Utilising semantic technologies for decision support in dementia care

The main objective of this work is to discuss our experience in utilising semantic technologies for building decision support in Dementia care systems that are based on the non-intrusive on the non-intrusive monitoring of the patient’s behaviour. Our approach adopts context-aware modelling of the patient’s condition to facilitate the analysis of the patient’s behaviour within the inhabited environment (movement and room occupancy patterns, use of equipment, etc.) with reference to the semantic knowledge about the patient’s condition (history of present of illness, dependable behaviour patterns, etc.). The reported work especially focuses on the critical role of the semantic reasoning engine in inferring medical advice, and by means of practical experimentation and critical analysis suggests important findings related to the methodology of deploying the appropriate semantic rules systems, and the dynamics of the efficient utilisation of complex event processing technology in order to the meet the requirements of decision support for remote healthcare systems

Sustainable Development through Holistic Multi-Paradigmatic Integrated Modeling and Decision Making

Sustainability, sustainable development, and transformation is about balancing the economic, environmental and social aspects of organisations and their operations. Existing systems do not comprehensively support sustainable transformation nor do they allow decision makers to explore interrelationships and influences between the sustainability dimensions. This leads to silo based decision making where vision and strategies are not mapped to execution, and sustainability modelling processes are uncoordinated, lack holism, are biased and myopic. One of the critical challenges is the lack of processes and systems that allow the integration of models belonging to disparate paradigms in a holistic manner. To overcome these problems this research proposes a holistic, multi-paradigmatic, integrated modelling and decision making (HOMIMD) approach for sustainable development and transformation. We apply the HOMIMD processes, and requirements to a warehouse management problem and leverage optimization (AIMMS), systems dynamics (iThink), and data mining (SPSS Modeler) approaches, techniques, and systems to solve the problem

System Dynamics Simulation Model for Cardiovascular Heart Disease Risk Factors - Smoking and Alcohol Intake

Detecting diseases at early stage can help to overcome and treat them accurately. Identifying the appropriate treatment depends on the method that is used in diagnosing the diseases. The incidence of cardiovascular heart disease (CVD) has been increasing steadily and so too its associated mortality. System Dynamics is appropriate methodology for Modelling and Simulation. The Expert knowledge about risk factors for CVD was elicited through interview and literature search. Two CVD risk factors Smoking and Alcohol Intake were analyzed by the proposed decision support system developed with System Dynamics Simulation software (iThink V9.0.2 ),used for the design, implementation and evaluation of the system. The proposed framework would be particularly useful for researchers in the field but also for medical practitioners and developers of medical decision support systems

Knowledge Modelling in Multiagent Systems: The Case of the Management of a National Network

This paper presents the knowledge model of a distributed decision support system, that has been designed for the management of a national network in Ukraine. It shows how advanced Artificial Intelligence techniques (multiagent systems and knowledge modelling) have been applied to solve this real-world decision support problem: on the one hand its distributed nature, implied by different loci of decision-making at the network nodes, suggested to apply a multiagent solution; on the other, due to the complexity of problem-solving for local network administration, it was useful to apply knowledge modelling techniques, in order to structure the different knowledge types and reasoning processes involved. The paper sets out from a description of our particular management problem. Subsequently, our agent model is described, pointing out the local problem-solving and coordination knowledge models. Finally, the dynamics of the approach is illustrated by an example

Capability of APSIM-Oryza to stimulate lowland rice-based farming systems under nitrogen treatments in a tropical climate

Rice is the most important crop in Asia and the staple food for most of the world’s population. Due to the overwhelming importance of this crop, modelling rice-based farming systems will provide valuable help to compare experimental research findings across regions, extrapolate field experimental data to wider environments, develop management recommendations and decision-support systems, explore effects of climate change and adaptation options, and prediction of crop yield. There is an increasing demand for the capability to simulate rice-based cropping systems, especially in Asia. Such a system capability will allow expanded investigation of nitrogen dynamics, crop sequencing, intercropping, crop residue management and soil and water management. Incorporation of the ORYZA2000 rice model(Bouman and van Laar, 2006) into APSIM (Agricultural Production Systems Simulator (APSIM-Oryza) together with recent work on carbon and nitrogen dynamics in transitional flooded/non-flooded systems(Gaydon et al., 2009) has facilitated long-term simulation of lowland rice-based farming systems scenarios. However, the capability of APSIM-Oryza to simulate rice-based crop sequences involving other crops has undergone limited testing to this point and under a variety of crop management practices and cropping systems. In this paper, we detail testing of the APSIM-Oryza simulation model against an experimental dataset involving lowland rice-rice-soybean crop rotation in West Nusa Tenggara Province(NTB) Indonesi

