The appraisal and management of multi-storey dwelling blocks using large concrete panel systems

This exploratory study is concerned with the integrated appraisal of multi-storey dwelling blocks which incorporate large concrete panel systems (LPS). The first step was to look at U.K. multi-storey dwelling stock in general, and under the management of Birmingham City Council in particular. The information has been taken from the databases of three departments in the City of Birmingham, and rearranged in a new database using a suite of PC software called `PROXIMA' for clarity and analysis. One hundred of their stock were built large concrete panel system. Thirteen LPS blocks were chosen for the purpose of this study as case-studies depending mainly on the height and age factors of the block. A new integrated appraisal technique has been created for the LPS dwelling blocks, which takes into account the most physical and social factors affecting the condition and acceptability of these blocks. This appraisal technique is built up in a hierarchical form moving from the general approach to particular elements (a tree model). It comprises two main approaches; physical and social. In the physical approach, the building is viewed as a series of manageable elements and sub-elements to cover every single physical or environmental factor of the block, in which the condition of the block is analysed. A quality score system has been developed which depends mainly on the qualitative and quantitative conditions of each category in the appraisal tree model, and leads to physical ranking order of the study blocks. In the social appraisal approach, the residents' satisfaction and attitude toward their multi-storey dwelling block was analysed in relation to: a. biographical and housing related characteristics; and b. social, physical and environmental factors associated with this sort of dwelling, block and estate in general.The random sample consisted of 268 residents living in the 13 case study blocks. Data collected was analysed using frequency counts, percentages, means, standard deviations, Kendall's tue, r-correlation coefficients, t-test, analysis of variance (ANOVA) and multiple regression analysis. The analysis showed a marginally positive satisfaction and attitude towards living in the block. The five most significant factors associated with the residents' satisfaction and attitude in descending order were: the estate, in general; the service categories in the block, including heating system and lift services; vandalism; the neighbours; and the security system of the block. An important attribute of this method, is that it is relatively inexpensive to implement, especially when compared to alternatives adopted by some local authorities and the BRE. It is designed to save time, money and effort, to aid decision making, and to provide ranked priority to the multi-storey dwelling stock, in addition to many other advantages. A series of solution options to the problems of the block was sought for selection and testing before implementation. The traditional solutions have usually resulted in either demolition or costly physical maintenance and social improvement of the blocks. However, a new solution has now emerged, which is particularly suited to structurally sound units. The solution of `re-cycling' might incorporate the reuse of an entire block or part of it, by removing panels, slabs and so forth from the upper floors in order to reconstruct them as low-rise accommodations.

Unsupervised KDD to creatively support managers' decision making with fuzzy association rules:a distribution channel application

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis. © 2013 Elsevier Inc.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.

Organisational learning - a critical systems thinking discipline

Original Paper European Journal of Information Systems (2001) 10, 135–146; doi:10.1057/palgrave.ejis.3000394 Organisational learning—a critical systems thinking discipline P Panagiotidis1,3 and J S Edwards2,4 1Deloitte and Touche, Athens, Greece 2Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK Correspondence: Dr J S Edwards, Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. E-mail: j.s.edwards@aston.ac.uk 3Petros Panagiotidis is Manager responsible for the Process and Systems Integrity Services of Deloitte and Touche in Athens, Greece. He has a BSc in Business Administration and an MSc in Management Information Systems from Western International University, Phoenix, Arizona, USA; an MSc in Business Systems Analysis and Design from City University, London, UK; and a PhD degree from Aston University, Birmingham, UK. His doctorate was in Business Systems Analysis and Design. His principal interests now are in the ERP/DSS field, where he serves as project leader and project risk managment leader in the implementation of SAP and JD Edwards/Cognos in various major clients in the telecommunications and manufacturing sectors. In addition, he is responsible for the development and application of knowledge management systems and activity-based costing systems. 4John S Edwards is Senior Lecturer in Operational Research and Systems at Aston Business School, Birmingham, UK. He holds MA and PhD degrees (in mathematics and operational research respectively) from Cambridge University. His principal research interests are in knowledge management and decision support, especially methods and processes for system development. He has written more than 30 research papers on these topics, and two books, Building Knowledge-based Systems and Decision Making with Computers, both published by Pitman. Current research work includes the effect of scale of operations on knowledge management, interfacing expert systems with simulation models, process modelling in law and legal services, and a study of the use of artifical intelligence techniques in management accounting. Top of pageAbstract This paper deals with the application of critical systems thinking in the domain of organisational learning and knowledge management. Its viewpoint is that deep organisational learning only takes place when the business systems' stakeholders reflect on their actions and thus inquire about their purpose(s) in relation to the business system and the other stakeholders they perceive to exist. This is done by reflecting both on the sources of motivation and/or deception that are contained in their purpose, and also on the sources of collective motivation and/or deception that are contained in the business system's purpose. The development of an organisational information system that captures, manages and institutionalises meaningful information—a knowledge management system—cannot be separated from organisational learning practices, since it should be the result of these very practices. Although Senge's five disciplines provide a useful starting-point in looking at organisational learning, we argue for a critical systems approach, instead of an uncritical Systems Dynamics one that concentrates only on the organisational learning practices. We proceed to outline a methodology called Business Systems Purpose Analysis (BSPA) that offers a participatory structure for team and organisational learning, upon which the stakeholders can take legitimate action that is based on the force of the better argument. In addition, the organisational learning process in BSPA leads to the development of an intrinsically motivated information organisational system that allows for the institutionalisation of the learning process itself in the form of an organisational knowledge management system. This could be a specific application, or something as wide-ranging as an Enterprise Resource Planning (ERP) implementation. Examples of the use of BSPA in two ERP implementations are presented.

