Foreign direct investment, transactions linkages, and the performance of the domestic sector

Using data from the UK Census of Production, including foreign ownership data, and information from UK industry input-output tables, this paper examines whether the intensity of transactions linkages between foreign and domestic firms affects productivity growth in domestic manufacturing industries. The implications of the findings for policies promoting linkages between multinational and domestic firms in the UK economy are outlined.

Developing a decomposable measure of profit efficiency using DEA

In for-profit organizations efficiency measurement with reference to the potential for profit augmentation is particularly important as is its decomposition into technical, and allocative components. Different profit efficiency approaches can be found in the literature to measure and decompose overall profit efficiency. In this paper, we highlight some problems within existing approaches and propose a new measure of profit efficiency based on a geometric mean of input/output adjustments needed for maximizing profits. Overall profit efficiency is calculated through this efficiency measure and is decomposed into its technical and allocative components. Technical efficiency is calculated based on a non-oriented geometric distance function (GDF) that is able to incorporate all the sources of inefficiency, while allocative efficiency is retrieved residually. We also define a measure of profitability efficiency which complements profit efficiency in that it makes it possible to retrieve the scale efficiency of a unit as a component of its profitability efficiency. In addition, the measure of profitability efficiency allows for a dual profitability interpretation of the GDF measure of technical efficiency. The concepts introduced in the paper are illustrated using a numerical example.

A circular Malmquist-type index for measuring productivity

This paper develops two new indices for measuring productivity in multi-input multi-output situations. One index enables the measure of productivity change of a unit over time while the second index makes it possible to compare two units on productivity at the same or different points in time. Productivity in a single input single output context is defined as the ratio of output to input. In multi-input multi-output contexts this ratio is not defined. Instead, one of the methods traditionally used is the Malmquist Index of productivity change over time. This is computed by reference to the distances of the input-output bundles of a production unit at two different points in time from the efficient boundaries corresponding to those two points in time. The indices developed in this paper depart form the use of two different reference boundaries and instead they use a single reference boundary which in a sense is the most efficient boundary observed over two or more successive time periods. We discuss the assumptions which make possible the definition of such a single reference boundary and proceed to develop the two Malmquist-type indices for measuring productivity. One key advantage of using a single reference boundary is that the resulting index values are circular. That is it is possible to use the index values of successive time periods to derive an index value of productivity change over a time period of any length covered by successive index values or vice versa. Further, the use of a single reference boundary makes it possible to construct an index for comparing the productivities of two units either at the same or at two different points in time. This was not possible with the traditional Malmquist Index. We decompose both new indices into components which isolate production unit from industry or comparator unit effects. The components themselves like the indices developed are also circular. The components of the indices drill down to reveal more clearly the performance of each unit over time relative either to itself or to other units. The indices developed and their components are aimed at managers of production units to enable them to diagnose the performance of their units with a view to guiding them to improved performance.

Expert systems for fault tree synthesis

Hazard and operability (HAZOP) studies on chemical process plants are very time consuming, and often tedious, tasks. The requirement for HAZOP studies is that a team of experts systematically analyse every conceivable process deviation, identifying possible causes and any hazards that may result. The systematic nature of the task, and the fact that some team members may be unoccupied for much of the time, can lead to tedium, which in turn may lead to serious errors or omissions. An aid to HAZOP are fault trees, which present the system failure logic graphically such that the study team can readily assimilate their findings. Fault trees are also useful to the identification of design weaknesses, and may additionally be used to estimate the likelihood of hazardous events occurring. The one drawback of fault trees is that they are difficult to generate by hand. This is because of the sheer size and complexity of modern process plants. The work in this thesis proposed a computer-based method to aid the development of fault trees for chemical process plants. The aim is to produce concise, structured fault trees that are easy for analysts to understand. Standard plant input-output equation models for major process units are modified such that they include ancillary units and pipework. This results in a reduction in the nodes required to represent a plant. Control loops and protective systems are modelled as operators which act on process variables. This modelling maintains the functionality of loops, making fault tree generation easier and improving the structure of the fault trees produced. A method, called event ordering, is proposed which allows the magnitude of deviations of controlled or measured variables to be defined in terms of the control loops and protective systems with which they are associated.

