Modelling passenger flow at airport terminals : individual agent decision model for stochastic passenger behaviour

Airport system is complex. Passenger dynamics within it appear to be complicate as well. Passenger behaviours outside standard processes are regarded more significant in terms of public hazard and service rate issues. In this paper, we devised an individual agent decision model to simulate stochastic passenger behaviour in airport departure terminal. Bayesian networks are implemented into the decision making model to infer the probabilities that passengers choose to use any in-airport facilities. We aim to understand dynamics of the discretionary activities of passengers.

Granule mining and its application for network traffic characterization

Decision table and decision rules play an important role in rough set based data analysis, which compress databases into granules and describe the associations between granules. Granule mining was also proposed to interpret decision rules in terms of association rules and multi-tier structure. In this paper, we further extend granule mining to describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other ganules, it provides a kind of novel knowledge in databases. Some experiments are conducted to test the proposed new concepts for describing the characteristics of a real network traffic data collection. The results show that the proposed concepts are promising.

Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.

Dynamics of rod and cone photoreceptor interactions under mesopic light levels

Visual adaptation regulates contrast sensitivity during dynamically changing light conditions (Crawford, 1947; Hecht, Haig & Chase, 1937). These adaptation dynamics are unknown under dim (mesopic) light levels when the rod (R) and long (L), medium (M) and short (S) wavelength cone photoreceptor classes contribute to vision via interactions in shared non-opponent Magnocellular (MC), chromatically opponent Parvocellular (PC) and Koniocellular (KC) visual pathways (Dacey, 2000). This study investigated the time-course of adaptation and post-receptoral pathways mediating receptor specific rod and cone interactions under mesopic illumination. A four-primary photostimulator (Pokorny, Smithson & Quinlan, 2004) was used to independently control the activity of the four photoreceptor classes and their post-receptoral visual athways in human observers. In the first experiment, the contrast sensitivity and time-course of visual adaptation under mesopic illumination were measured for receptoral (L, S, R) and post-receptoral (LMS, LMSR, L-M) stimuli. An incremental (Rapid-ON) sawtooth conditioning pulse biased detection to ON-cells within the visual pathways and sensitivity was assayed relative to pulse onset using a briefly presented incremental probe that did not alter adaptation. Cone.Cone interactions with luminance stimuli (L cone, LMS, LMSR) reduced sensitivity by 15% and the time course of recovery was 25± 5ms-1 (μ ± SEM). PC mediated (+L-M) chromatic stimuli sensitivity loss was less (8%) than for luminance and recovery was slower (μ = 2.95 ± 0.05 ms-1), with KC mediated (S cone) chromatic stimuli showing a high sensitivity loss (38%) and the slowest recovery time (1.6 ± 0.2 ms-1). Rod-Rod interactions increased sensitivity by 20% and the time course of recovery was 0.7 ± 0.2 ms-1 (μ ± SD). Compared to these interaction types, Rod-Cone interactions reduced sensitivity to a lesser degree (5%) and showed the fastest recovery (μ = 43 ± 7 ms-1). In the second experiment, rod contribution to the magnocellular, parvocellular and koniocellular post-receptoral pathways under mesopic illumination was determined as a function of incremental stimulus duration and waveform (rectangular; sawtooth) using a rod colour match procedure (Cao, Pokorny & Smith, 2005; Cao, Pokorny, Smith & Zele, 2008a). For a 30% rod increment, a cone match required a decrease in [L/(L+M)] and an increase in [L+M] and [S/(L+M)], giving a greenish-blue and brighter appearance for probe durations of 75 ms or longer. Probe durations less than 75 ms showed an increase in [L+M] and no change in chromaticity [L/(L+M) or S/(L+M)], uggesting mediation by the MC pathway only for short duration rod stimuli. s We advance previous studies by determining the time-course and nature of photoreceptor specific retinal interactions in the three post-receptoral pathways under mesopic illumination. In the first experiment, the time-course of adaptation for ON cell processing was determined, revealing opponent cell facilitation in chromatic PC and KC pathways. The Rod-Rod and Rod-Cone data identify previously unknown interaction types that act to maintain contrast sensitivity during dynamically changing light conditions and improve the speed of light adaptation under mesopic light levels. The second experiment determined the degree of rod contribution to the inferred post-eceptoral pathways as a function of the temporal properties of the rod signal. r The understanding of the mechanisms underlying interactions between photoreceptors under mesopic illumination has implications for the study of retinal disease. Visual function has been shown to be reduced in persons with age-related maculopathy (ARM) risk genotypes prior to clinical signs of the disease (Feigl, Cao, Morris & Zele, 2011) and disturbances in rod-mediated adaptation have been shown in early phases of ARM (Dimitrov, Guymer, Zele, Anderson & Vingrys, 2008; Feigl, Brown, Lovie-Kitchin & Swann, 2005). Also, the understanding of retinal networks controlling vision enables the development of international lighting standards to optimise visual performance nder dim light levels (e.g. work-place environments, transportation).