Modelling forests as social-ecological systems: A systematic comparison of agent-based approaches

The multifunctionality of forest systems calls for appropriately complex modelling approaches to capture social and ecosystem dynamics. Using a social-ecological systems framework, we review the functionality of 31 existing agent-based models applied to managed forests. Several applications include advanced cognitive and emotional decision-making, crucial for understanding complex sustainability challenges. However, far from all demonstrate representation of key elements in a social-ecological system like direct interactions, and dynamic representations of social and ecological processes. We conclude that agent-based approaches are adequately complex for simulating both social and ecological subsystems, but highlight three main avenues for further development: i) robust methodological standards for calibration and validation of agent-based approaches; ii) modelling of agent learning, adaptive governance and feedback loops; iii) coupling to ecological models such as dynamic vegetation models or species distribution models. We round-off by providing a set of questions to support social-ecological systems modelling choices

AN ALTERNATIVE DECISION SUPPORT SYSTEMS PARADIGM FOR SUSTAINABLE ENERGY PLANNING IN DEVELOPING ECONOMIES: A CASE FOR SYSTEM THINKING / SYSTEM DYNAMICS METHODOLOGY

This article proposes an alternative Decision Support Systems (DSS) framework using a System Thinking (ST) andSystem Dynamics (SD) approach for sustainable energy planning in a Developing Economy (DE). Many DE haveundergone dramatic changes in socio-economic policies such liberalisation, finance sourcing and the incorporationof externalities such as the environmental implications of energy projects. The underlying dynamics of SustainableEnergy Development (SED) in the DE reveals their inherent limitations of traditional planning tools such asoptimisation, econometric and general simulation models in guiding future policy decisions. The unsuitability oftraditional tools is rooted in the socio-economic, political and technological differences, as compared to those of thedeveloped nations. DES methodology facilitates the design of policy rules that govern complex decisions bydemonstrating how past policies created the current crises. It enables the modelling of complex energy issues, andenhances understanding of the dominant system characteristics that causes systems’ instability. This article fills animportant gap in the literature by demonstrating the needs for a new modelling framework that permits focusing onthe holistic structure identifiable within energy systems as prevalent in the DE.Keywords: DSS, Developing, Framework, Energy, Planning, Systems, Sustainable and Methodologies

AQUACOAST: A Simulation Tool to Explore Coastal Groundwater and Irrigation Farming Interactions

In the framework of coastal groundwater-dependent irrigation agriculture, modelling becomes indispensable to know how this renewable resource responds to complex (usually not conceptualized nor monitored) biophysical, social, and economic interactions. Friendly user interfaces are essential to involve nonmodeling experts in exploiting and improving models. Decision support systems (DSS) are software systems that integrate models, databases, or other decision aids and package them in a way that decision makers can use. This paper addresses these two issues: firstly with the implementation of a System Dynamics (SD) model in Vensim software that considers the integration of hydrological, agronomic, and economic drivers and secondly with the design of a Venapp, push-button interfaces that allow users access to a Vensim model without going through the Vensim modelling environment. The prototype designed, the AQUACOAST tool, gives an idea of the possibilities of this type of models to identify and analyze the impact of apparently unrelated factors such as the prices of cultivated products, subsidies or exploitation costs on the advance of saltwater intrusion, and the great threat to coastal groundwater-dependent irrigation agriculture systems.This study was funded by the European Research Council grant agreement no. 647038 (BIODESERT)

The application of discrete event simulation and system dynamics in the logistics and supply chain context

Discrete event simulation (DES) and system dynamics (SD) are two modelling approaches widely used as decision support tools in logistics and supply chain management (LSCM). A widely held belief exists that SD is mostly used to model problems at a strategic level, whereas DES is used at an operational/tactical level. This paper explores the application of DES and SD as decision support systems (DSS) for LSCM by looking at the nature and level of issues modelled. Peer reviewed journal papers that use these modelling approaches to study supply chains, published between 1996 and 2006 are reviewed. A total of 127 journal articles are analysed to identify the frequency with which the two simulation approaches are used as modelling tools for DSS in LSCM. Our findings suggest that DES has been used more frequently to model supply chains, with the exception of the bullwhip effect, which is mostly modelled using SD. Based on the most commonly used modelling approach, issues in LSCM are categorised into four groups: the DES domain, the SD domain, the common domain and the less common domain. The study furthermore suggests that in terms of the level of decision making involved, strategic or operational/tactical, there is no difference in the use of either DES or SD. The results of this study inform the existing literature about the use of DES and SD as DSS tools in LSCM