An Artificial Intelligence Approach to Individuals' Age Assessment

A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

Information systems to support decision-making in *construction owner organizations: A data warehousing approach

Construction organizations typically deal with large volumes of project data containing valuable information. It is found that these organizations do not use these data effectively for planning and decision-making. There are two reasons. First, the information systems in construction organizations are designed to support day-to-day construction operations. The data stored in these systems are often non-validated, non-integrated and are available in a format that makes it difficult for decision makers to use in order to make timely decisions. Second, the organizational structure and the IT infrastructure are often not compatible with the information systems thereby resulting in higher operational costs and lower productivity. These two issues have been investigated in this research with the objective of developing systems that are structured for effective decision-making. ^ A framework was developed to guide storage and retrieval of validated and integrated data for timely decision-making and to enable construction organizations to redesign their organizational structure and IT infrastructure matched with information system capabilities. The research was focused on construction owner organizations that were continuously involved in multiple construction projects. Action research and Data warehousing techniques were used to develop the framework. ^ One hundred and sixty-three construction owner organizations were surveyed in order to assess their data needs, data management practices and extent of use of information systems in planning and decision-making. For in-depth analysis, Miami-Dade Transit (MDT) was selected which is in-charge of all transportation-related construction projects in the Miami-Dade county. A functional model and a prototype system were developed to test the framework. The results revealed significant improvements in data management and decision-support operations that were examined through various qualitative (ease in data access, data quality, response time, productivity improvement, etc.) and quantitative (time savings and operational cost savings) measures. The research results were first validated by MDT and then by a representative group of twenty construction owner organizations involved in various types of construction projects. ^

Information systems to support decision-making in construction owner organizations : a data warehousing approach

Construction organizations typically deal with large volumes of project data containing valuable information. It is found that these organizations do not use these data effectively for planning and decision-making. There are two reasons. First, the information systems in construction organizations are designed to support day-to-day construction operations. The data stored in these systems are often non-validated, nonintegrated and are available in a format that makes it difficult for decision makers to use in order to make timely decisions. Second, the organizational structure and the IT infrastructure are often not compatible with the information systems thereby resulting in higher operational costs and lower productivity. These two issues have been investigated in this research with the objective of developing systems that are structured for effective decision-making. A framework was developed to guide storage and retrieval of validated and integrated data for timely decision-making and to enable construction organizations to redesign their organizational structure and IT infrastructure matched with information system capabilities. The research was focused on construction owner organizations that were continuously involved in multiple construction projects. Action research and Data warehousing techniques were used to develop the framework. One hundred and sixty-three construction owner organizations were surveyed in order to assess their data needs, data management practices and extent of use of information systems in planning and decision-making. For in-depth analysis, Miami-Dade Transit (MDT) was selected which is in-charge of all transportation-related construction projects in the Miami-Dade county. A functional model and a prototype system were developed to test the framework. The results revealed significant improvements in data management and decision-support operations that were examined through various qualitative (ease in data access, data quality, response time, productivity improvement, etc.) and quantitative (time savings and operational cost savings) measures. The research results were first validated by MDT and then by a representative group of twenty construction owner organizations involved in various types of construction projects.

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

Re-framing infrastructure investment decision-making processes : a preliminary scoping study for urban flood risk management in Jakarta, Indonesia

Jakarta, Indonesia’s chronic housing shortage poses multiple challenges for contemporary policy-makers. While it may be in the city’s interest to increase the availability of housing, there is limited land to do so. Market pressures, in tandem with government’s desire for housing availability, demand consideration of even marginal lands, such as those within floodplains, for development. Increasingly, planning for a flood resilient Jakarta is complicated by a number of factors, including: the city is highly urbanized and land use data is limited; flood management is technically complex, creating potential barriers to engagement for both decision-makers and the public; inherent uncertainty exists throughout modelling efforts, central to management; and risk and liability for infrastructure investments is unclear. These obstacles require localized watershed-level participatory planning to address risks of flooding where possible and reduce the likelihood that informal settlements occur in areas of extreme risk. This paper presents a preliminary scoping study for determination of an effective participatory planning method to encourage more resilient development. First, the scoping study provides background relevant to the challenges faced in planning for contemporary Jakarta. Second, the study examines the current use of decision-support tools, such as Geographic Information Systems (GIS), in planning for Jakarta. Existing capacity in the use of GIS allows for consideration of the use of an emerging method of community consultation - Multi-Criteria Decision-Making (MCDM) support systems infused with geospatial information - to aid in engagement with the public and improve decision-making outcomes. While these methods have been used in Australia to promote stakeholder engagement in urban intensification, the planned research will be an early introduction of the method to Indonesia. As a consequence of this intervention, it is expected that planning activities will result in a more resilient city, capable of engaging with disaster risk management in a more effective manner.