Variations on a routine : how selection-adaptation-retention dynamics create variety in organisational routines

The question "what causes variety in organisational routines" is of considerable interest to organisational scholars, and one to which this thesis seeks to answer. To this end an evolutionary theory of change is advanced which holds that the dynamics of selection, adaptation and retention explain the creation of variety in organisational routines. A longitudinal, multi-level, multi-case analysis is undertaken in this thesis, using multiple data collection strategies. In each case, different types of variety were identified, according to a typology, together with how selection, adaptation and retention contribute to variety in a positive or negative sense. Methodologically, the thesis makes a contribution to our understanding of variety, as certain types of variety only become evident when examined by specific types of research design. The research also makes a theoretical contribution by explaining how selection, adaptation and retention individually and collectively contribute to variety in organisational routines. Moreover, showing that routines could be stable, diverse, adaptive and dynamic at the same time; is a significant, and novel, theoretical contribution.

Strategizing public sector human resource management : the implications of working network

Networks have come to the fore as a means by which government can achieve its strategic objectives, particularly when addressing complex or “wicked” issues. Such joined-up arrangements differ in their operations from other forms of organizing as they require collaborative effort to deliver the collaborative advantage. Strategic Human Resource Management is concerned with the matching of human resource practices to the strategic direction of organizations. It is argued that the strategic direction of government has been towards network involvement and that, as a result, a reconfiguration of Human Resource Management practices is needed to support this new direction. Drawing on eight network case studies findings are presented in relation to the roles government is expected to play in networks and conclusions are drawn about what types of human resource management practices would best support those roles. Implications for Strategic Human Resource Management are posited.

Assessing the representativeness of a groundwater quality monitoring network

The monitoring sites comprising a state of the environment (SOE) network must be carefully selected to ensure that they will be representative of the broader resource. Hierarchical cluster analysis (HCA) is a data-driven technique that can potentially be employed to assess the representativeness of a SOE monitoring network. The objective of this paper is to explore the use of HCA as an approach for assessing the representativeness of the New Zealand National Groundwater Monitoring Programme (NGMP), which is comprised of 110 monitoring sites across the country.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

Development of network-level project screening methods supporting the 4-Year pavement management plan in Texas

The Texas Department of Transportation (TxDOT) is concerned about the widening gap between pavement preservation needs and available funding. Thus, the TxDOT Austin District Pavement Engineer (DPE) has investigated methods to strategically allocate available pavement funding to potential projects that improve the overall performance of the District and Texas highway systems. The primary objective of the study presented in this paper is to develop a network-level project screening and ranking method that supports the Austin District 4-year pavement management plan development. The study developed candidate project selection and ranking algorithms that evaluated pavement conditions of each project candidate using data contained in the Pavement Management Information system (PMIS) database and incorporated insights from Austin District pavement experts; and implemented the developed method and supporting algorithm. This process previously required weeks to complete, but now requires about 10 minutes including data preparation and running the analysis algorithm, which enables the Austin DPE to devote more time and resources to conducting field visits, performing project-level evaluation and testing candidate projects. The case study results showed that the proposed method assisted the DPE in evaluating and prioritizing projects and allocating funds to the right projects at the right time.

An analytical tool that quantifies cellular morphology changes from three-dimensional fluorescence images

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology(1) even in complex tissue sections(2). Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells(3), however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgenössische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.