Middlesex University

About: Middlesex University is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Context (language use) & Population. The organization has 4203 authors who have published 10964 publications receiving 247580 citations.

A rose by any other name may smell as sweet but “group discussion” is not another name for a “focus group” nor should it be

Abstract: Purpose – This paper seeks to highlight the current confusion in the terminology for group research, identify the geographic, historical and scientific sources of this confusion and suggest a reduction in the number of terms used to two, thereby offering a definition on which researchers from different cultural backgrounds and scientific traditions may be able to agree.Design/methodology/approach – A review of the academic and practitioner literature on qualitative group research in academic, social and market research indicates that various terms for groups are used interchangeably and are often assumed to have the same meaning. These terms include; Focus Group, Group Discussion, Group Interview, Group, Focus Group Interview, Focus Group Discussion, Qualitative Group Discussion and Nominal Group Interview.Practical implications – The contribution of this paper is that it offers a resolution of this issue and so allows researchers from across geographic borders, different scientific traditions and from bo...

Social Reporting by Companies Listed on the Alternative Investment Market.

Abstract: While the existing literature focuses on the disclosure of social information mainly by large companies, this paper concentrates on the disclosure of social information by small- and medium-sized companies (SME) listed on the Alternative Investment Market (AIM) in the U.K. The paper investigates the prevalent view that SMEs are unlikely to report social information due to their financial constraints and the perception that they have very little social conduct on which to report. Our overall evidence illustrates that, contrary to this view, SMEs report social information regardless of their financial constraints, most likely in the same manner as large companies do, because they realise the significance of social reporting in establishing and retaining their corporate reputation.

Human Osteoblasts' Proliferative Responses to Strain and 17β-Estradiol Are Mediated by the Estrogen Receptor and the Receptor for Insulin-Like Growth Factor I

Abstract: The mechanism by which mechanical strain and estrogen stimulate bone cell proliferation was investigated using monolayer cultures of human osteoblastic TE85 cells and female human primary (first-passage) osteoblasts (fHOBs). Both cell types showed small but statistically significant dose-dependent increases in [3H]thymidine incorporation in response to 17beta-estradiol and to a single 10-minute period of uniaxial cyclic strain (1 Hz). In both cell types, the peak response to 17beta-estradiol occurred at 10(-8) - 10(-7) M and the peak response to strain occurred at 3500 microstrain ((mu)epsilon). Both strain-related and 17beta-estradiol-related increases in [3H]thymidine incorporation were abolished by the estrogen receptor (ER) modulator ICI 182,780 (10-8 M). Tamoxifen (10(-9) - 10(-8) M) increased [3H]thymidine incorporation in both cell types but had no effect on their response to strain. In TE85 cells, tamoxifen reduced the increase in [3H]thymidine incorporation associated with 17beta-estradiol to that of tamoxifen alone but had no such effect in fHOBs. In TE85 cells, strain increased medium concentrations of insulin-like growth factor (IGF) II but not IGF-I, whereas 17beta-estradiol increased medium concentrations of IGF-I but not IGF-II. Neutralizing monoclonal antibody (MNAb) to IGF-I (3 microg/ml) blocked the effects of 17beta-estradiol and exogenous truncated IGF-I (tIGF-I; 50 ng/ml) but not those of strain or tIGF-II (50 ng/ml). Neutralizing antibody to IGF-II (3 microg/ml) blocked the effects of strain and tIGF-II but not those of 17beta-estradiol or tIGF-I. MAb aIR-3 (100 ng/ml) to the IGF-I receptor blocked the effects on [3H]thymidine incorporation of strain, tIGF-II, 17beta-estradiol, and tIGF-I. HOBs and TE85 cells, act similarly to rat primary osteoblasts and ROS 17/2.8 cells in their dose-related proliferative responses to strain and 17beta-estradiol, both of which can be blocked by the ER modulator ICI 182,780. In TE85 cells (as in rat primaries and ROS 17/2.8 cells), the response to 17beta-estradiol is mediated by IGF-I, and the response to strain is mediated by IGF-II. Human cells differ from rat cells in that tamoxifen does not block their response to strain and reduces the response to 17beta-estradiol in TE85s but not primaries. In both human cell types (unlike rat cells) the effects of strain and IGF-II as well as estradiol and IGF-I can be blocked at the IGF-I receptor.

Requirements engineering: A good practice

Abstract: (2000). Requirements engineering: A good practice. European Journal of Information Systems: Vol. 9, No. 2, pp. 124-125.

Quantum Neural Network-Based EEG Filtering for a Brain–Computer Interface

Abstract: A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.