Manchester Metropolitan University

About: Manchester Metropolitan University is a education organization based out in Manchester, Manchester, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 5435 authors who have published 16202 publications receiving 442561 citations. The organization is also known as: Manchester Polytechnic & MMU.

Manual or electronic? The role of coding in qualitative data analysis

Abstract: Data analysis is the most difficult and most crucial aspect of qualitative research. Coding is one of the significant steps taken during analysis to organize and make sense of textual data. This paper examines the use of manual and electronic methods to code data in two rather different projects in which the data were collected mainly by in-depth interviewing. The author looks at both the methods in the light of her own experience and concludes that the choice will be dependent on the size of the project, the funds and time available, and the inclination and expertise of the researcher.

Validating the organizational climate measure: links to managerial practices, productivity and innovation

Abstract: This paper describes the development and validation of a multidimensional measure of organizational climate, the Organizational Climate Measure (OCM), based upon Quinn and Rohrbaugh's Competing Values model. A sample of 6869 employees across 55 manufacturing organizations completed the questionnaire. The 17 scales contained within the measure had acceptable levels of reliability and were factorially distinct. Concurrent validity was measured by correlating employees' ratings with managers' and interviewers' descriptions of managerial practices and organizational characteristics. Predictive validity was established using measures of productivity and innovation. The OCM also discriminated effectively between organizations, demonstrating good discriminant validity. The measure offers researchers a relatively comprehensive and flexible approach to the assessment of organizational members' experience and promises applied and theoretical benefits. Copyright © 2005 John Wiley & Sons, Ltd.

The adaptations to strength training : morphological and neurological contributions to increased strength.

Abstract: High-resistance strength training (HRST) is one of the most widely practiced forms of physical activity, which is used to enhance athletic performance, augment musculo-skeletal health and alter body aesthetics. Chronic exposure to this type of activity produces marked increases in muscular strength, which are attributed to a range of neurological and morphological adaptations. This review assesses the evidence for these adaptations, their interplay and contribution to enhanced strength and the methodologies employed. The primary morphological adaptations involve an increase in the cross-sectional area of the whole muscle and individual muscle fibres, which is due to an increase in myofibrillar size and number. Satellite cells are activated in the very early stages of training; their proliferation and later fusion with existing fibres appears to be intimately involved in the hypertrophy response. Other possible morphological adaptations include hyperplasia, changes in fibre type, muscle architecture, myofilament density and the structure of connective tissue and tendons. Indirect evidence for neurological adaptations, which encompasses learning and coordination, comes from the specificity of the training adaptation, transfer of unilateral training to the contralateral limb and imagined contractions. The apparent rise in whole-muscle specific tension has been primarily used as evidence for neurological adaptations; however, morphological factors (e.g. preferential hypertrophy of type 2 fibres, increased angle of fibre pennation, increase in radiological density) are also likely to contribute to this phenomenon. Changes in inter-muscular coordination appear critical. Adaptations in agonist muscle activation, as assessed by electromyography, tetanic stimulation and the twitch interpolation technique, suggest small, but significant increases. Enhanced firing frequency and spinal reflexes most likely explain this improvement, although there is contrary evidence suggesting no change in cortical or corticospinal excitability. The gains in strength with HRST are undoubtedly due to a wide combination of neurological and morphological factors. Whilst the neurological factors may make their greatest contribution during the early stages of a training programme, hypertrophic processes also commence at the onset of training.

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

Carbon Quantum Dots and Their Derivative 3D Porous Carbon Frameworks for Sodium-Ion Batteries with Ultralong Cycle Life.

Abstract: A new methodology for the synthesis of carbon quantum dots (CQDs) for large production is proposed. The as-obtained CQDs can be transformed into 3D porous carbon frameworks exhibiting superb sodium storage properties with ultralong cycle life and ultrahigh rate capability, comparable to state-of-the-art carbon anode materials for sodium-ion batteries.