Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity.

In spite of the considerable research on sustainability, reports suggest that we are barely any closer to a more sustainable society. As such, there is an urgent need to improve the effectiveness of human efforts towards sustainability. A clearer and more unified understanding of sustainability among different people and sectors could help to facilitate this. This paper presents the results of an inductive literature investigation, aiming to develop models to explain the nature of sustainability in the Earth system, and how humans can effectively strive for it. The major contributions are two general and complementary models, that may be applied in any context to provide a common basis for understanding sustainability: the Sustainability Cycle (S-Cycle), and the Sustainability Loop (S-Loop). Literature spanning multiple sectors is examined from the perspective of three concepts, emerging as significant in relation to our aim. Systems are shown to provide the context for human action towards sustainability, and the nature of the Earth system and its sub-systems is explored. Activities are outlined as a fundamental target that humans need to sustain, since they produce the entities both needed and desired by society. The basic behaviour of activities operating in the Earth system is outlined. Finally, knowledge is positioned as the driver of human action towards sustainability, and the key components of knowledge involved are examined. The S-Cycle and S-Loop models are developed via a process of induction from the reviewed literature. The S-Cycle describes the operation of activities in a system from the perspective of sustainability. The sustainability of activities in a system depends upon the availability of resources, and the availability of resources depends upon the rate that activities consume and produce them. Humans may intervene in these dynamics via an iterative process of interpretation and action, described in the S-Loop model. The models are briefly applied to a system described in the literature. It is shown that the S-Loop may be used to guide efforts towards sustainability in a particular system of interest, by prescribing the basic activities involved. The S-Cycle may be applied complementary to the S-Loop, to support the interpretation of activity behaviour described in the latter. Given their general nature, the models provide the basis for a more unified understanding of sustainability. It is hoped that their use may go some way towards improving the effectiveness of human action towards sustainability.

The mechanism of titanium dissolution in sulfuric acid has been investigated using wire, rotating disk and platinum ring‐titanium disk electrodes. Titanium dissolves directly as Ti3+ , without any solution soluble intermediates. The reaction is H+ catalyzed (approximately first order). A possible mechanism is suggested.

A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom-up analysis of the data required to support RCM within this specific industry, combined with a top-down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for intelligent condition monitoring, reliability and maintenance modelling, and maintenance scheduling that provide a scalable solution for performing dynamic, efficient and cost-effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time the integration of state-of-the-art advanced mathematical techniques: Random Forests, dynamic Bayesian networks and memetic algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines' survivability and creation of a hierarchy of maintenance actions, and the optimizing of the maintenance schedule with a view to maximizing the availability and revenue generation of the turbines.