Mobile case-based decision support for intelligent patient knowledge management

Hospitals everywhere are integrating health data using electronic health record (EHR) systems, and disparate and multimedia patient data can be input by different caregivers at different locations as encapsulated patient profiles. Healthcare institutions are also using the flexibility and speed of wireless computing to improve quality and reduce costs. We are developing a mobile application that allows doctors to efficiently record and access complete and accurate real-time patient information. The system integrates medical imagery with textual patient profiles as well as expert interactions by healthcare personnel using knowledge management and case-based reasoning techniques. The application can assist other caregivers in searching large repositories of previous patient cases. Patients' symptoms can be input to a portable device and the application can quickly retrieve similar profiles which can be used to support effective diagnoses and prognoses by comparing symptoms, treatments, diagnosis, test results and other patient information. © 2007 Sage Publications.

A framework for automated construction of resource space based on background knowledge

Resource Space Model is a kind of data model which can effectively and flexibly manage the digital resources in cyber-physical system from multidimensional and hierarchical perspectives. This paper focuses on constructing resource space automatically. We propose a framework that organizes a set of digital resources according to different semantic dimensions combining human background knowledge in WordNet and Wikipedia. The construction process includes four steps: extracting candidate keywords, building semantic graphs, detecting semantic communities and generating resource space. An unsupervised statistical language topic model (i.e., Latent Dirichlet Allocation) is applied to extract candidate keywords of the facets. To better interpret meanings of the facets found by LDA, we map the keywords to Wikipedia concepts, calculate word relatedness using WordNet's noun synsets and construct corresponding semantic graphs. Moreover, semantic communities are identified by GN algorithm. After extracting candidate axes based on Wikipedia concept hierarchy, the final axes of resource space are sorted and picked out through three different ranking strategies. The experimental results demonstrate that the proposed framework can organize resources automatically and effectively.©2013 Published by Elsevier Ltd. All rights reserved.

The translation of cell-based therapies:clinical landscape and manufacturing challenges

Cell-based therapies have the potential to make a large contribution toward currently unmet patient need and thus effective manufacture of these products is essential. Many challenges must be overcome before this can become a reality and a better definition of the manufacturing requirements for cell-based products must be obtained. The aim of this study is to inform industry and academia of current cell-based therapy clinical development and to identify gaps in their manufacturing requirements. A total of 1342 active cell-based therapy clinical trials have been identified and characterized based on cell type, target indication and trial phase. Multiple technologies have been assessed for the manufacture of these cell types in order to facilitate product translation and future process development.

Reshoring:opportunities and limits for manufacturing in the UK - the case of the auto sector

In recent years, offshoring and outsourcing have transformed fundamentally nationally based auto sectors into global networks of design, production and distribution across the global value chains coordinated by the major automotive Original Equipment Manufacturers (OEMs). As manufacturing activities tended to be shifted to low-labour cost locations in Asia, Africa and Latin America, high-end design, R&D, product development have stayed anchored mostly to high-cost and high knowledge-intensive home economy locations (perhaps with the except of some design and styling activities which are often located in major end markets around the world. However, very recently the weaknesses of and risks inherent in such global value chains (GVCs) have been exposed, triggering attempts to rethink their nature and also raising possibilities to reshore some manufacturing activities to home countries. A combination of a more competitive exchange rate (despite the very recent appreciation of sterling), increased transport costs, rising wages in key areas of China, and a greater awareness of supply chain resilience have all contributed to a perceived change in some business fundamentals. The potential for some supply chain relocalisation also links in with the servitisation of manufacturing including the auto sector and shift to a hybrid model where manufacturing and services are increasingly intertwined. However, there are limits as to how far this can go and these raise some important questions and issues over the possible role for industrial policy.

Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.

Modelling in the evaluation of a manufacturing strategy

This thesis describes research that has developed the principles of a modelling tool for the analytical evaluation of a manufacturing strategy. The appropriate process of manufacturing strategy formulation is based on mental synthesis with formal planning processes supporting this role. Inherent to such processes is a stage where the effects of alternative strategies on the performance of a manufacturing system must be evaluated so that a choice of preferred strategy can be made. Invariably this evaluation is carried out by practitioners applying mechanisms of judgement, bargaining and analysis. Ibis thesis makes a significant and original contribution to the provision of analytical support for practitioners in this role. The research programme commences by defining the requirements of analytical strategy evaluation from the perspective of practitioners. A broad taxonomy of models has been used to identify a set of potentially suitable techniques for the strategy evaluation task. Then, where possible, unsuitable modelling techniques have been identified on the basis of evidence in the literature and discarded from this set. The remaining modelling techniques have been critically appraised by testing representative contemporary modelling tools in an industrially based experimentation programme. The results show that individual modelling techniques exhibit various limitations in the strategy evaluation role, though some combinations do appear to provide the necessary functionality. On the basis of this comprehensive and in-depth knowledge a modelling tool ' has been specifically designed for this task. Further experimental testing has then been conducted to verify the principles of this modelling tool. Ibis research has bridged the fields of manufacturing strategy formulation and manufacturing systems modelling and makes two contributions to knowledge. Firstly, a comprehensive and in-depth platform of knowledge has been established about modelling techniques in manufacturing strategy evaluation. Secondly, the principles of a tool that supports this role have been formed and verified.

