Accelerator-induced transients in Accelerator Driven Subcritical Reactors

Achieving higher particles energies and beam powers have long been the main focus of research in accelerator technology. Since Accelerator Driven Subcritical Reactors (ADSRs) have become the subject of increasing interest, accelerator reliability and modes of operation have become important matters that require further research and development in order to accommodate the engineering and economic needs of ADSRs. This paper focuses on neutronic and thermo-mechanical analyses of accelerator-induced transients in an ADSR. Such transients fall into three main categories: beam interruptions (trips), pulsed-beam operation, and beam overpower. The concept of a multiple-target ADSR is shown to increase system reliability and to mitigate the negative effects of beam interruptions, such as thermal cyclic fatigue in the fuel cladding and the huge financial cost of total power loss. This work also demonstrates the effectiveness of the temperature-to-reactivity feedback mechanisms in ADSRs. A comparison of shutdown mechanisms using control rods and beam cut-off highlights the intrinsic safety features of ADSRs. It is evident that the presence of control rods is crucial in an industrial-scale ADSR. This paper also proposes a method to monitor core reactivity online using the repetitive pattern of beam current fluctuations in a pulsed-beam operation mode. Results were produced using PTS-ADS, a computer code developed specifically to study the dynamic neutronic and thermal responses to beam transients in subcritical reactor systems. © 2012 Elsevier B.V.

Beyond niche thinking: Market selection in science-based ventures

Matching a new technology to an appropriate market is a major challenge for new technology-based firms (NTBF). Such firms are often advised to target niche-markets where the firms and their technologies can establish themselves relatively free of incumbent competition. However, technologies are diverse in nature and do not benefit from identical strategies. In contrast to many Information and Communication Technology (ICT) innovations which build on an established knowledge base for fairly specific applications, technologies based on emerging science are often generic and so have a number of markets and applications open to them, each carrying considerable technological and market uncertainty. Each of these potential markets is part of a complex and evolving ecosystem from which the venture may have to access significant complementary assets in order to create and sustain commercial value. Based on dataset and case study research on UK advanced material university spin-outs (USO), we find that, contrary to conventional wisdom, the more commercially successful ventures were targeting mainstream markets by working closely with large, established competitors during early development. While niche markets promise protection from incumbent firms, science-based innovations, such as new materials, often require the presence, and participation, of established companies in order to create value. © 2012 IEEE.

Market-pull and technology-push in manufacturing start-ups in emerging industries

Purpose - As traditional manufacturing, previously vital to the UK economy, is increasingly outsourced to lower-cost locations, policy makers seek leadership in emerging industries by encouraging innovative start-up firms to pursue competitive opportunities. Emerging industries can either be those where a technology exists but the corresponding downstream value chain is unclear, or a new technology may subvert the existing value chain to satisfy existing customer needs. Hence, this area shows evidence of both technology-push and market-pull forces. The purpose of this paper is to focus on market-pull and technology-push orientations in manufacturing ventures, specifically examining how and why this orientation shifts during the firm's formative years. Design/methodology/approach - A multiple case study approach of 25 UK start-ups in emerging industries is used to examine this seldom explored area. The authors offer two models of dynamic business-orientation in start-ups and explain the common reasons for shifts in orientation and why these two orientations do not generally co-exist during early firm development. Findings - Separate evolution paths were found for strategic orientation in manufacturing start-ups and separate reasons for them to shift in their early development. Technology-push start-ups often changed to a market-pull orientation because of new partners, new market information or shift in management priorities. In contrast, many of the start-ups beginning with a market-pull orientation shifted to a technology-push orientation because early market experiences necessitated a focus on improving processes in order to increase productivity or meet partner specifications, or meet a demand for complementary products. Originality/value - While a significant body of work exists regarding manufacturing strategy in established firms, little work has been found that investigates how manufacturing strategy emerges in start-up companies, particularly those in emerging industries. © Emerald Group Publishing Limited.

