Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

Continuous phase synchronised drives (for a rod-making machine)

Traditional high speed machinery actuators are powered and coordinated by mechanical linkages driven from a central drive, but these linkages may be replaced by independently synchronised electric drives. Problems associated with utilising such electric drives for this form of machinery were investigated. The research concentrated on a high speed rod-making machine, which required control of high inertias (0.01-0.5kgm2), at continuous high speed (2500 r/min), with low relative phase errors between two drives (0.0025 radians). Traditional minimum energy drive selection techniques for incremental motions were not applicable to continuous applications which require negligible energy dissipation. New selection techniques were developed. A brushless configuration constant enabled the comparison between seven different servo systems; the rate earth brushless drives had the best power rates which is a performance measure. Simulation was used to review control strategies, such that a microprocessor controller with a proportional velocity loop within a proportional position loop with velocity feedforward was designed. Local control schemes were investigated as means of reducing relative errors between drives: the slave of a master/slave scheme compensates for the master's errors: the matched scheme has drives with similar absolute errors so the relative error is minimised, and the feedforward scheme minimises error by adding compensation from previous knowledge. Simulation gave an approximate velocity loop bandwidth and position loop gain required to meet the specification. Theoretical limits for these parameters were defined in terms of digital sampling delays, quantisation, and system phase shifts. Performance degradation due to mechanical backlash was evaluated. Thus any drive could be checked to ensure that the performance specification could be realised. A two drive demonstrator was commissioned with 0.01kgm2 loads. By use of simulation the performance of one drive was improved by increasing the velocity loop bandwidth fourfold. With the master/slave scheme relative errors were within 0.0024 radians at a constant 2500 r/min for two 0.01 kgm^2 loads.

Firm-level productivity growth in Northern Ireland:the impact of exporting, innovation and public financial assistance

Motivated by the historically poor productivity performance of Northern Ireland firms and the longstanding productivity gap with the UK, the aim of this thesis is to examine, through the use of firm-level data, how exporting, innovation and public financial assistance impact on firm productivity growth. These particular activities are investigated due to the continued policy focus on their link to productivity growth and the theoretical claims of a direct positive relationship. In order to undertake these analyses a newly constructed dataset is used which links together cross-sectional and longitudinal data over the 1998-2008 period from the Annual Business Survey, the Manufacturing Sales and Export Survey; the Community Innovation Survey and Invest NI Selective Financial Assistance (SFA) payment data. Econometric methodologies are employed to estimate each of the relationships with regards to productivity growth, making use in particular of Heckman selection techniques and propensity score matching to take account of critical issues of endogeneity and selection bias. The results show that more productive firms self-select into exporting but there is no resulting productivity effect from starting to export; contesting the argument for learning-by-exporting. Product innovation is also found to have no impact on productivity growth over a four year period but there is evidence of a negative process innovation impact, likely to reflect temporary learning effects. Finally SFA assistance, including the amount of the payment, is found to have no short term impact on productivity growth suggesting substantial deadweight effects and/or targeting of inefficient firms. The results provide partial evidence as to why Northern Ireland has failed to narrow the productivity gap with the rest of the UK. The analyses further highlight the need for access to comprehensive firm-level data for research purposes, not least to underpin robust evidence-based policymaking.

Selection and application of risk management tools and techniques for build-operate-transfer projects

Construction projects are risky. However, the characteristics of the risk highly depend on the type of procurement being adopted for managing the project. A build-operate-transfer (BOT) project is recognized as one of the most risky project schemes. There are instances of project failure where a BOT scheme was employed. Ineffective rts are increasingly being managed using various risk management tools and techniques. However, application of those tools depends on the nature of the project, organization's policy, project management strategy, risk attitude of the project team members, and availability of the resources. Understanding of the contents and contexts of BOT projects, together with a thorough understanding of risk management tools and techniques, helps select processes of risk management for effective project implementation in a BOT scheme. This paper studies application of risk management tools and techniques in BOT projects through reviews of relevant literatures and develops a model for selecting risk management process for BOT projects. The application to BOT projects is considered from the viewpoints of the major project participants. Discussion is also made with regard to political risks. This study would contribute to the establishment of a framework for systematic risk management in BOT projects.

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

A study of cluster analysis techniques and their applications

This thesis seeks to describe the development of an inexpensive and efficient clustering technique for multivariate data analysis. The technique starts from a multivariate data matrix and ends with graphical representation of the data and pattern recognition discriminant function. The technique also results in distances frequency distribution that might be useful in detecting clustering in the data or for the estimation of parameters useful in the discrimination between the different populations in the data. The technique can also be used in feature selection. The technique is essentially for the discovery of data structure by revealing the component parts of the data. lhe thesis offers three distinct contributions for cluster analysis and pattern recognition techniques. The first contribution is the introduction of transformation function in the technique of nonlinear mapping. The second contribution is the us~ of distances frequency distribution instead of distances time-sequence in nonlinear mapping, The third contribution is the formulation of a new generalised and normalised error function together with its optimal step size formula for gradient method minimisation. The thesis consists of five chapters. The first chapter is the introduction. The second chapter describes multidimensional scaling as an origin of nonlinear mapping technique. The third chapter describes the first developing step in the technique of nonlinear mapping that is the introduction of "transformation function". The fourth chapter describes the second developing step of the nonlinear mapping technique. This is the use of distances frequency distribution instead of distances time-sequence. The chapter also includes the new generalised and normalised error function formulation. Finally, the fifth chapter, the conclusion, evaluates all developments and proposes a new program. for cluster analysis and pattern recognition by integrating all the new features.