Modelling in the evaluation of a manufacturing strategy

This thesis describes research that has developed the principles of a modelling tool for the analytical evaluation of a manufacturing strategy. The appropriate process of manufacturing strategy formulation is based on mental synthesis with formal planning processes supporting this role. Inherent to such processes is a stage where the effects of alternative strategies on the performance of a manufacturing system must be evaluated so that a choice of preferred strategy can be made. Invariably this evaluation is carried out by practitioners applying mechanisms of judgement, bargaining and analysis. Ibis thesis makes a significant and original contribution to the provision of analytical support for practitioners in this role. The research programme commences by defining the requirements of analytical strategy evaluation from the perspective of practitioners. A broad taxonomy of models has been used to identify a set of potentially suitable techniques for the strategy evaluation task. Then, where possible, unsuitable modelling techniques have been identified on the basis of evidence in the literature and discarded from this set. The remaining modelling techniques have been critically appraised by testing representative contemporary modelling tools in an industrially based experimentation programme. The results show that individual modelling techniques exhibit various limitations in the strategy evaluation role, though some combinations do appear to provide the necessary functionality. On the basis of this comprehensive and in-depth knowledge a modelling tool ' has been specifically designed for this task. Further experimental testing has then been conducted to verify the principles of this modelling tool. Ibis research has bridged the fields of manufacturing strategy formulation and manufacturing systems modelling and makes two contributions to knowledge. Firstly, a comprehensive and in-depth platform of knowledge has been established about modelling techniques in manufacturing strategy evaluation. Secondly, the principles of a tool that supports this role have been formed and verified.

The effects of material variations in high value manufacturing industries

It has never been easy for manufacturing companies to understand their confidence level in terms of how accurate and to what degree of flexibility parts can be made. This brings uncertainty in finding the most suitable manufacturing method as well as in controlling their product and process verification systems. The aim of this research is to develop a system for capturing the company’s knowledge and expertise and then reflect it into an MRP (Manufacturing Resource Planning) system. A key activity here is measuring manufacturing and machining capabilities to a reasonable confidence level. For this purpose an in-line control measurement system is introduced to the company. Using SPC (Statistical Process Control) not only helps to predict the trend in manufacturing of parts but also minimises the human error in measurement. Gauge R&R (Repeatability and Reproducibility) study identifies problems in measurement systems. Measurement is like any other process in terms of variability. Reducing this variation via an automated machine probing system helps to avoid defects in future products.Developments in aerospace, nuclear, oil and gas industries demand materials with high performance and high temperature resistance under corrosive and oxidising environments. Superalloys were developed in the latter half of the 20th century as high strength materials for such purposes. For the same characteristics superalloys are considered as difficult-to-cut alloys when it comes to formation and machining. Furthermore due to the sensitivity of superalloy applications, in many cases they should be manufactured with tight tolerances. In addition superalloys, specifically Nickel based, have unique features such as low thermal conductivity due to having a high amount of Nickel in their material composition. This causes a high surface temperature on the work-piece at the machining stage which leads to deformation in the final product.Like every process, the material variations have a significant impact on machining quality. The main cause of variations can originate from chemical composition and mechanical hardness. The non-uniform distribution of metal elements is a major source of variation in metallurgical structures. Different heat treatment standards are designed for processing the material to the desired hardness levels based on application. In order to take corrective actions, a study on the material aspects of superalloys has been conducted. In this study samples from different batches of material have been analysed. This involved material preparation for microscopy analysis, and the effect of chemical compositions on hardness (before and after heat treatment). Some of the results are discussed and presented in this paper.

Decision-making in product quality based on failure knowledge

Decision-making in product quality is an indispensable stage in product development, in order to reduce product development risk. Based on the identification of the deficiencies of quality function deployment (QFD) and failure modes and effects analysis (FMEA), a novel decision-making method is presented that draws upon a knowledge network of failure scenarios. An ontological expression of failure scenarios is presented together with a framework of failure knowledge network (FKN). According to the roles of quality characteristics (QCs) in failure processing, QCs are set into three categories namely perceptible QCs, restrictive QCs, and controllable QCs, which present the monitor targets, control targets and improvement targets respectively for quality management. A mathematical model and algorithms based on the analytic network process (ANP) is introduced for calculating the priority of QCs with respect to different development scenarios. A case study is provided according to the proposed decision-making procedure based on FKN. This methodology is applied in the propeller design process to solve the problem of prioritising QCs. This paper provides a practical approach for decision-making in product quality. Copyright © 2011 Inderscience Enterprises Ltd.

