Comparing the effect of inadequate documentation to the cost management and tendering processes in managing contractor and fixed lump sum contracts in Australia

Purpose of this paper This research aims to examine the effects of inadequate documentation to the cost management & tendering processes in Managing Contractor Contracts using Fixed Lump Sum as a benchmark. Design/methodology/approach A questionnaire survey was conducted with industry practitioners to solicit their views on documentation quality issues associated with the construction industry. This is followed by a series of semi-structured interviews with a purpose of validating survey findings. Findings and value The results showed that documentation quality remains a significant issue, contributing to the industries inefficiency and poor reputation. The level of satisfaction for individual attributes of documentation quality varies. Attributes that do appear to be affected by the choice of procurement method include coordination, build ability, efficiency, completeness and delivery time. Similarly the use and effectiveness of risk mitigation techniques appears to vary between the methods, based on a number of factors such as documentation completeness, early involvement, fast tracking etc. Originality/value of paper This research fills the gap of existing body of knowledge in terms of limited studies on the choice of a project procurement system has an influence on the documentation quality and the level of impact. Conclusions Ultimately research concludes that the entire project team including the client and designers should carefully consider the individual projects requirements and compare those to the trade-offs associated with documentation quality and the procurement method. While documentation quality is definitely an issue to be improved upon, by identifying the projects performance requirements a procurement method can be chosen to maximise the likelihood that those requirements will be met. This allows the aspects of documentation quality considered most important to the individual project to be managed appropriately.

Airborne culturable fungi in naturally ventilated primary school environments in a subtropical climate

There is currently a lack of reference values for indoor air fungal concentrations to allow for the interpretation of measurement results in subtropical school settings. Analysis of the results of this work established that, in the majority of properly maintained subtropical school buildings, without any major affecting events such as floods or visible mould or moisture contamination, indoor culturable fungi levels were driven by outdoor concentration. The results also allowed us to benchmark the “baseline range” concentrations for total culturable fungi, Penicillium spp., Cladosporium spp. and Aspergillus spp. in such school settings. The measured concentration of total culturable fungi and three individual fungal genera were estimated using Bayesian hierarchical modelling. Pooling of these estimates provided a predictive distribution for concentrations at an unobserved school. The results indicated that “baseline” indoor concentration levels for indoor total fungi, Penicillium spp., Cladosporium spp. and Aspergillus spp. in such school settings were generally ≤ 1450, ≤ 680, ≤ 480 and ≤ 90 cfu/m3, respectively, and elevated levels would indicate mould damage in building structures. The indoor/outdoor ratio for most classrooms had 95% credible intervals containing 1, indicating that fungi concentrations are generally the same indoors and outdoors at each school. Bayesian fixed effects regression modeling showed that increasing both temperature and humidity resulted in higher levels of fungi concentration.

SOTrain: A new program for training supervisors and operators in Australian sugar factories

The provision of effective training of supervisors and operators is essential if sugar factories are to operate profitably and in an environmentally sustainable and safe manner. The benefits of having supervisor and operator staff with a high level of operational skills are reduced stoppages, increased recovery, improved sugar quality, reduced damage to equipment, and reduced OH&S and environmental impacts. Training of new operators and supervisors in factories has traditionally relied on on-the-job training of the new or inexperienced staff by experienced supervisors and operators, supplemented by courses conducted by contractors such as Sugar Research Institute (SRI). However there is clearly a need for staff to be able to undertake training at any time, drawing on the content of online courses as required. An improved methodology for the training of factory supervisors and operators has been developed by QUT on behalf of a syndicate of mills. The new methodology provides ‘at factory’ learning via self-paced modules. Importantly, the training resources for each module are designed to support the training programs within sugar factories, thereby establishing a benchmark for training across the sugar industry. The modules include notes, training guides and session plans, guidelines for walkthrough tours of the stations, learning activities, resources such as videos, animations, job aids and competency assessments. The materials are available on the web for registered users in Australian Mills and many activities are best undertaken online. Apart from a few interactive online resources, the materials for each module can also be downloaded. The acronym SOTrain (Supervisor and Operator Training) has been applied to the new training program.

In search of good probability assessors: An experimental comparison of elicitation rules for confidence judgments

In this paper, we use an experimental design to compare the performance of elicitation rules for subjective beliefs. Contrary to previous works in which elicited beliefs are compared to an objective benchmark, we consider a purely subjective belief framework (confidence in one’s own performance in a cognitive task and a perceptual task). The performance of different elicitation rules is assessed according to the accuracy of stated beliefs in predicting success. We measure this accuracy using two main factors: calibration and discrimination. For each of them, we propose two statistical indexes and we compare the rules’ performances for each measurement. The matching probability method provides more accurate beliefs in terms of discrimination, while the quadratic scoring rule reduces overconfidence and the free rule, a simple rule with no incentives, which succeeds in eliciting accurate beliefs. Nevertheless, the matching probability appears to be the best mechanism for eliciting beliefs due to its performances in terms of calibration and discrimination, but also its ability to elicit consistent beliefs across measures and across tasks, as well as its empirical and theoretical properties.