Enhanced integrated modelling approach to reconfiguring manufacturing enterprises

Dynamism and uncertainty are real challenges for present day manufacturing enterprises (MEs). Reasons include: an increasing demand for customisation, reduced time to market, shortened product life cycles and globalisation. MEs can reduce competitive pressure by becoming reconfigurable and change-capable. However, modern manufacturing philosophies, including agile and lean, must complement the application of reconfigurable manufacturing paradigms. Choosing and applying the best philosophies and techniques is very difficult as most MEs deploy complex and unique configurations of processes and resource systems, and seek economies of scope and scale in respect of changing and distinctive product flows. It follows that systematic methods of achieving model driven reconfiguration and interoperation of component based manufacturing systems are required to design, engineer and change future MEs. This thesis, titled Enhanced Integrated Modelling Approach to Reconfiguring Manufacturing Enterprises , introduces the development and prototyping a model-driven environment for the design, engineering, optimisation and control of the reconfiguration of MEs with an embedded capability to handle various types of change. The thesis describes a novel systematic approach, namely enhanced integrated modelling approach (EIMA), in which coherent sets of integrated models are created that facilitates the engineering of MEs especially their production planning and control (PPC) systems. The developed environment supports the engineering of common types of strategic, tactical and operational processes found in many MEs. The EIMA is centred on the ISO standardised CIMOSA process modelling approach. Early study led to the development of simulation models during which various CIMOSA shortcomings were observed, especially in its support for aspects of ME dynamism. A need was raised to structure and create semantically enriched models hence forming an enhanced integrated modelling environment. The thesis also presents three industrial case examples: (1) Ford Motor Company; (2) Bradgate Furniture Manufacturing Company; and (3) ACM Bearings Company. In order to understand the system prior to realisation of any PPC strategy, multiple process segments of any target organisation need to be modelled. Coherent multi-perspective case study models are presented that have facilitated process reengineering and associated resource system configuration. Such models have a capability to enable PPC decision making processes in support of the reconfiguration of MEs. During these case studies, capabilities of a number of software tools were exploited such as Arena®, Simul8®, Plant Simulation®, MS Visio®, and MS Excel®. Case study results demonstrated effectiveness of the concepts related to the EIMA. The research has resulted in new contributions to knowledge in terms of new understandings, concepts and methods in following ways: (1) a structured model driven integrated approach to the design, optimisation and control of future reconfiguration of MEs. The EIMA is an enriched and generic process modelling approach with capability to represent both static and dynamic aspects of an ME; and (2) example application cases showing benefits in terms of reduction in lead time, cost and resource load and in terms of improved responsiveness of processes and resource systems with a special focus on PPC; (3) identification and industrial application of a new key performance indicator (KPI) known as P3C the measuring and monitoring of which can aid in enhancing reconfigurability and responsiveness of MEs; and (4) an enriched modelling concept framework (E-MUNE) to capture requirements of static and dynamic aspects of MEs where the conceptual framework has the capability to be extended and modified according to the requirements. The thesis outlines key areas outlining a need for future research into integrated modelling approaches, interoperation and updating mechanisms of partial models in support of the reconfiguration of MEs.

Sound radiation from point-driven shell structures

A numerical study is presented showing the structural response and sound radiation from a range of thin shell structures excited by a point force: a baffled flat plate, a sphere, a family of spheroids and a family of closed circular cylinders. All the structures have the same material properties, thickness and total surface area so the asymptotic modal density is the same. Dramatic differences are shown in the total radiated sound power for the different shells. It was already known that the flat plate and the sphere behave very differently. These results show that the cylinders and, particularly, the spheroids show patterns that are not intermediate between the two but instead display new features: in certain frequency ranges the radiated sound power can be at least an order of magnitude greater than either the plate or the sphere. © 2013 Elsevier Ltd.

Rebuilding the limit order book: Sequential Bayesian inference on hidden states

The limit order book of an exchange represents an information store of market participants' future aims and for many traders the information held in this store is of interest. However, information loss occurs between orders being entered into the exchange and limit order book data being sent out. We present an online algorithm which carries out Bayesian inference to replace information lost at the level of the exchange server and apply our proof of concept algorithm to real historical data from some of the world's most liquid futures contracts as traded on CME GLOBEX, EUREX and NYSE Liffe exchanges. © 2013 © 2013 Taylor & Francis.

Fabrication of an integrated pixel with an organic light-emitting diode driven by copper phthalocyanine organic thin film transistors

An organic integrated pixel with organic light-emitting diodes (OLEDs) driven by organic thin film transistors (OTFTs) is fabricated by a greatly simplified processing. The OTFTs are based on copper phthalocyanine as the active medium and fabricated on indium-tin-oxide (ITO) glass with top-gate structure, thus an organic integrated pixel is easily made by integrating OLED with OTFT. The OTFTs show field-effect mobility of 0.4 cm(2) /Vs and on/off ratio of 10(3) order. The OLED is driven well and emits the brightness as large as 2100cd/m(2) at a current density of 14.6 mu A/cm(2) at -19.7 V gate voltage. This simple device structure is promising in the future large-area flexible OLED displays.