Do manufacturing firms need informality in ERP post-implementation? A study of Chinese manufacturing sites

Purpose - Enterprise resource planning (ERP) systems are limited due to their operation around a fixed design production process and a fixed lead time to production plan and purchasing plan. The purpose of this paper is to define the concept of informality and to describe the notion of a system combining informality and ERP systems, based on empirical research from four manufacturing case studies. Design/methodology/approach - The case studies present a range of applications of ERP and are analysed in terms of the three characteristics of informality, namely, organisation structure, communication method and leadership approach. Findings - The findings suggest that systems consisting of informality in combination with ERP systems can elicit knowledge fromfrontlineworkers leading to timely improvements in the system. This is achieved by allowing users to modify work procedures or production orders, and to support collaborative working among all employees. However it was found that informality is not required for manufacturers with a relatively stable environment who can deal with uncertainty with a proactive strategy. Research limitations/implications - This study was carried out in China, with four companies as unit of analysis. Future work can help to extend this study across countries. Originality/value - The use of Four dimensions of informality that relate to manufacturers implementing ERP are defined as "technology in practice", "user flexibility", "trusted human networks" and "positive reaction to uncertainty". This is a new construct not applied before to ERP implementations.

CO2 emissions reduction of Chinese light manufacturing industries:a novel RAM-based global Malmquist-Luenberger productivity index

Climate change has become one of the most challenging issues facing the world. Chinese government has realized the importance of energy conservation and prevention of the climate changes for sustainable development of China's economy and set targets for CO2 emissions reduction in China. In China industry contributes 84.2% of the total CO2 emissions, especially manufacturing industries. Data envelopment analysis (DEA) and Malmquist productivity (MP) index are the widely used mathematical techniques to address the relative efficiency and productivity of a group of homogenous decision making units, e.g. industries or countries. However, in many real applications, especially those related to energy efficiency, there are often undesirable outputs, e.g. the pollutions, waste and CO2 emissions, which are produced inevitably with desirable outputs in the production. This paper introduces a novel Malmquist-Luenberger productivity (MLP) index based on directional distance function (DDF) to address the issue of productivity evolution of DMUs in the presence of undesirable outputs. The new RAM (Range-adjusted measure)-based global MLP index has been applied to evaluate CO2 emissions reduction in Chinese light manufacturing industries. Recommendations for policy makers have been discussed.

Decision support based on genomics:integration of data-and knowledge-driven reasoning

The breadth and depth of available clinico-genomic information, present an enormous opportunity for improving our ability to study disease mechanisms and meet the individualised medicine needs. A difficulty occurs when the results are to be transferred 'from bench to bedside'. Diversity of methods is one of the causes, but the most critical one relates to our inability to share and jointly exploit data and tools. This paper presents a perspective on current state-of-the-art in the analysis of clinico-genomic data and its relevance to medical decision support. It is an attempt to investigate the issues related to data and knowledge integration. Copyright © 2010 Inderscience Enterprises Ltd.

Knowledge sources, innovative activity and the performance of UK new technology based firms

We present an innovation value chain analysis for a representative sample of new technology based firms (NTBFs) in the UK. This involves determining which factors lead to the usage of different knowledge sources and the relationships that exist between those sources of knowledge; the effect that each knowledge source has on innovative activity; and how innovation outputs affect the performance of NTBFs. We find that internal (i.e. R&D) and external knowledge sources are complementary for NTBFs, and that supply chain linkages have both a direct and indirect effect on innovation. NTBFs' skill resources matter throughout the innovation value chain, being positively associated with external knowledge linkages and innovation success, and also having a direct effect on growth independent of the effect on innovation. ©2010 IEEE.

Knowledge sources, innovative activity and the performance of UK new technology based firms

We present an innovation value chain analysis for a representative sample of new technology based firms (NTBFs) in the UK. This involves determining which factors lead to the usage of different knowledge sources and the relationships that exist between those sources of knowledge; the effect that each knowledge source has on innovative activity; and how innovation outputs affect the performance of NTBFs. We find that internal (i.e. R&D) and external knowledge sources are complementary for NTBFs, and that supply chain linkages have both a direct and indirect effect on innovation. NTBFs' skill resources matter throughout the innovation value chain, being positively associated with external knowledge linkages and innovation success, and also having a direct effect on growth independent of the effect on innovation. ©2010 IEEE.