• D-shaped photonic crystal fiber based surface plasmon resonance sensor D-Şeklinde fotonik kristal fiber tabanli yüzey plazmon rezonans sensörü

      Yaşli, Ahmet; Ademgil, Huseyin; Haxha, Shyqyri; Lefke Avrupa Üniversitesi; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2018-07-09)
      In this study, we have proposed photonic crystal fiber based surface plasmon resonance sensor, where hexagonally arranged circular air holes surrounded the core of photonic crystal fiber. Gold has been placed on top surface as a metallic layer, while all the structure covered with analyte channel. Full Vectorial Finite Elimination Method has been used for analysing effective refractive index and confinement losses for proposed sensor. When refractive index of analyte channel changes, spectral interrogation and amplitude methods have been used for calculating the sensitivities and resolutions of offered sensor.
    • Daily-running exercise may induce incomplete energy intake compensation: a 7-day crossover trial

      Hough, John; Esh, Christopher John; Mackie, Paul Ian; Stensel, David J.; Zakrzewski-Fruer, Julia K.; University of Bedfordshire (Canadian Science Publishing, 2019-12-13)
      Understanding daily-exercise effects on energy balance is important. This study examined the effects of seven days of imposed exercise (EX) and no exercise (N-EX) on free-living energy intake (EI) and physical activity energy expenditure (PAEE) in nine men. Free-living EI was higher in EX compared with N-EX. Total and vigorous PAEE were higher, with PAEE in sedentary activities lower, during EX compared with N-EX. Daily-running (for 7 days) induced EI compensation of ~60% exercise-induced EE.
    • Dance Digital Education APP

      Ashley, Tamara; Vom Kothen, K.; NESTA (NESTA, 2015-12-31)
      Report on the creation and testing phases of the dancedigital education app that was funded by Arts Council and NESTA
    • Dance, diaspora and the role of the archives: a dialogic reflection upon the Black Dance Arcives Project (UK)

      Carr, Jane; Baddoo, Deborah; University of Bedfordshire; State of Emergency Productions (Edinburgh University Press Ltd, 2020-05-01)
      The Black Dance Archives project collected materials that record the activities of black British artists who created and performed dance predominantly in the later years of the twentieth century. Through the form of a dialogue we bring the perspective of the dance producer who led the project together with a more academic interest in the potential of the materials collected to contribute to dance research. Our shared reflections reveal how a focus on archiving the work of dance artists of diasporic heritage emphasizes that dance, as a form of intangible cultural heritage, is particularly vulnerable to becoming lost to future generations. This leads to reflections upon the role of dance archives within the context of post-colonial Britain that brings to the fore some of the complexities of the archival process and the significance of how this project resulted in materials being dispersed across different institutions.
    • Dancing brains: dance as a key motivator for success in mathematics

      Pugh, Kathryn; University of Bedfordshire (2018-08-01)
      A growing body of research supports the notion that dance enhances cognitive function as well as providing an enjoyable means of learning, as evidenced by recent news items and experiments such as that of Professor Michael Duncan of Coventry University as shown in the recent BBC documentary ‘The Truth About Getting Fit’ (BBC 50:43-57:00) where dance was declared “unusually beneficial” (Michael Mosley, 50:47) for the brain. Lynnette Overby, Beth Post and Diane Newman espouse the “bodies-on” nature of interdisciplinary dance stating that dance is: Uniquely suited to support conceptual learning because the dance vocabulary is expressed in terms of the body, space, time, and force – concepts also fundamental to understanding the universe (2005, Preface xi). Other scholars such as Anne Watson, Anne Green-Gilbert (BrainDance) and Eric Jensen, and on-going programmes such as Learning Through the Arts and Project Zero support the notion that dance is beneficial for the mind and useful as a means of interdisciplinary learning. In addition, neuroscience research shows that 85% of learners are predominantly kinesthetic learners (Jensen, 2010) and the President’s Committee on the Arts and the Humanities agrees that there are: Documented significant links between arts integration models and academic and social outcomes for students, efficacy for teachers, and school-wide improvements in culture and climate (PCAH 2011 in Wheeler and Bogard 2013, p.4). In my action research project carried out in Primary Schools in Canada, using a quasi-experimental approach and pre-/post data, it was clear that the increase in motivation to learn, along with increase in attainment was evident with students also enjoying both subjects more than they anticipated or experienced prior. In this paper, therefore, I will explore the notion of an equal interdisciplinary partnership of dance and mathematics that increases motivation and enhances learning in both subjects.
    • The dark web: cyber-security intelligence gathering opportunities, risks and rewards