CIMS环境下单元的柔性计划生产方法

本文研究了ＣＩＭＳ环境下的生产计划问题．考虑了两种市场需求驱动生产方式，一种是在计划周期内各阶段市场需求由预先订货决定，另一种是市场需求不是由预先订货决定，而是一个随机变量．但可根据统计，确认它服从某种分布规律，对于这两种生产方式，本文可给出最优的生产计划。

A scenario-driven decision support system for serious crime investigation

Q. Shen, J. Keppens, C. Aitken, B. Schafer, and M. Lee. A scenario driven decision support system for serious crime investigation. Law, Probability and Risk, 5(2):87-117, 2006. Sponsorship: UK Engineering and Physical Sciences Research Council grant GR/S63267; partially supported by grant GR/S98603

The Retail Market for Bednets in Kenya: How Well Is It Working?

Background Achieving the goals set by Roll Back Malaria and the Government of Kenya for use of insecticide treated bednets (ITNs) will require that the private retail market for nets and insecticide treatments grow substantially. This paper applies some basic concepts of market structure and pricing to a set of recently-collected retail price data from Kenya in order to answer the question, “How well are Kenyan retail markets for ITNs working?” Methods Data on the availability and prices of ITNs at a wide range of retail outlets throughout Kenya were collected in January 2002, and vendors and manufacturers were interviewed regarding market structure. Findings Untreated nets are manufactured in Kenya by a number of companies and are widely available in large and medium-sized towns. Availability in smaller villages is limited. There is relatively little geographic price variation, and nets can be found at competitive prices in towns and cities. Marketing margins on prices appear to be within normal ranges. No finished nets are imported. Few pre-treated nets or net+treatment combinations are available, with the exception of the subsidized Supanet/Power Tab combination marketed by a donor-funded social marketing project. Conclusions Retail markets for untreated nets in Kenya are well established and appear to be competitive. Markets for treated nets and insecticide treatment kits are not well established. The role of subsidized ITN marketing projects should be monitored to ensure that these projects support, rather than hinder, the development of retail markets.

Exponential sensitivity of noise-driven switching in genetic networks

Cells are known to utilize biochemical noise to probabilistically switch between distinct gene expression states. We demonstrate that such noise-driven switching is dominated by tails of probability distributions and is therefore exponentially sensitive to changes in physiological parameters such as transcription and translation rates. However, provided mRNA lifetimes are short, switching can still be accurately simulated using protein-only models of gene expression. Exponential sensitivity limits the robustness of noise-driven switching, suggesting cells may use other mechanisms in order to switch reliably.

Market-oriented new product development of functional beverages

Strategic reviews of the Irish Food and Beverage Industry have consistently emphasised the need for food and beverage firms to improve their innovation and marketing capabilities, in order to maintain competitiveness in both domestic and overseas markets. In particular, the functional food and beverages market has been singled out as an extremely important emerging market, which Irish firms could benefit from through an increased technological and market orientation. Although health and wellness have been the most significant drivers of new product development (NPD) in recent years, failure rates for new functional foods and beverages have been reportedly high. In that context, researchers in the US, UK, Denmark and Ireland have reported a marked divergence between NPD practices within food and beverage firms and normative advice for successful product development. The high reported failure rates for new functional foods and beverages suggest a failure to manage customer knowledge effectively, as well as a lack of knowledge management between functional disciplines involved in the NPD process. This research explored the concept of managing customer knowledge at the early stages of the NPD process, and applied it to the development of a range of functional beverages, through the use of advanced concept optimisation research techniques, which provided for a more market-oriented approach to new food product development. A sequential exploratory research design strategy using mixed research methods was chosen for this study. First, the qualitative element of this research investigated customers’ choice motives for orange juice and soft drinks, and explored their attitudes and perceptions towards a range of new functional beverage concepts through a combination of 15 in-depth interviews and 3 focus groups. Second, the quantitative element of this research consisted of 3 conjoint-based questionnaires administered to 400 different customers in each study in order to model their purchase preferences for chilled nutrient-enriched and probiotic orange juices, and stimulant soft drinks. The in-depth interviews identified the key product design attributes that influenced customers’ choice motives for orange juice. The focus group discussions revealed that groups of customers were negative towards the addition of certain functional ingredients to natural foods and beverages. K-means cluster analysis was used to quantitatively identify segments of customers with similar preferences for chilled nutrient-enriched and probiotic orange juices, and stimulant soft drinks. Overall, advanced concept optimisation research methods facilitate the integration of the customer at the early stages of the NPD process, which promotes a multi-disciplinary approach to new food product design. This research illustrated how advanced concept optimisation research methods could contribute towards effective and efficient knowledge management in the new food product development process.