CORD an object model supporting statistical summary information for management decision making

Information systems have developed to the stage that there is plenty of data available in most organisations but there are still major problems in turning that data into information for management decision making. This thesis argues that the link between decision support information and transaction processing data should be through a common object model which reflects the real world of the organisation and encompasses the artefacts of the information system. The CORD (Collections, Objects, Roles and Domains) model is developed which is richer in appropriate modelling abstractions than current Object Models. A flexible Object Prototyping tool based on a Semantic Data Storage Manager has been developed which enables a variety of models to be stored and experimented with. A statistical summary table model COST (Collections of Objects Statistical Table) has been developed within CORD and is shown to be adequate to meet the modelling needs of Decision Support and Executive Information Systems. The COST model is supported by a statistical table creator and editor COSTed which is also built on top of the Object Prototyper and uses the CORD model to manage its metadata.

Data analysis methods in optometry Part 7: multiple linear regression

Multiple regression analysis is a complex statistical method with many potential uses. It has also become one of the most abused of all statistical procedures since anyone with a data base and suitable software can carry it out. An investigator should always have a clear hypothesis in mind before carrying out such a procedure and knowledge of the limitations of each aspect of the analysis. In addition, multiple regression is probably best used in an exploratory context, identifying variables that might profitably be examined by more detailed studies. Where there are many variables potentially influencing Y, they are likely to be intercorrelated and to account for relatively small amounts of the variance. Any analysis in which R squared is less than 50% should be suspect as probably not indicating the presence of significant variables. A further problem relates to sample size. It is often stated that the number of subjects or patients must be at least 5-10 times the number of variables included in the study.5 This advice should be taken only as a rough guide but it does indicate that the variables included should be selected with great care as inclusion of an obviously unimportant variable may have a significant impact on the sample size required.

Comparing transport emissions and impacts for energy recovery from domestic waste (EfW): centralised and distributed disposal options for two UK Counties

Many local authorities (LAs) are currently working to reduce both greenhouse gas emissions and the amount of municipal solid waste (MSW) sent to landfill. The recovery of energy from waste (EfW) can assist in meeting both of these objectives. The choice of an EfW policy combines spatial and non-spatial decisions which may be handled using Multi-Criteria Analysis (MCA) and Geographic Information Systems (GIS). This paper addresses the impact of transporting MSW to EfW facilities, analysed as part of a larger decision support system designed to make an overall policy assessment of centralised (large-scale) and distributed (local-scale) approaches. Custom-written ArcMap extensions are used to compare centralised versus distributed approaches, using shortest-path routing based on expected road speed. Results are intersected with 1-kilometre grids and census geographies for meaningful maps of cumulative impact. Case studies are described for two counties in the United Kingdom (UK); Cornwall and Warwickshire. For both case study areas, centralised scenarios generate more traffic, fuel costs and emitted carbon per tonne of MSW processed.