Measuring the walkability of local communities using geographic information systems data

Researchers are working to identify and promote environment and policy initiatives to encourage more active and healthy communities. Measuring environmental attributes through objective means can verify which physical environment factors are most important. We describe how Geographic Information Systems (GIS) may be used to measure objectively, the features of the built environment that may influence walking. We show how four key attributes currently believed to be of most relevance to walking for transport may be used to create a ‘walkability’ index. These are dwelling density (higher-density neighbourhoods support greater retail and service variety, resulting in shorter, walkable distances between facilities; driving and parking are more difficult); street connectivity (higher intersection density provides people with a greater choice of potential routes, easier access to major roads where public transport is available and shorter times to get to destinations); land use mix (the more varied the land use mix and built form, the more conducive it is to walk to various destinations); and net retail area (people who live near multiple and diverse retail opportunities are able to make more frequent and shorter shopping trips by walking and can walk to more local employment opportunities). The potential relationships between each of the objective environmental-attribute measures and walking behaviours is discussed, together with suggestions as to how such measures might be used to guide community infrastructure planning. GIS mapping can assist decision makers in where to focus transportation investments and where to guide future growth. Readily accessible GIS data can be used to guide and support urban planning and infrastructure investment decisions in both the private and public sectors, to increase walking in communities.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

A multi-organisational approach for disaster preparedness and response:the use of optimisation and GIS for facility location, stock pre-positioning, resource allocation and relief distribution

From 1992 to 2012 4.4 billion people were affected by disasters with almost 2 trillion USD in damages and 1.3 million people killed worldwide. The increasing threat of disasters stresses the need to provide solutions for the challenges faced by disaster managers, such as the logistical deployment of resources required to provide relief to victims. The location of emergency facilities, stock prepositioning, evacuation, inventory management, resource allocation, and relief distribution have been identified to directly impact the relief provided to victims during the disaster. Managing appropriately these factors is critical to reduce suffering. Disaster management commonly attracts several organisations working alongside each other and sharing resources to cope with the emergency. Coordinating these agencies is a complex task but there is little research considering multiple organisations, and none actually optimising the number of actors required to avoid shortages and convergence. The aim of the this research is to develop a system for disaster management based on a combination of optimisation techniques and geographical information systems (GIS) to aid multi-organisational decision-making. An integrated decision system was created comprising a cartographic model implemented in GIS to discard floodable facilities, combined with two models focused on optimising the decisions regarding location of emergency facilities, stock prepositioning, the allocation of resources and relief distribution, along with the number of actors required to perform these activities. Three in-depth case studies in Mexico were studied gathering information from different organisations. The cartographic model proved to reduce the risk to select unsuitable facilities. The preparedness and response models showed the capacity to optimise the decisions and the number of organisations required for logistical activities, pointing towards an excess of actors involved in all cases. The system as a whole demonstrated its capacity to provide integrated support for disaster preparedness and response, along with the existence of room for improvement for Mexican organisations in flood management.

Linking early design decisions across multiple disciplines

The early stages of the building design process are when the most far reaching decisions are made regarding the configuration of the proposed project. This paper examines methods of providing decision support to building designers across multiple disciplines during the early stage of design. The level of detail supported is at the massing study stage where the basic envelope of the project is being defined. The block outlines on the building envelope are sliced into floors. Within a floor the only spatial divisions supported are the “user” space and the building core. The building core includes vertical transportation systems, emergency egress and vertical duct runs. The current focus of the project described in the paper is multi-storey mixed use office/residential buildings with car parking. This is a common type of building in redevelopment projects within and adjacent to the central business districts of major Australian cities. The key design parameters for system selection across the major systems in multi-storey building projects - architectural, structural, HVAC, vertical transportation, electrical distribution, fire protection, hydraulics and cost – are examined. These have been identified through literature research and discussions with building designers from various disciplines. This information is being encoded in decision support tools. The decision support tools communicate through a shared database to ensure that the relevant information is shared across all of the disciplines. An internal data model has been developed to support the very early design phase and the high level system descriptions required. A mapping to IFC 2x2 has also been defined to ensure that this early information is available at later stages of the design process.

