An ingenuous Decision Support System (iDSS) approach for remote area pregnant women

This paper addresses development of an ingenious decision support system (iDSS) based on the methodology of survey instruments and identification of significant variables to be used in iDSS using statistical analysis. A survey was undertaken with pregnant women and factorial experimental design was chosen to acquire sample size. Variables with good reliability in any one of the statistical techniques such as Chi-square, Cronbach’s α and Classification Tree were incorporated in the iDSS. The ingenious decision support system was implemented with Visual Basic as front end and Microsoft SQL server management as backend. Outcome of the ingenious decision support system include advice on Symptoms, Diet and Exercise to pregnant women.

A flexible asset maintenance decision-making process model

Optimal Asset Maintenance decisions are imperative for efficient asset management. Decision Support Systems are often used to help asset managers make maintenance decisions, but high quality decision support must be based on sound decision-making principles. For long-lived assets, a successful Asset Maintenance decision-making process must effectively handle multiple time scales. For example, high-level strategic plans are normally made for periods of years, while daily operational decisions may need to be made within a space of mere minutes. When making strategic decisions, one usually has the luxury of time to explore alternatives, whereas routine operational decisions must often be made with no time for contemplation. In this paper, we present an innovative, flexible decision-making process model which distinguishes meta-level decision making, i.e., deciding how to make decisions, from the information gathering and analysis steps required to make the decisions themselves. The new model can accommodate various decision types. Three industrial case studies are given to demonstrate its applicability.

Modification of advanced programmatic risk analysis and management model for the whole project life cycle’s risks

The advanced programmatic risk analysis and management model (APRAM) is one of the recently developed methods that can be used for risk analysis and management purposes considering schedule, cost, and quality risks simultaneously. However, this model considers those failure risks that occur only over the design and construction phases of a project’s life cycle. While it can be sufficient for some projects for which the required cost during the operating life is much less than the budget required over the construction period, it should be modified in relation to infrastructure projects because the associated costs during the operating life cycle are significant. In this paper, a modified APRAM is proposed, which can consider potential risks that might occur over the entire life cycle of the project, including technical and managerial failure risks. Therefore, the modified model can be used as an efficient decision-support tool for construction managers in the housing industry in which various alternatives might be technically available. The modified method is demonstrated by using a real building project, and this demonstration shows that it can be employed efficiently by construction managers. The Delphi method was applied in order to figure out the failure events and their associated probabilities. The results show that although the initial cost of a cold-formed steel structural system is higher than a conventional construction system, the former’s failure cost is much lower than the latter’s

Reputation modelling in Citizen Science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Data reliability issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. Participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data reliability has become an urgent demand. This study aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we propose to design a reputation framework to enhance data reliability and also investigate some critical elements that should be aware of during developing and designing new reputation systems.

Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

Analysis of environmental efficiency variation : a materials balance approach

Recent literature has argued that environmental efficiency (EE), which is built on the materials balance (MB) principle, is more suitable than other EE measures in situations where the law of mass conversation regulates production processes. In addition, the MB-based EE method is particularly useful in analysing possible trade-offs between cost and environmental performance. Identifying determinants of MB-based EE can provide useful information to decision makers but there are very few empirical investigations into this issue. This article proposes the use of data envelopment analysis and stochastic frontier analysis techniques to analyse variation in MB-based EE. Specifically, the article develops a stochastic nutrient frontier and nutrient inefficiency model to analyse determinants of MB-based EE. The empirical study applies both techniques to investigate MB-based EE of 96 rice farms in South Korea. The size of land, fertiliser consumption intensity, cost allocative efficiency, and the share of owned land out of total land are found to be correlated with MB-based EE. The results confirm the presence of a trade-off between MB-based EE and cost allocative efficiency and this finding, favouring policy interventions to help farms simultaneously achieve cost efficiency and MP-based EE.

Analysis of productive performance of crop production systems: an integrated analytical framework

Recent literature has argued that environmental efficiency (EE), which is built on the materials balance (MB) principle, is more suitable than other EE measures in situations where the law of mass conversation regulates production processes. In addition, the MB-based EE method is particularly useful in analysing possible trade-offs between cost and environmental performance. Identifying determinants of MB-based EE can provide useful information to decision makers but there are very few empirical investigations into this issue. This article proposes the use of data envelopment analysis and stochastic frontier analysis techniques to analyse variation in MB-based EE. Specifically, the article develops a stochastic nutrient frontier and nutrient inefficiency model to analyse determinants of MB-based EE. The empirical study applies both techniques to investigate MB-based EE of 96 rice farms in South Korea. The size of land, fertiliser consumption intensity, cost allocative efficiency, and the share of owned land out of total land are found to be correlated with MB-based EE. The results confirm the presence of a trade-off between MB-based EE and cost allocative efficiency and this finding, favouring policy interventions to help farms simultaneously achieve cost efficiency and MP-based EE.