Development of an e-business selection framework for manufacturing SMEs : a study of the printing industry

The aim of the research is to develop an e-business selection framework for small and medium enterprises (SMEs) by integrating established techniques in planning. The research is case based, comprising four case studies carried out in the printing industry for the purpose of evaluating the framework. Two of the companies are from Singapore, while the other two are from Guangzhou, China and Jinan, China respectively. To determine the need of an e-business selection framework for SMEs, extensive literature reviews were carried out in the area of e-business, business planning frameworks, SMEs and the printing industry. An e-business selection framework is then proposed by integrating the three established techniques of the Balanced Scorecard (BSC), Value Chain Analysis (VCA) and Quality Function Deployment (QFD). The newly developed selection framework is pilot tested using a published case study before actual evaluation is carried out in four case study companies. The case study methodology was chosen because of its ability to integrate diverse data collection techniques required to generate the BSC, VCA and QFD for the selection framework. The findings of the case studies revealed that the three techniques of BSC, VCA and QFD can be integrated seamlessly to complement on each other’s strengths in e-business planning. The eight-step methodology of the selection framework can provide SMEs with a step-by-step approach to e-business through structured planning. Also, the project has also provided better understanding and deeper insights into SMEs in the printing industry.

Characterisation and surface-profiling techniques for composite particles produced by dry powder coating in pharmaceutical drug delivery

The production of composite particles using dry powder coating is a one-step, environmentally friendly, process for the fabrication of particles with targeted properties and favourable functionalities. Diverse functionalities, such flowability enhancement, content uniformity, and dissolution, can be developed from dry particle coating. In this review, we discuss the particle functionalities that can be tailored and the selection of characterisation techniques relevant to understanding their molecular basis. We address key features in the powder blend sampling process and explore the relevant characterisation techniques, focussing on the functionality delivered by dry coating and on surface profiling that explores the dynamics and surface characteristics of the composite blends. Dry particle coating is a solvent- and heat-free process that can be used to develop functionalised particles. However, assessment of the resultant functionality requires careful selection of sensitive analytical techniques that can distinguish particle surface changes within nano and/or micrometre ranges.

A method for systematic artifact selection decision making

Artifact selection decisions typically involve the selection of one from a number of possible/candidate options (decision alternatives). In order to support such decisions, it is important to identify and recognize relevant key issues of problem solving and decision making (Albers, 1996; Harris, 1998a, 1998b; Jacobs & Holten, 1995; Loch & Conger, 1996; Rumble, 1991; Sauter, 1999; Simon, 1986). Sauter classifies four problem solving/decision making styles: (1) left-brain style, (2) right-brain style, (3) accommodating, and (4) integrated (Sauter, 1999). The left-brain style employs analytical and quantitative techniques and relies on rational and logical reasoning. In an effort to achieve predictability and minimize uncertainty, problems are explicitly defined, solution methods are determined, orderly information searches are conducted, and analysis is increasingly refined. Left-brain style decision making works best when it is possible to predict/control, measure, and quantify all relevant variables, and when information is complete. In direct contrast, right-brain style decision making is based on intuitive techniques—it places more emphasis on feelings than facts. Accommodating decision makers use their non-dominant style when they realize that it will work best in a given situation. Lastly, integrated style decision makers are able to combine the left- and right-brain styles—they use analytical processes to filter information and intuition to contend with uncertainty and complexity.

A survey of UK selection practices across different organization sizes and industry sectors

This paper presents results of a study examining the methods used to select employees in 579 UK organizations representing a range of different organization sizes and industry sectors. Overall, a smaller proportion of organizations in this sample reported using formalized methods (e.g., assessment centres) than informal methods (e.g., unstructured interviews). The curriculum vitae (CVs) was the most commonly used selection method, followed by the traditional triad of application form, interviews, and references. Findings also indicated that the use of different selection methods was similar in both large organizations and small-to-medium-sized enterprises. Differences were found across industry sector with public and voluntary sectors being more likely to use formalized techniques (e.g., application forms rather than CVs and structured rather than unstructured interviews). The results are discussed in relation to their implications, both in terms of practice and future research.