Online geographic information systems for improving health planning practice : lessons learned from the case study of Logan-Beaudesert, Australia

This study examines the impact of utilising a Decision Support System (DSS) in a practical health planning study. Specifically, it presents a real-world case of a community-based initiative aiming to improve overall public health outcomes. Previous studies have emphasised that because of a lack of effective information, systems and an absence of frameworks for making informed decisions in health planning, it has become imperative to develop innovative approaches and methods in health planning practice. Online Geographical Information Systems (GIS) has been suggested as one of the innovative methods that will inform decision-makers and improve the overall health planning process. However, a number of gaps in knowledge have been identified within health planning practice: lack of methods to develop these tools in a collaborative manner; lack of capacity to use the GIS application among health decision-makers perspectives, and lack of understanding about the potential impact of such systems on users. This study addresses the abovementioned gaps and introduces an online GIS-based Health Decision Support System (HDSS), which has been developed to improve collaborative health planning in the Logan-Beaudesert region of Queensland, Australia. The study demonstrates a participatory and iterative approach undertaken to design and develop the HDSS. It then explores the perceived user satisfaction and impact of the tool on a selected group of health decision makers. Finally, it illustrates how decision-making processes have changed since its implementation. The overall findings suggest that the online GIS-based HDSS is an effective tool, which has the potential to play an important role in the future in terms of improving local community health planning practice. However, the findings also indicate that decision-making processes are not merely informed by using the HDSS tool. Instead, they seem to enhance the overall sense of collaboration in health planning practice. Thus, to support the Healthy Cities approach, communities will need to encourage decision-making based on the use of evidence, participation and consensus, which subsequently transfers into informed actions.

Developing tools to support complex infrastructure decision-making

Purpose – The rapidly changing role of capital city airports has placed demands on surrounding infrastructure. The need for infrastructure management and coordination is increasing as airports and cities grow and share common infrastructure frameworks. The purpose of this paper is to document the changing context in Australia, where the privatisation of airports has stimulated considerable land development with resulting pressures on surrounding infrastructure provision. It aims to describe a tool that is being developed to support decision-making between various stakeholders in the airport region. The use of planning support systems improves both communication and data transfer between stakeholders and provides a foundation for complex decisions on infrastructure. Design/methodology/approach – The research uses a case study approach and focuses on Brisbane International Airport and Brisbane City Council. The research is primarily descriptive and provides an empirical assessment of the challenges of developing and implementing planning support systems as a tool for governance and decision-making. Findings – The research assesses the challenges in implementing a common data platform for stakeholders. Agency data platforms and models, traditional roles in infrastructure planning, and integrating similar data platforms all provide barriers to sharing a common language. The use of a decision support system has to be shared by all stakeholders with a common platform that can be versatile enough to support scenarios and changing conditions. The use of iPadss for scenario modelling provides stakeholders the opportunity to interact, compare scenarios and views, and react with the modellers to explore other options. Originality/value – The research confirms that planning support systems have to be accessible and interactive by their users. The Airport City concept is a new and evolving focus for airport development and will place continuing pressure on infrastructure servicing. A coordinated and efficient approach to infrastructure decision-making is critical, and an interactive planning support system that can model infrastructure scenarios provides a sound tool for governance.

Developing a predictive contractor satisfaction model (CoSMo) for construction projects

Over the last few decades, construction project performance has been evaluated due to the increase of delays, cost overruns and quality failures. Growing numbers of disputes, inharmonious working environments, conflict, blame cultures, and mismatches of objectives among project teams have been found to be contributory factors to poor project performance. Performance measurement (PM) approaches have been developed to overcome these issues, however, the comprehensiveness of PM as an overall approach is still criticised in terms of the iron triangle; namely time, cost, and quality. PM has primarily focused on objective measures, however, continuous improvement requires the inclusion of subjective measures, particularly contractor satisfaction (Co-S). It is challenging to deal with the two different groups of large and small-medium contractor satisfaction as to date, Co-S has not been extensively defined, primarily in developing countries such as Malaysia. Therefore, a Co-S model is developed in this research which aims to fulfil the current needs in the construction industry by integrating performance measures to address large and small-medium contractor perceptions. The positivist paradigm used in the research was adhered to by reviewing relevant literature and evaluating expert discussions on the research topic. It yielded a basis for the contractor satisfaction model (CoSMo) development which consists of three elements: contractor satisfaction (Co-S) dimensions; contributory factors and characteristics (project and participant). Using valid questionnaire results from 136 contractors in Malaysia lead to the prediction of several key factors of contractor satisfaction and to an examination of the relationships between elements. The relationships were examined through a series of sequential statistical analyses, namely correlation, one-way analysis of variance (ANOVA), t-tests and multiple regression analysis (MRA). Forward and backward MRAs were used to develop Co-S mathematical models. Sixteen Co-S models were developed for both large and small-medium contractors. These determined that the large contractor Malaysian Co-S was most affected by the conciseness of project scope and quality of the project brief. Contrastingly, Co-S for small-medium contractors was strongly affected by the efficiency of risk control in a project. The results of the research provide empirical evidence in support of the notion that appropriate communication systems in projects negatively contributes to large Co-S with respect to cost and profitability. The uniqueness of several Co-S predictors was also identified through a series of analyses on small-medium contractors. These contractors appear to be less satisfied than large contractors when participants lack effectiveness in timely authoritative decision-making and communication between project team members. Interestingly, the empirical results show that effective project health and safety measures are influencing factors in satisfying both large and small-medium contractors. The perspectives of large and small-medium contractors in respect to the performance of the entire project development were derived from the Co-S models. These were statistically validated and refined before a new Co-S model was developed. Developing such a unique model has the potential to increase project value and benefit all project participants. It is important to improve participant collaboration as it leads to better project performance. This study may encourage key project participants; such as client, consultant, subcontractor and supplier; to increase their attention to contractor needs in the development of a project. Recommendations for future research include investigating other participants‟ perspectives on CoSMo and the impact of the implementation of CoSMo in a project, since this study is focused purely on the contractor perspective.