‘Hot chicks’, ‘better parties’ or academic stuff : perceptions of a regional university campus using Repertory Grid Analysis and Importance-performance Analysis

In the increasingly competitive Australian tertiary education market, a consumer orientation is essential. This is particularly so for small regional campuses competing with larger universities in the state capitals. Campus management need to carefully monitor both the perceptions of prospective students within the catchment area, and the (dis)satisfaction levels of current students. This study reports the results of an exploratory investigation into the perceptions held of a regional campus, using two techniques that have arguably been underutilised in the education marketing literature. Repertory Grid Analysis, a technique developed almost fifty years ago, was used to identify attributes deemed salient to year 12 high school students at the time they were applying for university places. Importance-performance analysis (IPA), developed three decades ago, was then used to identify attributes that were determinant for a new cohort of first year undergraduate students. The paper concludes that group applications of Repertory Grid offer education market researchers a useful means for identifying attributes used by high school students to differentiate universities, and that IPA is a useful technique for guiding promotional decision making. In this case, the two techniques provided a quick, economical and effective snapshot of market perceptions, which can be used as a foundation for the development of an ongoing market research programme.

Integration of opinion into customer analysis model

As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. In order to enhance customer satisfaction and their shopping experiences, it has become important to analysis customers reviews to extract opinions on the products that they buy. Thus, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’ attributes.

Application of multi-criteria decision making methods to compression ignition engine efficiency and gaseous, particulate and greenhouse gas emissions

Compression ignition (CI) engine design is subject to many constraints which presents a multi-criteria optimisation problem that the engine researcher must solve. In particular, the modern CI engine must not only be efficient, but must also deliver low gaseous, particulate and life cycle greenhouse gas emissions so that its impact on urban air quality, human health, and global warming are minimised. Consequently, this study undertakes a multi-criteria analysis which seeks to identify alternative fuels, injection technologies and combustion strategies that could potentially satisfy these CI engine design constraints. Three datasets are analysed with the Preference Ranking Organization Method for Enrichment Evaluations and Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) algorithm to explore the impact of 1): an ethanol fumigation system, 2): alternative fuels (20 % biodiesel and synthetic diesel) and alternative injection technologies (mechanical direct injection and common rail injection), and 3): various biodiesel fuels made from 3 feedstocks (i.e. soy, tallow, and canola) tested at several blend percentages (20-100 %) on the resulting emissions and efficiency profile of the various test engines. The results show that moderate ethanol substitutions (~20 % by energy) at moderate load, high percentage soy blends (60-100 %), and alternative fuels (biodiesel and synthetic diesel) provide an efficiency and emissions profile that yields the most “preferred” solutions to this multi-criteria engine design problem. Further research is, however, required to reduce Reactive Oxygen Species (ROS) emissions with alternative fuels, and to deliver technologies that do not significantly reduce the median diameter of particle emissions.

Decision-making about hormone replacement therapy by women in England and Scotland

OBJECTIVE: The aim of this study was to explore women's decision-making about the balance of risks and benefits of taking hormone replacement therapy (HRT) based on the latest evidence from the Women's Health Initiative (WHI) trial of combined HRT. METHODS: Women aged 50-69 years, who were eligible for the Women's International Study of long Duration Oestrogen after Menopause (WISDOM) trial, were invited to participate in one of eight focus groups. Participants were asked to discuss their views about taking HRT based on the latest international evidence. RESULTS AND CONCLUSIONS: Eighty-two women participated overall. Qualitative content analysis was applied to the discussion transcripts. Women regarded the decisions they make about taking HRT as highly personal, and, for women currently taking HRT, the overwhelming reason for continuation was perceived improvement in quality of life regardless of either the risks or the benefits in the longer term.

