Manchester Metropolitan University

This fully updated sixth edition of the international bestseller Research Methods in Education covers the whole range of methods currently employed by educational research at all stages. It is divided into five main parts: the context of educational research; planning educational research; styles of educational research; strategies for data collection and researching; and data analysis. The book also contains references to a comprehensive dedicated website of accompanying materials. The sixth edition includes new material on: complexity theory, ethics, sampling and sensitive educational research experimental research, questionnaire design and administration with practical guidance qualitative and quantitative data analysis, with practical examples internet based research. Research Methods in Education is essential reading for the professional researcher and continues to be the standard text for students and lecturers in educational research. To access the dedicated website of accompanying materials, please visit: www.routledge.com/textbooks/9780415368780.

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.

Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.

Abstract The concept of an agent has become important in both artificial intelligence (AT) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents; researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

We present the greenhouse gas concentrations for the Representative Concentration Pathways (RCPs) and their extensions beyond 2100, the Extended Concentration Pathways (ECPs). These projections include all major anthropogenic greenhouse gases and are a result of a multi-year effort to produce new scenarios for climate change research. We combine a suite of atmospheric concentration observations and emissions estimates for greenhouse gases (GHGs) through the historical period (1750–2005) with harmonized emissions projected by four different Integrated Assessment Models for 2005–2100. As concentrations are somewhat dependent on the future climate itself (due to climate feedbacks in the carbon and other gas cycles), we emulate median response characteristics of models assessed in the IPCC Fourth Assessment Report using the reduced-complexity carbon cycle climate model MAGICC6. Projected ‘best-estimate’ global-mean surface temperature increases (using inter alia a climate sensitivity of 3°C) range from 1.5°C by 2100 for the lowest of the four RCPs, called both RCP3-PD and RCP2.6, to 4.5°C for the highest one, RCP8.5, relative to pre-industrial levels. Beyond 2100, we present the ECPs that are simple extensions of the RCPs, based on the assumption of either smoothly stabilizing concentrations or constant emissions: For example, the lower RCP2.6 pathway represents a strong mitigation scenario and is extended by assuming constant emissions after 2100 (including net negative CO2 emissions), leading to CO2 concentrations returning to 360 ppm by 2300. We also present the GHG concentrations for one supplementary extension, which illustrates the stringent emissions implications of attempting to go back to ECP4.5 concentration levels by 2250 after emissions during the 21st century followed the higher RCP6 scenario. Corresponding radiative forcing values are presented for the RCP and ECPs.

Fungi play major roles in ecosystem processes, but the determinants of fungal diversity and biogeographic patterns remain poorly understood. Using DNA metabarcoding data from hundreds of globally distributed soil samples, we demonstrate that fungal richness is decoupled from plant diversity. The plant-to-fungus richness ratio declines exponentially toward the poles. Climatic factors, followed by edaphic and spatial variables, constitute the best predictors of fungal richness and community composition at the global scale. Fungi show similar latitudinal diversity gradients to other organisms, with several notable exceptions. These findings advance our understanding of global fungal diversity patterns and permit integration of fungi into a general macroecological framework.

C-reactive protein (CRP) is an acute inflammatory protein that increases up to 1,000-fold at sites of infection or inflammation. CRP is produced as a homopentameric protein, termed native CRP (nCRP), which can irreversibly dissociate at sites of inflammation and infection into five separate monomers, termed monomeric CRP (mCRP). CRP is synthesized primarily in liver hepatocytes but also by smooth muscle cells, macrophages, endothelial cells, lymphocytes, and adipocytes. Evidence suggests that estrogen in the form of hormone replacement therapy influences CRP levels in the elderly. Having been traditionally utilized as a marker of infection and cardiovascular events, there is now growing evidence that CRP plays important roles in inflammatory processes and host responses to infection including the complement pathway, apoptosis, phagocytosis, nitric oxide (NO) release, and the production of cytokines, particularly interleukin-6 and tumor necrosis factor-α. Unlike more recent publications, the findings of early work on CRP can seem somewhat unclear and at times conflicting since it was often not specified which particular CRP isoform was measured or utilized in experiments and whether responses attributed to nCRP were in fact possibly due to dissociation into mCRP or lipopolysaccharide contamination. In addition, since antibodies for mCRP are not commercially available, few laboratories are able to conduct studies investigating the mCRP isoform. Despite these issues and the fact that most CRP research to date has focused on vascular disorders, there is mounting evidence that CRP isoforms have distinct biological properties, with nCRP often exhibiting more anti-inflammatory activities compared to mCRP. The nCRP isoform activates the classical complement pathway, induces phagocytosis, and promotes apoptosis. On the other hand, mCRP promotes the chemotaxis and recruitment of circulating leukocytes to areas of inflammation and can delay apoptosis. The nCRP and mCRP isoforms work in opposing directions to inhibit and induce NO production, respectively. In terms of pro-inflammatory cytokine production, mCRP increases interleukin-8 and monocyte chemoattractant protein-1 production, whereas nCRP has no detectable effect on their levels. Further studies are needed to expand on these emerging findings and to fully characterize the differential roles that each CRP isoform plays at sites of local inflammation and infection.

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.