Exploiting uncertainty in nonlinear stochastic control problem

This work introduces a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. Convergence of the output error for the proposed control method is verified by using a Lyapunov function. Several simulation examples are provided to demonstrate the efficiency of the developed control method. The manner in which such a method is extended to nonlinear multi-variable systems with different delays between the input-output pairs is considered and demonstrated through simulation examples.

Managing performance of organisational units with PIMsoft:a new DEA software

DEA literature continues apace but software has lagged behind. This session uses suitably selected data to present newly developed software which includes many of the most recent DEA models. The software enables the user to address a variety of issues not frequently found in existing DEA software such as: -Assessments under a variety of possible assumptions of returns to scale including NIRS and NDRS; -Scale elasticity computations; -Numerous Input/Output variables and truly unlimited number of assessment units (DMUs) -Panel data analysis -Analysis of categorical data (multiple categories) -Malmquist Index and its decompositions -Computations of Supper efficiency -Automated removal of super-efficient outliers under user-specified criteria; -Graphical presentation of results -Integrated statistical tests

An investigation into the effects of processing instruction on the acquisition of English relative clauses by Syrian learners

The purpose of this research was to investigate the effects of Processing Instruction (VanPatten, 1996, 2007), as an input-based model for teaching second language grammar, on Syrian learners’ processing abilities. The present research investigated the effects of Processing Instruction on the acquisition of English relative clauses by Syrian learners in the form of a quasi-experimental design. Three separate groups were involved in the research (Processing Instruction, Traditional Instruction and a Control Group). For assessment, a pre-test, a direct post-test and a delayed post-test were used as main tools for eliciting data. A questionnaire was also distributed to participants in the Processing Instruction group to give them the opportunity to give feedback in relation to the treatment they received in comparison with the Traditional Instruction they are used to. Four hypotheses were formulated on the possible effectivity of Processing Instruction on Syrian learners’ linguistic system. It was hypothesised that Processing Instruction would improve learners’ processing abilities leading to an improvement in learners’ linguistic system. This was expected to lead to a better performance when it comes to the comprehension and production of English relative clauses. The main source of data was analysed statistically using the ANOVA test. Cohen’s d calculations were also used to support the ANOVA test. Cohen’s d showed the magnitude of effects of the three treatments. Results of the analysis showed that both Processing Instruction and Traditional Instruction groups had improved after treatment. However, the Processing Instruction Group significantly outperformed the other two groups in the comprehension of relative clauses. The analysis concluded that Processing Instruction is a useful tool for instructing relative clauses to Syrian learners. This was enhanced by participants’ responses to the questionnaire as they were in favour of Processing Instruction, rather than Traditional Instruction. This research has theoretical and pedagogical implications. Theoretically, the study showed support for the Input hypothesis. That is, it was shown that Processing Instruction had a positive effect on input processing as it affected learners’ linguistic system. This was reflected in learners’ performance where learners were able to produce a structure which they had not been asked to produce. Pedagogically, the present research showed that Processing Instruction is a useful tool for teaching English grammar in the context where the experiment was carried out, as it had a large effect on learners’ performance.

Application of calorimetric method for loss measurement of a SynRM drive system

Synchronous reluctance motors (SynRMs) are gaining in popularity in industrial drives due to their permanent magnet-free, competitive performance, and robust features. This paper studies the power losses in a 90-kW converter-fed SynRM drive by a calorimetric method in comparison of the traditional input-output method. After the converter and the motor were measured simultaneously in separate chambers, the converter was installed inside the large-size chamber next to the motor and the total drive system losses were obtained using one chamber. The uncertainty of both measurement methods is analyzed and discussed.

Classifying flexible measures in data envelopment analysis:a slack-based measure

Data envelopment analysis (DEA) has gained a wide range of applications in measuring comparative efficiency of decision making units (DMUs) with multiple incommensurate inputs and outputs. The standard DEA method requires that the status of all input and output variables be known exactly. However, in many real applications, the status of some measures is not clearly known as inputs or outputs. These measures are referred to as flexible measures. This paper proposes a flexible slacks-based measure (FSBM) of efficiency in which each flexible measure can play input role for some DMUs and output role for others to maximize the relative efficiency of the DMU under evaluation. Further, we will show that when an operational unit is efficient in a specific flexible measure, this measure can play both input and output roles for this unit. In this case, the optimal input/output designation for flexible measure is one that optimizes the efficiency of the artificial average unit. An application in assessing UK higher education institutions used to show the applicability of the proposed approach. © 2013 Elsevier Ltd. All rights reserved.