      Epiphaniou, Gregory; French, Tim; Maple, Carsten; University of Bedfordshire (University of Zagreb, 2014-12-31)
      We offer a partial articulation of the threats and opportunities posed by the so-called Dark Web (DW). We go on to propose a novel DW attack detection and prediction model. Signalling aspects are considered wherein the DW is seen to comprise a low cost signaling environment. This holds inherent dangers as well as rewards for investigators as well as those with criminal intent. Suspected DW perpetrators typically act entirely in their own self-interest (e.g. illicit financial gain, terrorism, propagation of extremist views, extreme forms of racism, pornography, and politics; so-called 'radicalisation'). DWinvestigators therefore need to be suitably risk aware such that the construction of a credible legally admissible, robust evidence trail does not expose investigators to undue operational or legal risk.
    • Data mining techniques in health informatics: a case study from breast cancer research

      Lu, Jing; Hales, Alan; Rew, David; Keech, Malcolm; Fröhlingsdorf, Christian; Mills-Mullett, Alex; Wette, Christian; Southampton Solent University; University Hospital Southampton; University of Bedfordshire (Springer Verlag, 2015-08-11)
      This paper presents a case study of using data mining techniques in the analysis of diagnosis and treatment events related to Breast Cancer disease. Data from over 16,000 patients has been pre-processed and several data mining techniques have been implemented by using Weka (Waikato Environment for Knowledge Analysis). In particular, Generalized Sequential Patterns mining has been used to discover frequent patterns from disease event sequence profiles based on groups of living and deceased patients. Furthermore, five models have been evaluated in Classification with the objective to classify the patients based on selected attributes. This research showcases the data mining process and techniques to transform large amounts of patient data into useful information and potentially valuable patterns to help understand cancer outcomes.
    • Data mining, management and visualization in large scientific corpuses

      Wei, Hui; Wu, Shaopeng; Zhao, Youbing; Deng, Zhikun; Ersotelos, Nikolaos; Parvinzamir, Farzad; Liu, Baoquan; Liu, Enjie; Dong, Feng; University of Bedfordshire (Springer Verlag, 2016-12-31)
      Organizing scientific papers helps efficiently derive meaningful insights of the published scientific resources, enables researchers grasp rapid technological change and hence assists new scientific discovery. In this paper, we experiment text mining and data management of scientific publications for collecting and presenting useful information to support research. For efficient data management and fast information retrieval, four data storages are employed: a semantic repository, an index and search repository, a document repository and a graph repository, taking full advantage of their features and strength. The results show that the combination of these four repositories can effectively store and index the publication data with reliability and efficiency and hence supply meaningful information to support scientific research.
    • Data of the impact of aligning business, IT, and marketing strategies on firm performance

      Al-Surmi, Abdulrahman Mohamed; Cao, Guangming; Duan, Yanqing; University of Bedfordshire (Elsevier, 2019-10-15)
      The data presented in this article are related to the research article entitled “The Impact of Aligning Business, IT, and Marketing Strategies on Firm Performance” [https://www.sciencedirect.com/science/article/pii/S0019850118304449]. In order to succeed in today's competitive business environment, a firm should have a clear business strategy that is supported by other organizational strategies. While prior studies argue that strategic alignment enhances firm performance, either strategic alignment including multiple factors or strategic orientation of firms has received little attention. This study, drawing on contingency theory and configuration theory, investigates the performance impact of triadic strategic alignment among business, IT, and marketing strategies while simultaneously considers strategic orientation of firms. A research model is tested through SEM and MANOVA using data collected in a questionnaire survey of 242 Yemen managers. The findings indicate that (1) triadic strategic alignment has a positive impact on firm performance and (2) there is an ideal triadic strategic alignment for prospectors and defenders. This research contributes to strategic alignment literature and managers' understanding of how to align business, IT and marketing strategies to improve firm performance.
    • Dealing with comparability problem of cross-cultural data

      Ertubey, Candan; Russell, R.J.H.; University of Bedfordshire (PSYCHOLOGY PRESS, 1996-01-01)
    • The debate on flexibility of environmental regulations, innovation capabilities and financial performance - a novel use of DEA

      Ramanathan, Ramakrishnan; Ramanathan, Usha; Bentley, Yongmei; University of Bedfordshire; Nottingham Trent University (Elsevier Ltd, 2017-03-27)
      Operational research models have been employed to understand development issues associated with environmental sustainability. This article describes a novel application of Data Envelopment Analysis (DEA) to help extend a specific debate in the literature on Porter’s hypothesis in environmental policy. The debate deals with the impact of flexibility of regulations on the relationship between innovation capabilities on financial performance in organisations. Using the resource based view of a firm, we hypothesise that relationship between innovation capabilities and financial performance in firms depends on how flexible or inflexible environmental regulations are. We apply DEA to capture the flexibility of environmental regulations. Our results indicate that innovation capabilities significantly influence financial performance of firms if firms feel that the environmental regulations they face are flexible and offer more freedom in meeting the requirements of regulations. On the other hand, corporations that feel that they face more inflexible regulations are not so effective in improving their financial performance with their innovation capabilities.
    • A decade on from the summer riots

