Fuzzy assessment of performance of a decision making units using DEA:a non-radical approach

Data Envelopment Analysis (DEA) is recognized as a modern approach to the assessment of performance of a set of homogeneous Decision Making Units (DMUs) that use similar sources to produce similar outputs. While DEA commonly is used with precise data, recently several approaches are introduced for evaluating DMUs with uncertain data. In the existing approaches many information on uncertainties are lost. For example in the defuzzification, the a-level and fuzzy ranking approaches are not considered. In the tolerance approach the inequality or equality signs are fuzzified but the fuzzy coefficients (inputs and outputs) are not treated directly. The purpose of this paper is to develop a new model to evaluate DMUs under uncertainty using Fuzzy DEA and to include a-level to the model under fuzzy environment. An example is given to illustrate this method in details.

Analysis of a full-memory multidestination ARQ protocol over broadcast links

Based on an assumption that a steady state exists in the full-memory multidestination automatic repeat request (ARQ) scheme, we propose a novel analytical method called steady-state function method (SSFM), to evaluate the performance of the scheme with any size of receiver buffer. For a wide range of system parameters, SSFM has higher accuracy on throughput estimation as compared to the conventional analytical methods.

Analysis of functional materials by X-ray photoelectron spectroscopy

X-ray photoelectron spectroscopy (XPS) can play an important role in guiding the design of new materials, tailored to meet increasingly stringent constraints on performance devices, by providing insight into their surface compositions and the fundamental interactions between the surfaces and the environment. This chapter outlines the principles and application of XPS as a versatile, chemically specific analytical tool in determining the electronic structures and (usually surface) compositions of constituent elements within diverse functional materials. Advances in detector electronics have opened the way for development of photoelectron microscopes and instruments with XPS imaging capabilities. Advances in surface science instrumentation to enable time-resolved spectroscopic measurements offer exciting opportunities to quantitatively investigate the composition, structure and dynamics of working catalyst surfaces. Attempts to study the effects of material processing in realistic environments currently involves the use of high- or ambient-pressure XPS in which samples can be exposed to reactive environments.

Political leadership and the politics of performance:France, Syria and the chemical weapons crisis of 2013

This article draws upon developments in UK research on political rhetoric and political performance in order to examine the incident in 2013 when French President François Hollande committed French forces to a US-led punitive strike against Syria, after the use of chemical weapons in a Damascus suburb on 21 August. The US-led retaliation did not take place. This article analyses Hollande's declaration on 27 July and his TV appearance on 15 September. His rhetoric and style are best understood as generic to the nature of the presidential office of the Fifth Republic. The article concludes by appraising how analysis of the French case contributes to the developing literature on rhetoric, celebrity and performance.

Cramer-Rao bound analysis of location estimation in multi-hop wireless sensor networks

Location estimation is important for wireless sensor network (WSN) applications. In this paper we propose a Cramer-Rao Bound (CRB) based analytical approach for two centralized multi-hop localization algorithms to get insights into the error performance and its sensitivity to the distance measurement error, anchor node density and placement. The location estimation performance is compared with four distributed multi-hop localization algorithms by simulation to evaluate the efficiency of the proposed analytical approach. The numerical results demonstrate the complex tradeoff between the centralized and distributed localization algorithms on accuracy, complexity and communication overhead. Based on this analysis, an efficient and scalable performance evaluation tool can be designed for localization algorithms in large scale WSNs, where simulation-based evaluation approaches are impractical. © 2013 IEEE.

Effective forms of market orientation across the business cycle:a longitudinal analysis of business-to-business firms

Macroeconomic developments, such as the business cycle, have a remarkable influence on firms and their performance. In business-to-business (B-to-B) markets characterized by a strong emphasis on long-term customer relationships, market orientation (MO) provides a particularly important safeguard for firms against fluctuating market forces. Using panel data from an economic upturn and downturn, we examine the effectiveness of different forms of MO (i.e., customer orientation, competitor orientation, interfunctional coordination, and their combinations) on firm performance in B-to-B firms. Our findings suggest that the impact of MO increases especially during a downturn, with interfunctional coordination clearly boosting firm performance and, conversely, competitor orientation becoming even detrimental. The findings further indicate that both the role of MO and its most effective forms vary across industry sectors, MO having a particularly strong impact on performance among B-to-B service firms. The findings of our study provide guidelines for executives to better manage performance across the business cycle and tailor their investments in MO more effectively, according to the firm's specific industry sector.

A simulation analysis of spoke-terminals operating in LTL Hub-and-Spoke freight distribution systems