Typical performance of regular low-density parity-check codes over general symmetric channels

Typical performance of low-density parity-check (LDPC) codes over a general binary-input output-symmetric memoryless channel is investigated using methods of statistical mechanics. Relationship between the free energy in statistical-mechanics approach and the mutual information used in the information-theory literature is established within a general framework; Gallager and MacKay-Neal codes are studied as specific examples of LDPC codes. It is shown that basic properties of these codes known for particular channels, including their potential to saturate Shannon's bound, hold for general symmetric channels. The binary-input additive-white-Gaussian-noise channel and the binary-input Laplace channel are considered as specific channel models.

Multimodal 'Eyes-Free' interaction techniques for mobile devices

Mobile and wearable computers present input/output prob-lems due to limited screen space and interaction techniques. When mobile, users typically focus their visual attention on navigating their environment - making visually demanding interface designs hard to operate. This paper presents two multimodal interaction techniques designed to overcome these problems and allow truly mobile, 'eyes-free' device use. The first is a 3D audio radial pie menu that uses head gestures for selecting items. An evaluation of a range of different audio designs showed that egocentric sounds re-duced task completion time, perceived annoyance, and al-lowed users to walk closer to their preferred walking speed. The second is a sonically enhanced 2D gesture recognition system for use on a belt-mounted PDA. An evaluation of the system with and without audio feedback showed users' ges-tures were more accurate when dynamically guided by au-dio-feedback. These novel interaction techniques demon-strate effective alternatives to visual-centric interface de-signs on mobile devices.

Using the max-sum algorithm for supply chain emergence in dynamic multiunit environments

Supply chain formation (SCF) is the process of determining the set of participants and exchange relationships within a network with the goal of setting up a supply chain that meets some predefined social objective. Many proposed solutions for the SCF problem rely on centralized computation, which presents a single point of failure and can also lead to problems with scalability. Decentralized techniques that aid supply chain emergence offer a more robust and scalable approach by allowing participants to deliberate between themselves about the structure of the optimal supply chain. Current decentralized supply chain emergence mechanisms are only able to deal with simplistic scenarios in which goods are produced and traded in single units only and without taking into account production capacities or input-output ratios other than 1:1. In this paper, we demonstrate the performance of a graphical inference technique, max-sum loopy belief propagation (LBP), in a complex multiunit unit supply chain emergence scenario which models additional constraints such as production capacities and input-to-output ratios. We also provide results demonstrating the performance of LBP in dynamic environments, where the properties and composition of participants are altered as the algorithm is running. Our results suggest that max-sum LBP produces consistently strong solutions on a variety of network structures in a multiunit problem scenario, and that performance tends not to be affected by on-the-fly changes to the properties or composition of participants.

Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses

We show theoretically and experimentally a mechanismbehind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Some Properties of Mittag-Leffler Functions and Matrix-Variate Analogues: A Statistical Perspective

Mathematical Subject Classification 2010:26A33, 33E99, 15A52, 62E15.

Applying consumer responsibility principle in evaluating environmental load of carbon emissions

There is a need for a proper indicator in order to assess the environmental impact of international trade, therefore using the carbon footprint as an indicator can be relevant and useful. The aim of this study is to show from a methodological perspective how the carbon footprint, combined with input- output models can be used for analysing the impacts of international trade on the sustainable use of national resources in a country. The use of the input-output approach has the essential advantage of being able to track the transformation of goods through the economy. The study examines the environmental impact of consumption related to international trade, using the consumer responsibility principle. In this study the use of the carbon footprint and input-output methodology is shown on the example of the Hungarian consumption and the impact of international trade. Moving from a production- based approach in climate policy to a consumption-perspective principle and allocation, would also help to increase the efficiency of emission reduction targets and the evaluation of the ecological impacts of international trade.