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.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce 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. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Interaction dynamics in small networks of nonlinear elements

We study a small circuit of coupled nonlinear elements to investigate general features of signal transmission through networks. The small circuit itself is perceived as building block for larger networks. Individual dynamics and coupling are motivated by neuronal systems: We consider two types of dynamical modes for an individual element, regular spiking and chattering and each individual element can receive excitatory and/or inhibitory inputs and is subjected to different feedback types (excitatory and inhibitory; forward and recurrent). Both, deterministic and stochastic simulations are carried out to study the input-output relationships of these networks. Major results for regular spiking elements include frequency locking, spike rate amplification for strong synaptic coupling, and inhibition-induced spike rate control which can be interpreted as a output frequency rectification. For chattering elements, spike rate amplification for low frequencies and silencing for large frequencies is characteristic

Computer simulation of radio-frequency methane/hydrogen plasmas and their interaction with GaAs Surfaces

The following thesis describes the computer modelling of radio frequency capacitively coupled methane/hydrogen plasmas and the consequences for the reactive ion etching of (100) GaAs surfaces. In addition a range of etching experiments was undertaken over a matrix of pressure, power and methane concentration. The resulting surfaces were investigated using X-ray photoelectron spectroscopy and the results were discussed in terms of physical and chemical models of particle/surface interactions in addition to the predictions for energies, angles and relative fluxes to the substrate of the various plasma species. The model consisted of a Monte Carlo code which followed electrons and ions through the plasma and sheath potentials whilst taking account of collisions with background neutral gas molecules. The ionisation profile output from the electron module was used as input for the ionic module. Momentum scattering interactions of ions with gas molecules were investigated via different models and compared against results given by quantum mechanical code. The interactions were treated as central potential scattering events and the resulting neutral cascades were followed. The resulting predictions for ion energies at the cathode compared well to experimental ion energy distributions and this verified the particular form of the electrical potentials used and their applicability in the particular geometry plasma cell used in the etching experiments. The final code was used to investigate the effect of external plasma parameters on the mass distribution, energy and angles of all species impingent on the electrodes. Comparisons of electron energies in the plasma also agreed favourably with measurements made using a Langmuir electric probe. The surface analysis showed the surfaces all to be depleted in arsenic due to its preferential removal and the resultant Ga:As ratio in the surface was found to be directly linked to the etch rate. The etch rate was determined by the methane flux which was predicted by the code.

Emulation of dynamic computer models with multivariate output

This preliminary report describes work carried out as part of work package 1.2 of the MUCM research project. The report is split in two parts: the ?rst part (Sections 1 and 2) summarises the state of the art in emulation of computer models, while the second presents some initial work on the emulation of dynamic models. In the ?rst part, we describe the basics of emulation, introduce the notation and put together the key results for the emulation of models with single and multiple outputs, with or without the use of mean function. In the second part, we present preliminary results on the chaotic Lorenz 63 model. We look at emulation of a single time step, and repeated application of the emulator for sequential predic- tion. After some design considerations, the emulator is compared with the exact simulator on a number of runs to assess its performance. Several general issues related to emulating dynamic models are raised and discussed. Current work on the larger Lorenz 96 model (40 variables) is presented in the context of dimension reduction, with results to be provided in a follow-up report. The notation used in this report are summarised in appendix.

A study of thermodynamic characteristics and predictive computer modelling of an air to water heat pump system