A robust optimization approach for index tracking problem

Index tracking is an investment approach where the primary objective is to keep portfolio return as close as possible to a target index without purchasing all index components. The main purpose is to minimize the tracking error between the returns of the selected portfolio and a benchmark. In this paper, quadratic as well as linear models are presented for minimizing the tracking error. The uncertainty is considered in the input data using a tractable robust framework that controls the level of conservatism while maintaining linearity. The linearity of the proposed robust optimization models allows a simple implementation of an ordinary optimization software package to find the optimal robust solution. The proposed model of this paper employs Morgan Stanley Capital International Index as the target index and the results are reported for six national indices including Japan, the USA, the UK, Germany, Switzerland and France. The performance of the proposed models is evaluated using several financial criteria e.g. information ratio, market ratio, Sharpe ratio and Treynor ratio. The preliminary results demonstrate that the proposed model lowers the amount of tracking error while raising values of portfolio performance measures.

Computer analysis of impact behavior of concrete filled steel tube columns

This paper presents a numerical study of the response of axially loaded concrete filled steel tube (CFST) columns under lateral impact loading using explicit non-linear finite element techniques. The aims of this paper are to evaluate the vulnerability of existing columns to credible impact events as well as to contribute new information towards the safe design of such vulnerable columns. The model incorporates concrete confinement, strain rate effects of steel and concrete, contact between the steel tube and concrete and dynamic relaxation for pre-loading, which is a relatively recent method for applying a pre-loading in the explicit solver. The finite element model was first verified by comparing results with existing experimental results and then employed to conduct a parametric sensitivity analysis. The effects of various structural and load parameters on the impact response of the CFST column were evaluated to identify the key controlling factors. Overall, the major parameters which influence the impact response of the column are the steel tube thickness to diameter ratio, the slenderness ratio and the impact velocity. The findings of this study will enhance the current state of knowledge in this area and can serve as a benchmark reference for future analysis and design of CFST columns under lateral impact.

New graph-based algorithms to efficiently solve large scale open pit mining optimisation problems

In the mining optimisation literature, most researchers focused on two strategic-level and tactical-level open-pit mine optimisation problems, which are respectively termed ultimate pit limit (UPIT) or constrained pit limit (CPIT). However, many researchers indicate that the substantial numbers of variables and constraints in real-world instances (e.g., with 50-1000 thousand blocks) make the CPIT’s mixed integer programming (MIP) model intractable for use. Thus, it becomes a considerable challenge to solve the large scale CPIT instances without relying on exact MIP optimiser as well as the complicated MIP relaxation/decomposition methods. To take this challenge, two new graph-based algorithms based on network flow graph and conjunctive graph theory are developed by taking advantage of problem properties. The performance of our proposed algorithms is validated by testing recent large scale benchmark UPIT and CPIT instances’ datasets of MineLib in 2013. In comparison to best known results from MineLib, it is shown that the proposed algorithms outperform other CPIT solution approaches existing in the literature. The proposed graph-based algorithms leads to a more competent mine scheduling optimisation expert system because the third-party MIP optimiser is no longer indispensable and random neighbourhood search is not necessary.

Parametric study on cement-based soft-hard-soft (SHS) multi-layer composite pavement against blast load

An innovative cement-based soft-hard-soft (SHS) multi-layer composite has been developed for protective infrastructures. Such composite consists of three layers including asphalt concrete (AC), high strength concrete (HSC), and engineered cementitious composites (ECC). A three dimensional benchmark numerical model for this SHS composite as pavement under blast load was established using LSDYNA and validated by field blast test. Parametric studies were carried out to investigate the influence of a few key parameters including thickness and strength of HSC and ECC layers, interface properties, soil conditions on the blast resistance of the composite. The outcomes of this study also enabled the establishment of a damage pattern chart for protective pavement design and rapid repair after blast load. Efficient methods to further improve the blast resistance of the SHS multi-layer pavement system were also recommended.

Speed-based diagnostics of aerodynamic and mass imbalance in large wind turbines

Any kind of imbalance in the operation of a wind turbine has adverse effect on the downstream torsional components as well as tower structure. It is crucial to detect imbalance at its very inception. The identification of the type of imbalance is also required so that appropriate measures of fault accommodation can be performed in the control system. In particular, it is important to distinguish between mass and aerodynamic imbalance. While the former is gradually caused by a structural anomaly (e.g. ice deposition, moisture accumulation inside blade), the latter is generally associated to a fault in the pitch control system. This paper proposes a technique for the detection and identification of imbalance fault in large scale wind turbines. Unlike most other existing method it requires only the rotor speed signal which is readily available in existing turbines. Signature frequencies have been proposed in this work to identify imbalance type based on their physical phenomenology. The performance of this technique has been evaluated by simulations using an existing benchmark model. The effectiveness of the proposed method has been confirmed by the simulation results.