Designing a safety management support system using open architecture databases: the requirements of information representation in the support of managers' risk-related decision-making

This research was conducted at the Space Research and Technology Centre o the European Space Agency at Noordvijk in the Netherlands. ESA is an international organisation that brings together a range of scientists, engineers and managers from 14 European member states. The motivation for the work was to enable decision-makers, in a culturally and technologically diverse organisation, to share information for the purpose of making decisions that are well informed about the risk-related aspects of the situations they seek to address. The research examined the use of decision support system DSS) technology to facilitate decision-making of this type. This involved identifying the technology available and its application to risk management. Decision-making is a complex activity that does not lend itself to exact measurement or precise understanding at a detailed level. In view of this, a prototype DSS was developed through which to understand the practical issues to be accommodated and to evaluate alternative approaches to supporting decision-making of this type. The problem of measuring the effect upon the quality of decisions has been approached through expert evaluation of the software developed. The practical orientation of this work was informed by a review of the relevant literature in decision-making, risk management, decision support and information technology. Communication and information technology unite the major the,es of this work. This allows correlation of the interests of the research with European public policy. The principles of communication were also considered in the topic of information visualisation - this emerging technology exploits flexible modes of human computer interaction (HCI) to improve the cognition of complex data. Risk management is itself an area characterised by complexity and risk visualisation is advocated for application in this field of endeavour. The thesis provides recommendations for future work in the fields of decision=making, DSS technology and risk management.

Planning and decision-making for energy recovery from waste at the sub-regional level

This research project has developed a novel decision support system using Geographical Information Systems and Multi Criteria Decision Analysis and used it to develop and evaluate energy-from-waste policy options. The system was validated by applying it to the UK administrative areas of Cornwall and Warwickshire. Different strategies have been defined by the size and number of the facilities, as well as the technology chosen. Using sensitivity on the results from the decision support system, it was found that key decision criteria included those affected by cost, energy efficiency, transport impacts and air/dioxin emissions. The conclusions of this work are that distributed small-scale energy-from-waste facilities score most highly overall and that scale is more important than technology design in determining overall policy impact. This project makes its primary contribution to energy-from-waste planning by its development of a Decision Support System that can be used to assist waste disposal authorities to identify preferred energy-from-waste options that have been tailored specifically to the socio-geographic characteristics of their jurisdictional areas. The project also highlights the potential of energy-from-waste policies that are seldom given enough attention to in the UK, namely those of a smaller-scale and distributed nature that often have technology designed specifically to cater for this market.

GEO Label – quality information interrogation tool for geospatial datasets:towards effective visualization of quality metadata

Although the importance of dataset fitness-for-use evaluation and intercomparison is widely recognised within the GIS community, no practical tools have yet been developed to support such interrogation. GeoViQua aims to develop a GEO label which will visually summarise and allow interrogation of key informational aspects of geospatial datasets upon which users rely when selecting datasets for use. The proposed GEO label will be integrated in the Global Earth Observation System of Systems (GEOSS) and will be used as a value and trust indicator for datasets accessible through the GEO Portal. As envisioned, the GEO label will act as a decision support mechanism for dataset selection and thereby hopefully improve user recognition of the quality of datasets. To date we have conducted 3 user studies to (1) identify the informational aspects of geospatial datasets upon which users rely when assessing dataset quality and trustworthiness, (2) elicit initial user views on a GEO label and its potential role and (3), evaluate prototype label visualisations. Our first study revealed that, when evaluating quality of data, users consider 8 facets: dataset producer information; producer comments on dataset quality; dataset compliance with international standards; community advice; dataset ratings; links to dataset citations; expert value judgements; and quantitative quality information. Our second study confirmed the relevance of these facets in terms of the community-perceived function that a GEO label should fulfil: users and producers of geospatial data supported the concept of a GEO label that provides a drill-down interrogation facility covering all 8 informational aspects. Consequently, we developed three prototype label visualisations and evaluated their comparative effectiveness and user preference via a third user study to arrive at a final graphical GEO label representation. When integrated in the GEOSS, an individual GEO label will be provided for each dataset in the GEOSS clearinghouse (or other data portals and clearinghouses) based on its available quality information. Producer and feedback metadata documents are being used to dynamically assess information availability and generate the GEO labels. The producer metadata document can either be a standard ISO compliant metadata record supplied with the dataset, or an extended version of a GeoViQua-derived metadata record, and is used to assess the availability of a producer profile, producer comments, compliance with standards, citations and quantitative quality information. GeoViQua is also currently developing a feedback server to collect and encode (as metadata records) user and producer feedback on datasets; these metadata records will be used to assess the availability of user comments, ratings, expert reviews and user-supplied citations for a dataset. The GEO label will provide drill-down functionality which will allow a user to navigate to a GEO label page offering detailed quality information for its associated dataset. At this stage, we are developing the GEO label service that will be used to provide GEO labels on demand based on supplied metadata records. In this presentation, we will provide a comprehensive overview of the GEO label development process, with specific emphasis on the GEO label implementation and integration into the GEOSS.