A study on heat pump thermodynamic characteristics has been made in the laboratory on a specially designed and instrumented air to water heat pump system. The design, using refrigerant R12, was based on the requirement to produce domestic hot water at a temperature of about 50 °C and was assembled in the laboratory. All the experimental data were fed to a microcomputer and stored on disk automatically from appropriate transducers via amplifier and 16 channel analogue to digital converters. The measurements taken were R12 pressures and temperatures, water and R12 mass flow rates, air speed, fan and compressor input powers, water and air inlet and outlet temperatures, wet and dry bulb temperatures. The time interval between the observations could be varied. The results showed, as expected, that the COP was higher at higher air inlet temperatures and at lower hot water output temperatures. The optimum air speed was found to be at a speed when the fan input power was about 4% of the condenser heat output. It was also found that the hot water can be produced at a temperature higher than the appropriate R12 condensing temperature corresponding to condensing pressure. This was achieved by condenser design to take advantage of discharge superheat and by further heating the water using heat recovery from the compressor. Of the input power to the compressor, typically about 85% was transferred to the refrigerant, 50 % by the compression work and 35% due to the heating of the refrigerant by the cylinder wall, and the remaining 15% (of the input power) was rejected to the cooling medium. The evaporator effectiveness was found to be about 75% and sensitive to the air speed. Using the data collected, a steady state computer model was developed. For given input conditions s air inlet temperature, air speed, the degree of suction superheat , water inlet and outlet temperatures; the model is capable of predicting the refrigerant cycle, compressor efficiency, evaporator effectiveness, condenser water flow rate and system Cop.

A framework for assessing business-incubation input, process and output performance:proceedings of the RENT XXVII Conference, 20-22 November 2013, Vilnius, Lithuania

With business incubators deemed as a potent infrastructural element for entrepreneurship development, business incubation management practice and performance have received widespread attention. However, despite this surge of interest, scholars have questioned the extent to which business incubation delivers added value. Thus, there is a growing awareness among researchers, practitioners and policy makers of the need for more rigorous evaluation of the business incubation output performance. Aligned to this is an increasing demand for benchmarking business incubation input/process performance and highlighting best practice. This paper offers a business incubation assessment framework, which considers input/process and output performance domains with relevant indicators. This tool adds value on different levels. It has been developed in collaboration with practitioners and industry experts and therefore it would be relevant and useful to business incubation managers. Once a large enough database of completed questionnaires has been populated on an online platform managed by a coordinating mechanism, such as a business incubation membership association, business incubator managers can reflect on their practices by using this assessment framework to learn their relative position vis-à-vis their peers against each domain. This will enable them to align with best practice in this field. Beyond implications for business incubation management practice, this performance assessment framework would also be useful to researchers and policy makers concerned with business incubation management practice and impact. Future large-scale research could test for construct validity and reliability. Also, discriminant analysis could help link input and process indicators with output measures.

Optimal design for correlated processes with input-dependent noise

Optimal design for parameter estimation in Gaussian process regression models with input-dependent noise is examined. The motivation stems from the area of computer experiments, where computationally demanding simulators are approximated using Gaussian process emulators to act as statistical surrogates. In the case of stochastic simulators, which produce a random output for a given set of model inputs, repeated evaluations are useful, supporting the use of replicate observations in the experimental design. The findings are also applicable to the wider context of experimental design for Gaussian process regression and kriging. Designs are proposed with the aim of minimising the variance of the Gaussian process parameter estimates. A heteroscedastic Gaussian process model is presented which allows for an experimental design technique based on an extension of Fisher information to heteroscedastic models. It is empirically shown that the error of the approximation of the parameter variance by the inverse of the Fisher information is reduced as the number of replicated points is increased. Through a series of simulation experiments on both synthetic data and a systems biology stochastic simulator, optimal designs with replicate observations are shown to outperform space-filling designs both with and without replicate observations. Guidance is provided on best practice for optimal experimental design for stochastic response models. © 2013 Elsevier Inc. All rights reserved.

An efficient screening method for computer experiments

Computer simulators of real-world processes are often computationally expensive and require many inputs. The problem of the computational expense can be handled using emulation technology; however, highly multidimensional input spaces may require more simulator runs to train and validate the emulator. We aim to reduce the dimensionality of the problem by screening the simulators inputs for nonlinear effects on the output rather than distinguishing between negligible and active effects. Our proposed method is built upon the elementary effects (EE) method for screening and uses a threshold value to separate the inputs with linear and nonlinear effects. The technique is simple to implement and acts in a sequential way to keep the number of simulator runs down to a minimum, while identifying the inputs that have nonlinear effects. The algorithm is applied on a set of simulated examples and a rabies disease simulator where we observe run savings ranging between 28% and 63% compared with the batch EE method. Supplementary materials for this article are available online.