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT The research presented in this thesis is concerned with Discrete-Event Simulation (DES) modelling as a method to facilitate logistical policy development within the UK Less-than-Truckload (LTL) freight distribution sector which has been typified by “Pallet Networks” operating on a hub-and-spoke philosophy. Current literature relating to LTL hub-and-spoke and cross-dock freight distribution systems traditionally examines a variety of network and hub design configurations. Each is consistent with classical notions of creating process efficiency, improving productivity, reducing costs and generally creating economies of scale through notions of bulk optimisation. Whilst there is a growing abundance of papers discussing both the network design and hub operational components mentioned above, there is a shortcoming in the overall analysis when it comes to discussing the “spoke-terminal” of hub-and-spoke freight distribution systems and their capabilities for handling the diverse and discrete customer profiles of freight that multi-user LTL hub-and-spoke networks typically handle over the “last-mile” of the delivery, in particular, a mix of retail and non-retail customers. A simulation study is undertaken to investigate the impact on operational performance when the current combined spoke-terminal delivery tours are separated by ‘profile-type’ (i.e. retail or nonretail). The results indicate that a potential improvement in delivery performance can be made by separating retail and non-retail delivery runs at the spoke-terminal and that dedicated retail and non-retail delivery tours could be adopted in order to improve customer delivery requirements and adapt hub-deployed policies. The study also leverages key operator experiences to highlight the main practical implementation challenges when integrating the observed simulation results into the real-world. The study concludes that DES be harnessed as an enabling device to develop a ‘guide policy’. This policy needs to be flexible and should be applied in stages, taking into account the growing retail-exposure.

Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words' sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure. © 2014 Springer International Publishing.

Effectiveness of performance appraisal:an integrated framework

Based on a robust analysis of the existing literature on performance appraisal (PA), this paper makes a case for an integrated framework of effectiveness of performance appraisal (EPA). To achieve this, it draws on the expanded view of measurement criteria of EPA, i.e. purposefulness, fairness and accuracy, and identifies their relationships with ratee reactions. The analysis reveals that the expanded view of purposefulness includes more theoretical anchors for the purposes of PA and relates to various aspects of human resource functions, e.g. feedback and goal orientation. The expansion in the PA fairness criterion suggests certain newly established nomological networks, which were ignored in the past, e.g. the relationship between distributive fairness and organization-referenced outcomes. Further, refinements in PA accuracy reveal a more comprehensive categorization of rating biases. Coherence among measurement criteria has resulted in a ratee reactions-based integrated framework, which should be useful for both researchers and practitioners.

Analysis of Wiener-Hammerstein equalizer for downlink LTE system

We propose a Wiener-Hammerstein (W-H) channel estimation algorithm for Long-Term Evolution (LTE) systems. The LTE standard provides known data as pilot symbols and exploits them through coherent detection to improve system performance. These drivers are placed in a hybrid way to cover up both time and frequency domain. Our aim is to adapt the W-H equalizer (W-H/E) to LTE standard for compensation of both linear and nonlinear effects induced by power amplifiers and multipath channels. We evaluate the performance of the W-H/E for a Downlink LTE system in terms of BLER, EVM and Throughput versus SNR. Afterwards, we compare the results with a traditional Least-Mean Square (LMS) equalizer. It is shown that W-H/E can significantly reduce both linear and nonlinear distortions compared to LMS and improve LTE Downlink system performance.

Analysis of clogging in constructed wetlands using magnetic resonance

In this work we demonstrate the potential of permanent magnet based magnetic resonance sensors to monitor and assess the extent of pore clogging in water filtration systems. The performance of the sensor was tested on artificially clogged gravel substrates and on gravel bed samples from constructed wetlands used to treat wastewater. Data indicate that the spin lattice relaxation time is linearly related to the hydraulic conductivity in such systems. In addition, within biologically active filters we demonstrate the ability to determine the relative ratio of biomass to abiotic solids, a measurement which is not possible using alternative techniques. © 2011 The Royal Society of Chemistry.

The impact of ageing and gender on visual mental imagery processes:a study of performance on tasks from the Complete Visual Mental Imagery Battery (CVMIB)

In this study we aim to evaluate the impact of ageing and gender on different visual mental imagery processes. Two hundred and fifty-one participants (130 women and 121 men; age range = 18–77 years) were given an extensive neuropsychological battery including tasks probing the generation, maintenance, inspection, and transformation of visual mental images (Complete Visual Mental Imagery Battery, CVMIB). Our results show that all mental imagery processes with the exception of the maintenance are affected by ageing, suggesting that other deficits, such as working memory deficits, could account for this effect. However, the analysis of the transformation process, investigated in terms of mental rotation and mental folding skills, shows a steeper decline in mental rotation, suggesting that age could affect rigid transformations of objects and spare non-rigid transformations. Our study also adds to previous ones in showing gender differences favoring men across the lifespan in the transformation process, and, interestingly, it shows a steeper decline in men than in women in inspecting mental images, which could partially account for the mixed results about the effect of ageing on this specific process. We also discuss the possibility to introduce the CVMIB in clinical assessment in the context of theoretical models of mental imagery.

Simulation and experimental analysis of a brushless electrically excited synchronous machine with a hybrid rotor

Electrically excited synchronous machines with brushes and slip rings are popular but hardly used in inflammable and explosive environments. This paper proposes a new brushless electrically excited synchronous motor with a hybrid rotor. It eliminates the use of brushes and slip rings so as to improve the reliability and cost-effectiveness of the traction drive. The proposed motor is characterized with two sets of stator windings with two different pole numbers to provide excitation and drive torque independently. This paper introduces the structure and operating principle of the machine, followed by the analysis of the air-gap magnetic field using the finite-element method. The influence of the excitation winding's pole number on the coupling capability is studied and the operating characteristics of the machine are simulated. These are further examined by the experimental tests on a 16 kW prototype motor. The machine is proved to have good static and dynamic performance, which meets the stringent requirements for traction applications.

On stopwords, filtering and data sparsity for sentiment analysis of Twitter

Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier's feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space