Systematic delay-driven power optimisation and power-driven delay optimisation of combinational circuits

With the proliferation of mobile wireless communication and embedded systems, the energy efficiency becomes a major design constraint. The dissipated energy is often referred as the product of power dissipation and the input-output delay. Most of electronic design automation techniques focus on optimising only one of these parameters either power or delay. Industry standard design flows integrate systematic methods of optimising either area or timing while for power consumption optimisation one often employs heuristics which are characteristic to a specific design. In this work we answer three questions in our quest to provide a systematic approach to joint power and delay Optimisation. The first question of our research is: How to build a design flow which incorporates academic and industry standard design flows for power optimisation? To address this question, we use a reference design flow provided by Synopsys and integrate in this flow academic tools and methodologies. The proposed design flow is used as a platform for analysing some novel algorithms and methodologies for optimisation in the context of digital circuits. The second question we answer is: Is possible to apply a systematic approach for power optimisation in the context of combinational digital circuits? The starting point is a selection of a suitable data structure which can easily incorporate information about delay, power, area and which then allows optimisation algorithms to be applied. In particular we address the implications of a systematic power optimisation methodologies and the potential degradation of other (often conflicting) parameters such as area or the delay of implementation. Finally, the third question which this thesis attempts to answer is: Is there a systematic approach for multi-objective optimisation of delay and power? A delay-driven power and power-driven delay optimisation is proposed in order to have balanced delay and power values. This implies that each power optimisation step is not only constrained by the decrease in power but also the increase in delay. Similarly, each delay optimisation step is not only governed with the decrease in delay but also the increase in power. The goal is to obtain multi-objective optimisation of digital circuits where the two conflicting objectives are power and delay. The logic synthesis and optimisation methodology is based on AND-Inverter Graphs (AIGs) which represent the functionality of the circuit. The switching activities and arrival times of circuit nodes are annotated onto an AND-Inverter Graph under the zero and a non-zero-delay model. We introduce then several reordering rules which are applied on the AIG nodes to minimise switching power or longest path delay of the circuit at the pre-technology mapping level. The academic Electronic Design Automation (EDA) tool ABC is used for the manipulation of AND-Inverter Graphs. We have implemented various combinatorial optimisation algorithms often used in Electronic Design Automation such as Simulated Annealing and Uniform Cost Search Algorithm. Simulated Annealing (SMA) is a probabilistic meta heuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. We used SMA to probabilistically decide between moving from one optimised solution to another such that the dynamic power is optimised under given delay constraints and the delay is optimised under given power constraints. A good approximation to the global optimum solution of energy constraint is obtained. Uniform Cost Search (UCS) is a tree search algorithm used for traversing or searching a weighted tree, tree structure, or graph. We have used Uniform Cost Search Algorithm to search within the AIG network, a specific AIG node order for the reordering rules application. After the reordering rules application, the AIG network is mapped to an AIG netlist using specific library cells. Our approach combines network re-structuring, AIG nodes reordering, dynamic power and longest path delay estimation and optimisation and finally technology mapping to an AIG netlist. A set of MCNC Benchmark circuits and large combinational circuits up to 100,000 gates have been used to validate our methodology. Comparisons for power and delay optimisation are made with the best synthesis scripts used in ABC. Reduction of 23% in power and 15% in delay with minimal overhead is achieved, compared to the best known ABC results. Also, our approach is also implemented on a number of processors with combinational and sequential components and significant savings are achieved.

First- and second-order phase transitions in the adlayer of biadipate on Au(111)

The adsorption of biadipate on Au(111) was studied by cyclic voltammetry and chronocoulometry. The biadipate adlayer undergoes a potential-driven phase transition. It is shown that the phase transition can be either of the first- or second-order depending on the biadipate concentration. At low surfactant concentrations, the first-order transition is characterised by a discontinuity in the charge density-potential curve and by the presence of very sharp peaks in the voltammetric response. At higher concentrations, these peaks are no longer observed but a discontinuity in the capacity curve is still noticeable, in agreement with a second-order transition. © the Owner Societies.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.