A sustainable supply chain study of the Indian bioenergy sector

In India, more than one third of the population do not currently have access to modern energy services. Biomass to energy, known as bioenergy, has immense potential for addressing India’s energy poverty. Small scale decentralised bioenergy systems require low investment compared to other renewable technologies and have environmental and social benefits over fossil fuels. Though they have historically been promoted in India through favourable policies, many studies argue that the sector’s potential is underutilised due to sustainable supply chain barriers. Moreover, a significant research gap exists. This research addresses the gap by analysing the potential sustainable supply chain risks of decentralised small scale bioenergy projects. This was achieved through four research objectives, using various research methods along with multiple data collection techniques. Firstly, a conceptual framework was developed to identify and analyse these risks. The framework is founded on existing literature and gathered inputs from practitioners and experts. Following this, sustainability and supply chain issues within the sector were explored. Sustainability issues were collated into 27 objectives, and supply chain issues were categorised according to related processes. Finally, the framework was validated against an actual bioenergy development in Jodhpur, India. Applying the framework to the action research project had some significant impacts upon the project’s design. These include the development of water conservation arrangements, the insertion of auxiliary arrangements, measures to increase upstream supply chain resilience, and the development of a first aid action plan. More widely, the developed framework and identified issues will help practitioners to take necessary precautionary measures and address them quickly and cost effectively. The framework contributes to the bioenergy decision support system literature and the sustainable supply chain management field by incorporating risk analysis and introducing the concept of global and organisational sustainability in supply chains. The sustainability issues identified contribute to existing knowledge through the exploration of a small scale and developing country context. The analysis gives new insights into potential risks affecting the whole bioenergy supply chain.

GIS and optimisation:potential benefits for emergency facility location in humanitarian logistics

Floods are one of the most dangerous and common disasters worldwide, and these disasters are closely linked to the geography of the affected area. As a result, several papers in the academic field of humanitarian logistics have incorporated the use of Geographical Information Systems (GIS) for disaster management. However, most of the contributions in the literature are using these systems for network analysis and display, with just a few papers exploiting the capabilities of GIS to improve planning and preparedness. To show the capabilities of GIS for disaster management, this paper uses raster GIS to analyse potential flooding scenarios and provide input to an optimisation model. The combination is applied to two real-world floods in Mexico to evaluate the value of incorporating GIS for disaster planning. The results provide evidence that including GIS analysis for a decision-making tool in disaster management can improve the outcome of disaster operations by reducing the number of facilities used at risk of flooding. Empirical results imply the importance of the integration of advanced remote sensing images and GIS for future systems in humanitarian logistics.

Perceptions and experiences of the implementation, management, use and optimisation of electronic prescribing systems in hospital settings:protocol for a systematic review of qualitative studies

Introduction: There is increasing evidence that electronic prescribing (ePrescribing) or computerised provider/physician order entry (CPOE) systems can improve the quality and safety of healthcare services. However, it has also become clear that their implementation is not straightforward and may create unintended or undesired consequences once in use. In this context, qualitative approaches have been particularly useful and their interpretative synthesis could make an important and timely contribution to the field. This review will aim to identify, appraise and synthesise qualitative studies on ePrescribing/CPOE in hospital settings, with or without clinical decision support. Methods and analysis: Data sources will include the following bibliographic databases: MEDLINE, MEDLINE In Process, EMBASE, PsycINFO, Social Policy and Practice via Ovid, CINAHL via EBSCO, The Cochrane Library (CDSR, DARE and CENTRAL databases), Nursing and Allied Health Sources, Applied Social Sciences Index and Abstracts via ProQuest and SCOPUS. In addition, other sources will be searched for ongoing studies (ClinicalTrials.gov) and grey literature: Healthcare Management Information Consortium, Conference Proceedings Citation Index (Web of Science) and Sociological abstracts. Studies will be independently screened for eligibility by 2 reviewers. Qualitative studies, either standalone or in the context of mixed-methods designs, reporting the perspectives of any actors involved in the implementation, management and use of ePrescribing/CPOE systems in hospital-based care settings will be included. Data extraction will be conducted by 2 reviewers using a piloted form. Quality appraisal will be based on criteria from the Critical Appraisal Skills Programme checklist and Standards for Reporting Qualitative Research. Studies will not be excluded based on quality assessment. A postsynthesis sensitivity analysis will be undertaken. Data analysis will follow the thematic synthesis method. Ethics and dissemination: The study does not require ethical approval as primary data will not be collected. The results of the study will be published in a peer-reviewed journal and presented at relevant conferences.