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

Parent's socio-economic status and social support as risks for child mortality : consideration of health equity in The Gambia

A matched case-control study of mortality to children under age five was conducted to consider associations with parents' socio-economic status and social support in the Farafenni Demographic Surveillance Site (DSS). Cases and controls were selected from Farafenni DSS, matched on date of birth, and parents were interviewed about personal resources and social networks. Parents with the lowest personal socio-economic status and social support were identified. Multivariate multinomial regression was used to consider whether the children of these parents were at increased risk of either infant or 1-4 mortality, in separate models using either parents' characteristics. There was no benefit found for higher SES or better social support with respect to child mortality. Children of fathers who had the poorest social support had lower 1-4 mortality risk (OR=0.52, p=0.037). Given that socio-economic status was not associated with child mortality, it seems unlikely that the explanation for the link between father's social support and mortality is linked to resource availability. Explanations for the risk effect of father's social ties may lie in decision-making around health maintenance and health care for children.

EcoGIS – GIS tools for ecosystem approaches to fisheries management

Executive Summary: The EcoGIS project was launched in September 2004 to investigate how Geographic Information Systems (GIS), marine data, and custom analysis tools can better enable fisheries scientists and managers to adopt Ecosystem Approaches to Fisheries Management (EAFM). EcoGIS is a collaborative effort between NOAA’s National Ocean Service (NOS) and National Marine Fisheries Service (NMFS), and four regional Fishery Management Councils. The project has focused on four priority areas: Fishing Catch and Effort Analysis, Area Characterization, Bycatch Analysis, and Habitat Interactions. Of these four functional areas, the project team first focused on developing a working prototype for catch and effort analysis: the Fishery Mapper Tool. This ArcGIS extension creates time-and-area summarized maps of fishing catch and effort from logbook, observer, or fishery-independent survey data sets. Source data may come from Oracle, Microsoft Access, or other file formats. Feedback from beta-testers of the Fishery Mapper was used to debug the prototype, enhance performance, and add features. This report describes the four priority functional areas, the development of the Fishery Mapper tool, and several themes that emerged through the parallel evolution of the EcoGIS project, the concept and implementation of the broader field of Ecosystem Approaches to Management (EAM), data management practices, and other EAM toolsets. In addition, a set of six succinct recommendations are proposed on page 29. One major conclusion from this work is that there is no single “super-tool” to enable Ecosystem Approaches to Management; as such, tools should be developed for specific purposes with attention given to interoperability and automation. Future work should be coordinated with other GIS development projects in order to provide “value added” and minimize duplication of efforts. In addition to custom tools, the development of cross-cutting Regional Ecosystem Spatial Databases will enable access to quality data to support the analyses required by EAM. GIS tools will be useful in developing Integrated Ecosystem Assessments (IEAs) and providing pre- and post-processing capabilities for spatially-explicit ecosystem models. Continued funding will enable the EcoGIS project to develop GIS tools that are immediately applicable to today’s needs. These tools will enable simplified and efficient data query, the ability to visualize data over time, and ways to synthesize multidimensional data from diverse sources. These capabilities will provide new information for analyzing issues from an ecosystem perspective, which will ultimately result in better understanding of fisheries and better support for decision-making. (PDF file contains 45 pages.)