Selection of simulation tools for improving supply chain performance

Simulation is an effective method for improving supply chain performance. However, there is limited advice available to assist practitioners in selecting the most appropriate method for a given problem. Much of the advice that does exist relies on custom and practice rather than a rigorous conceptual or empirical analysis. An analysis of the different modelling techniques applied in the supply chain domain was conducted, and the three main approaches to simulation used were identified; these are System Dynamics (SD), Discrete Event Simulation (DES) and Agent Based Modelling (ABM). This research has examined these approaches in two stages. Firstly, a first principles analysis was carried out in order to challenge the received wisdom about their strengths and weaknesses and a series of propositions were developed from this initial analysis. The second stage was to use the case study approach to test these propositions and to provide further empirical evidence to support their comparison. The contributions of this research are both in terms of knowledge and practice. In terms of knowledge, this research is the first holistic cross paradigm comparison of the three main approaches in the supply chain domain. Case studies have involved building ‘back to back’ models of the same supply chain problem using SD and a discrete approach (either DES or ABM). This has led to contributions concerning the limitations of applying SD to operational problem types. SD has also been found to have risks when applied to strategic and policy problems. Discrete methods have been found to have potential for exploring strategic problem types. It has been found that discrete simulation methods can model material and information feedback successfully. Further insights have been gained into the relationship between modelling purpose and modelling approach. In terms of practice, the findings have been summarised in the form of a framework linking modelling purpose, problem characteristics and simulation approach.

Transferring knowledge of manufacturing techniques in multinational corporations:preliminary findings

With the growth of the multinational corporation (MNC) has come the need to understand how parent companies transfer knowledge to, and manage the operations of, their subsidiaries. This is of particular interest to manufacturing companies transferring their operations overseas. Japanese companies in particular have been pioneering in this regard, with techniques such as the Toyota Production System (TPS) for transferring the ethos of Japanese manufacturing and maintaining quality and control in overseas subsidiaries. A great deal has been written about the process of transferring Japanese manufacturing techniques, but much less is understood about how the subsidiaries themselves, which are required to make use of such techniques, actually acquire and incorporate them into their operations. The research on which this paper is based therefore examines how, from the perspective of the subsidiary, knowledge of manufacturing techniques is transferred from the parent company. There is clearly a need to take a practice-based view to understanding how the local managers and operatives incorporate knowledge about manufacturing techniques into their working practices. In-depth qualitative research was, therefore, conducted in the subsidiary of a Japanese multinational, Denso Corporation, involving three main manufacturing initiatives (or philosophies), namely ‘TPS’, ‘TPM’ and ‘TS’. The case data were derived from 52 in-depth interviews with project members, moderate participant observations, and documentations. The aim of this paper is to present the preliminary findings from the case analyses. The research contributes to our understanding of knowledge transfer in relation to the circumstances of the selection between adaptation and replication of knowledge in the subsidiary from its parent. In particular this understanding relates to transfer across different flows and levels in the organisational hierarchy, how the whole process is managed, and also how modification takes place.

A method for systematic artifact selection decision making

Artifact selection decisions typically involve the selection of one from a number of possible/candidate options (decision alternatives). In order to support such decisions, it is important to identify and recognize relevant key issues of problem solving and decision making (Albers, 1996; Harris, 1998a, 1998b; Jacobs & Holten, 1995; Loch & Conger, 1996; Rumble, 1991; Sauter, 1999; Simon, 1986). Sauter classifies four problem solving/decision making styles: (1) left-brain style, (2) right-brain style, (3) accommodating, and (4) integrated (Sauter, 1999). The left-brain style employs analytical and quantitative techniques and relies on rational and logical reasoning. In an effort to achieve predictability and minimize uncertainty, problems are explicitly defined, solution methods are determined, orderly information searches are conducted, and analysis is increasingly refined. Left-brain style decision making works best when it is possible to predict/control, measure, and quantify all relevant variables, and when information is complete. In direct contrast, right-brain style decision making is based on intuitive techniques—it places more emphasis on feelings than facts. Accommodating decision makers use their non-dominant style when they realize that it will work best in a given situation. Lastly, integrated style decision makers are able to combine the left- and right-brain styles—they use analytical processes to filter information and intuition to contend with uncertainty and complexity.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments

Integrated supplier selection and order allocation is an important decision for both designing and operating supply chains. This decision is often influenced by the concerned stakeholders, suppliers, plant operators and customers in different tiers. As firms continue to seek competitive advantage through supply chain design and operations they aim to create optimized supply chains. This calls for on one hand consideration of multiple conflicting criteria and on the other hand consideration of uncertainties of demand and supply. Although there are studies on supplier selection using advanced mathematical models to cover a stochastic approach, multiple criteria decision making techniques and multiple stakeholder requirements separately, according to authors' knowledge there is no work that integrates these three aspects in a common framework. This paper proposes an integrated method for dealing with such problems using a combined Analytic Hierarchy Process-Quality Function Deployment (AHP-QFD) and chance constrained optimization algorithm approach that selects appropriate suppliers and allocates orders optimally between them. The effectiveness of the proposed decision support system has been demonstrated through application and validation in the bioenergy industry.