      Bateman, Tim (2021-07-27)
    • Decentralising health services: a global perspective

      Regmi, Krishna (Springer Publication, 2014-01-01)
    • Decrypting cultural nuances: using drama techniques from the theatre of the oppressed to strengthen cross cultural communication in social work students

      Burroughs, Lana; Muzuva, Bethel; University of Bedfordshire; Waterlily & Co. (Taylor and Francis, 2019-03-25)
      Despite widening participation in social work education in the UK, social work students from black and minority ethnic (BME) backgrounds can find that they have less positive experiences on social work courses than their counterparts. This can happen when courses do not equip students to navigate the subtle rules of communication with service users that are premised on dominant UK values. As a consequence BME students can be assessed as having poor interpersonal skills and poor skills in engaging service users. However, the issue is often more one of cultural differences and high expectations of cultural integration than one of incompetence.
    • Deep learning for biometric face recognition: experimental study on benchmark data sets

      Selitskaya, Natalya; Sielicki, S.; Jakaite, Livija; Schetinin, Vitaly; Evans, F.; Conrad, Marc; Sant, Paul (Springer International Publishing, 2020-06-09)
      There are still problems in applications of Machine Learning for face recognition. Such factors as lighting conditions, head rotations, emotions, and view angles affect the recognition accuracy. A large number of recognition subjects requires complex class boundaries. Deep Neural Networks have provided efficient solutions, although their implementations require massive computations for evaluation and minimisation of error functions. Gradient algorithms provide iterative minimisation of the error function. A maximal performance is achieved if parameters of gradient algorithms and neural network structures are properly set. The use of pairwise neural network structures often improves the performance because such structures require a small set of optimisation parameters. The experiments have been conducted on some face biometric benchmark data sets, and the main findings are presented in the form of a tutorial.
    • Deep learning for early detection of pathological changes in X-ray bone microstructures: case of osteoarthritis

      Jakaite, Livija; Schetinin, Vitaly; Hladůvka, Jiří; Minaev, Sergey; Ambia, Aziz; Krzanowski, Wojtek; ; University of Bedfordshire; TU Wien; Stavropol State Medical University; et al. (Nature, 2021-01-27)
      Texture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirable consequences in medical image analysis. In this paper we explore the ability of machine learning (ML) methods to design a radiology test of Osteoarthritis (OA) at early stage when the number of patients’ cases is small. In our experiments we use high-resolution X-ray images of knees in patients which were identified with Kellgren–Lawrence scores progressing from 1. The existing ML methods have provided a limited diagnostic accuracy, whilst the proposed Group Method of Data Handling strategy of Deep Learning has significantly extended the diagnostic test. The comparative experiments demonstrate that the proposed framework using the Zernike-based texture features has significantly improved the diagnostic accuracy on average by 11%. This allows us to conclude that the designed model for early diagnostic of OA will provide more accurate radiology tests, although new study is required when a large number of patients’ cases will be available.
    • Deep neural-network prediction for study of informational efficiency

      Sulaiman, Rejwan Bin; Schetinin, Vitaly; University of Bedfordshire (Springer, 2021-08-03)
      In this paper, we attempt to verify a hypothesis of informational efficiency of financial markets, known as “random walk” introduced by Fama. Such hypotheses could be considered in relation to financial crises. In our study the hypothesis is tested on data taken from Warsaw Stock Exchange in 2007–2009 years. The hypothesis is tested by predictive modelling based on Machine Learning (ML). We compare conventional ML techniques and the proposed “deep” neural-network structures grown by Group Method of Data Handling (GMDH). In our experiments a GMDH-type neural-network model has outperformed the conventional ML techniques, which is important for achieving the reliable results of predictive modelling and testing the hypothesis. GMDH-type modelling does not require the knowledge of network structure, as a desired network of near-optimal connectivity is learnt from the data. The experimental results compared in terms of prediction error show that the GMDH-type prediction model has a significantly smaller error than the conventional autoregressive and neural-network models.
    • The defining constituents of adult attachment and their assessment

      Sochos, Antigonos (Springer, 2013-06-04)
      Reviewing the major issues regarding the definition of adult attachment and the nature of the attachment representations, this paper points out that attachment theory approaches intimate interpersonal processes using three fundamental dichotomies: self versus other, autonomy versus relatedness, and dependent versus depended-on positions. When these three dichotomies are intersected, eight components emerge to define the attachment representation: the autonomy and relatedness requests and autonomy and relatedness provisions of self and other. Moreover, as the main methodologies assessing adult attachment are also reviewed, it is argued that these have not yet provided an exhaustive empirical assessment of these eight components individually. It is suggested that such an approach to assessment may yield interesting findings. © 2013 Springer Science+Business Media New York.
    • Defining integrated reading-into-writing constructs: evidence at the B2 C1 interface

      Chan, Sathena Hiu Chong (Cambridge University Press, 2018-06-01)