Islamic fund and asset management: The opportunities and challenges of Islamic REITs

Purpose This paper aims to present a review on the issues and challenges for Islamic Funds and Asset Management, particularly the Islamic Real Estate Trusts (I-REITs) available in Malaysia. The key difference between the Islamic and the conventional investment vehicle is mainly the fund needs to adhere to the Shariah framework. Design/methodology/approach The paper reviews and synthesises the relevant literature on the framework of Islamic Asset and Fund Management, particularly the Islamic Real Estate Investment Trusts. The paper then provides insights for further research to address the issues and consider the Shariah framework applicable to other further research works. Findings The paper highlights the opportunities and challenges of Islamic REITs globally. There is a lack of the standardisation in the screening methodology used by the Malaysian I- REITs and Singapore I-REITs as the latter follows the Gulf Cooperation Council (GCC) guideline to capture the investors mainly from the Gulf countries. In term of tenants’ selection, there is similarity between I-REITs and the Socially Responsible Investment (SRI) or ethical investment. The gap between the investments can be bridged if the Islamic funds skewed the investment portfolio towards the social and ethical investment. Even though there is a limitation in the investment universe, I-REITs provide better diversification option and show better performance compared to the equity market during the economic crisis. The introduction of the Shariah-compliant REITs index for Asia Pacific allows the fund managers to benchmark the performance of either the funds or the sector with other investment vehicles. This will encourage more investors to consider I-REIT in the decision making of the asset allocation portfolio and broadening the horizon of the investment. Originality/value The contribution of the study is the examination and analysis of the Shariah framework currently adopted for Islamic REITs. This will assist in the identification of specific issues associated with Islamic REITs that will need to be addressed in the development and application of further research in the aspect of the management and operations to increase the efficiency level and better performance in order to capture more investors in this specific and promising market.

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php

Medical free-text to concept mapping as an information retrieval problem

Concept mapping involves determining relevant concepts from a free-text input, where concepts are defined in an external reference ontology. This is an important process that underpins many applications for clinical information reporting, derivation of phenotypic descriptions, and a number of state-of-the-art medical information retrieval methods. Concept mapping can be cast into an information retrieval (IR) problem: free-text mentions are treated as queries and concepts from a reference ontology as the documents to be indexed and retrieved. This paper presents an empirical investigation applying general-purpose IR techniques for concept mapping in the medical domain. A dataset used for evaluating medical information extraction is adapted to measure the effectiveness of the considered IR approaches. Standard IR approaches used here are contrasted with the effectiveness of two established benchmark methods specifically developed for medical concept mapping. The empirical findings show that the IR approaches are comparable with one benchmark method but well below the best benchmark.

With great structure comes great functionality: Understanding and emulating spider silk

The overarching aim of biomimetic approaches to materials synthesis is to mimic simultaneously the structure and function of a natural material, in such a way that these functional properties can be systematically tailored and optimized. In the case of synthetic spider silk fibers, to date functionalities have largely focused on mechanical properties. A rapidly expanding body of literature documents this work, building on the emerging knowledge of structure–function relationships in native spider silks, and the spinning processes used to create them. Here, we describe some of the benchmark achievements reported until now, with a focus on the last five years. Progress in protein synthesis, notably the expression on full-size spidroins, has driven substantial improvements in synthetic spider silk performance. Spinning technology, however, lags behind and is a major limiting factor in biomimetic production. We also discuss applications for synthetic silk that primarily capitalize on its nonmechanical attributes, and that exploit the remarkable range of structures that can be formed from a synthetic silk feedstock.

Unsupervised Domain Adaptation by Domain Invariant Projection

Domain-invariant representations are key to addressing the domain shift problem where the training and test exam- ples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be di- rectly suitable for such a comparison, since some of the fea- tures may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Domain Invariant Projection approach: An unsupervised domain adaptation method that overcomes this issue by extracting the information that is invariant across the source and tar- get domains. More specifically, we learn a projection of the data to a low-dimensional latent space where the distance between the empirical distributions of the source and target examples is minimized. We demonstrate the effectiveness of our approach on the task of visual object recognition and show that it outperforms state-of-the-art methods on a stan- dard domain adaptation benchmark dataset

Domain Adaptation on the Statistical Manifold

In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions be- come similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that prob- ability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this man- ifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the cor- responding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of- the-art results on a standard object recognition benchmark.