A comparative analysis of the integration of SOA elements in widely-used enterprise architecture frameworks

In recent years, enterprise architecture (EA) has captured increasing interest as a means to systematically consolidate and manage various enterprise artefacts in order to provide holistic decision support for business/IT alignment and business/IT landscapes management. To provide a holistic perspective on the enterprise over time, EA frameworks need to co-evolve with the changes in the enterprise and its IT over time. In this paper we focus on the emergence of Service-Oriented Architecture (SOA). There is a need to integrate SOA with EA to keep EA relevant and to use EA products to help drive successful SOA. This paper investigates and compares the integration of SOA elements in five widely used EA frameworks: Archimate, The Open Group Architecture Framework (TOGAF), Federal Enterprise Architecture Framework (FEAF), Department of Defence Architecture Framework (DoDAF) and the Ministry of Defence Architecture Framework (MODAF). It identifies what SOA elements are considered and their relative position in the overall structure. The results show that services and related elements are far from being well-integrated constructs in current EA frameworks and that the different EA frameworks integrated SOA elements in substantially different ways. Our results can support the academic EA and SOA communities with a closer and more consistent integration of EA and SOA and support practitioners in identifying an EA framework that provides the SOA support that matches their requirements.

Surgical site infection prevention following total hip arthroplasty in Australia : a cost-effectiveness analysis

Background: Surgical site infection (SSI) is associated with substantial costs for health services, reduced quality of life, and functional outcomes. The aim of this study was to evaluate the cost-effectiveness of strategies claiming to reduce the risk of SSI in hip arthroplasty in Australia. Methods: Baseline use of antibiotic prophylaxis (AP) was compared with no antibiotic prophylaxis (no AP), antibiotic-impregnated cement (AP þ ABC), and laminar air operating rooms (AP þ LOR). A Markov model was used to simulate long-term health and cost outcomes of a hypothetical cohort of 30,000 total hip arthroplasty patients from a health services perspective. Model parameters were informed by the best available evidence. Uncertainty was explored in probabilistic sensitivity and scenario analyses. Results: Stopping the routine use of AP resulted in over Australian dollars (AUD) $1.5 million extra costs and a loss of 163 quality-adjusted life years (QALYs). Using antibiotic cement in addition to AP (AP þ ABC)generated an extra 32 QALYs while saving over AUD $123,000. The use of laminar air operating rooms combined with routine AP (AP þ LOR) resulted in an AUD $4.59 million cost increase and 127 QALYs lost compared with the baseline comparator. Conclusion: Preventing deep SSI with antibiotic prophylaxis and antibiotic-impregnated cement has shown to improve health outcomes among hospitalized patients, save lives, and enhance resource allocation. Based on this evidence, the use of laminar air operating rooms is not recommended.

Empirical analysis of knowledge management activities in construction organisations

Knowledge is a commodity. It is a by-product of learning that involves the creation, sharing, processing and possible use of information in the mind of an individual. Knowledge management (KM) is, therefore, concerned with the effective implementation of such activities within the organisation. It is simply the process of leveraging organisational knowledge to deliver a long-term competitive advantage. This paper presents the results of an empirical research investigation into the interaction between different KM activities within the context of construction contracting organisations. The different KM activities include: responsiveness to the knowledge of business environment, knowledge acquisition, knowledge dissemination, and knowledge application. A questionnaire survey was administered to investigate the opinions of construction professionals regarding the intensity of activities currently implemented by their organisations to facilitate knowledge capturing, sharing and application. A total of 149 responses were then used to statistically examine the inter-relationships between the different KM activities as practised by contracting organisations in Hong Kong. The paper presents and discusses the survey findings and proposes recommendations for improving the effectiveness of current KM practices.

Health informatics domain knowledge analysis : an information technology perspective

Health Informatics is an intersection of information technology, several disciplines of medicine and health care. It sits at the common frontiers of health care services including patient centric, processes driven and procedural centric care. From the information technology perspective it can be viewed as computer application in medical and/or health processes for delivering better health care solutions. In spite of the exaggerated hype, this field is having a major impact in health care solutions, in particular health care deliveries, decision making, medical devices and allied health care industries. It also affords enormous research opportunities for new methodological development. Despite the obvious connections between Medical Informatics, Nursing Informatics and Health Informatics, most of the methodologies and approaches used in Health Informatics have so far originated from health system management, care aspects and medical diagnostic. This paper explores reasoning for domain knowledge analysis that would establish Health Informatics as a domain and recognised as an intellectual discipline in its own right.