internet-based mapping application and tools in collaborative decision-making, and introduces a prototype internet-based geographic information system that is developed to integrate public-oriented interactive decision mechanisms into urban planning practice. This system, referred as the 'Community-based Internet GIS' model, incorporates advanced information technologies, distance learning, sustainable urban development principles and community involvement techniques in decision-making processes, and piloted in Shibuya, Tokyo, Japan.

A test of the job demands : resources model with HIV/AIDS volunteers

Burnout has been identified as a significant factor in HIV/AIDS volunteering. It has been associated with depression, anxiety and the loss of volunteers from the health care delivery system. The aim of this study was to test the independence of the health and motivational processes hypothesized within the Job Demands – Resources model of burnout in HIV/AIDS volunteers. Participants were 307 HIV/AIDS volunteers from state AIDS Councils throughout Australia who completed self-report measures pertaining to role ambiguity and role conflict, social support, burnout, intrinsic and organizational satisfaction, and depression. Findings suggested that the independence of the dual processes hypothesized by the model was only partially supported. These findings provide a model for burnout which gives a framework for interventions at both the individual and organizational level which would contribute to the prevention of burnout, depression, and job dissatisfaction in HIV/AIDS volunteers.

An intelligent building model of sustainable architecture in a mechanism of open systems evolution

This paper argues a model of open systems evolution based on evolutionary thermodynamics and complex system science, as a design paradigm for sustainable architecture. The mechanism of open system evolution is specified in mathematical simulations and theoretical discourses. According to the mechanism, the authors propose an intelligent building model of sustainable design by a holistic information system of the end-users, the building and nature. This information system is used to control the consumption of energy and material resources in building system at microscopic scale, to adapt the environmental performance of the building system to the natural environment at macroscopic scale, for an evolutionary emergence of sustainable performance of buildings.