Abstract. We present and discuss a new dataset of gridded emissions covering the historical period (1850–2000) in decadal increments at a horizontal resolution of 0.5° in latitude and longitude. The primary purpose of this inventory is to provide consistent gridded emissions of reactive gases and aerosols for use in chemistry model simulations needed by climate models for the Climate Model Intercomparison Program #5 (CMIP5) in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment report (AR5). Our best estimate for the year 2000 inventory represents a combination of existing regional and global inventories to capture the best information available at this point; 40 regions and 12 sectors are used to combine the various sources. The historical reconstruction of each emitted compound, for each region and sector, is then forced to agree with our 2000 estimate, ensuring continuity between past and 2000 emissions. Simulations from two chemistry-climate models are used to test the ability of the emission dataset described here to capture long-term changes in atmospheric ozone, carbon monoxide and aerosol distributions. The simulated long-term change in the Northern mid-latitudes surface and mid-troposphere ozone is not quite as rapid as observed. However, stations outside this latitude band show much better agreement in both present-day and long-term trend. The model simulations indicate that the concentration of carbon monoxide is underestimated at the Mace Head station; however, the long-term trend over the limited observational period seems to be reasonably well captured. The simulated sulfate and black carbon deposition over Greenland is in very good agreement with the ice-core observations spanning the simulation period. Finally, aerosol optical depth and additional aerosol diagnostics are shown to be in good agreement with previously published estimates and observations.

The use of the internet and social media have changed consumer behavior and the ways in which companies conduct their business. Social and digital marketing offers significant opportunities to organizations through lower costs, improved brand awareness and increased sales. However, significant challenges exist from negative electronic word-of-mouth as well as intrusive and irritating online brand presence. This article brings together the collective insight from several leading experts on issues relating to digital and social media marketing. The experts’ perspectives offer a detailed narrative on key aspects of this important topic as well as perspectives on more specific issues including artificial intelligence, augmented reality marketing, digital content management, mobile marketing and advertising, B2B marketing, electronic word of mouth and ethical issues therein. This research offers a significant and timely contribution to both researchers and practitioners in the form of challenges and opportunities where we highlight the limitations within the current research, outline the research gaps and develop the questions and propositions that can help advance knowledge within the domain of digital and social marketing.

Udgivelsesdato: April 2010

Thematic analysis is widely used in qualitative psychology research, and in this article, we present a particular style of thematic analysis known as Template Analysis. We outline the technique and consider its epistemological position, then describe three case studies of research projects which employed Template Analysis to illustrate the diverse ways it can be used. Our first case study illustrates how the technique was employed in data analysis undertaken by a team of researchers in a large-scale qualitative research project. Our second example demonstrates how a qualitative study that set out to build on mainstream theory made use of the a priori themes (themes determined in advance of coding) permitted in Template Analysis. Our final case study shows how Template Analysis can be used from an interpretative phenomenological stance. We highlight the distinctive features of this style of thematic analysis, discuss the kind of research where it may be particularly appropriate, and consider possible limitations of the technique. We conclude that Template Analysis is a flexible form of thematic analysis with real utility in qualitative psychology research.

Purpose This paper aims to undertake a content analysis of extant definitions of “innovation” as a basis for proposing an integrative definition of organizational “innovation”. Design/methodology/approach A literature review was used to generate a representative pool of definitions of organizational innovation, including definitions from the different disciplinary literatures of economics, innovation and entrepreneurship, business and management, and technology, science and engineering. A content analysis of these definitions was conducted in order to surface the key attributes mentioned in the definitions, and to profile the descriptors used in relation to each attribute. Findings The key attributes in the paper present in definitions were identified as: nature of innovation; type of innovation; stages of innovation, social context; means of innovation; and aim of innovation. These attributes are defined, descriptors assigned to them, and both a diagrammatic definition and a textual definition of organizational innovation are proposed. Originality/value As a concept that is owned and discussed by many business disciplines, “innovation” has many different definitions that align with the dominant paradigm of the respective disciplines. Building on these diverse definitions, this paper proposes a general and integrative definition of organizational “innovation” that encompasses the different perspectives on, and aspects of, innovation, and captures its essence.

Sarcopenia is a loss of muscle mass and function in the elderly that reduces mobility, diminishes quality of life, and can lead to fall-related injuries, which require costly hospitalization and extended rehabilitation. This review focuses on the aging-related structural changes and mechanisms at cellular and subcellular levels underlying changes in the individual motor unit: specifically, the perikaryon of the α-motoneuron, its neuromuscular junction(s), and the muscle fibers that it innervates. Loss of muscle mass with aging, which is largely due to the progressive loss of motoneurons, is associated with reduced muscle fiber number and size. Muscle function progressively declines because motoneuron loss is not adequately compensated by reinnervation of muscle fibers by the remaining motoneurons. At the intracellular level, key factors are qualitative changes in posttranslational modifications of muscle proteins and the loss of coordinated control between contractile, mitochondrial, and sarcoplasmic reticulum protein expression. Quantitative and qualitative changes in skeletal muscle during the process of aging also have been implicated in the pathogenesis of acquired and hereditary neuromuscular disorders. In experimental models, specific intervention strategies have shown encouraging results on limiting deterioration of motor unit structure and function under conditions of impaired innervation. Translated to the clinic, if these or similar interventions, by saving muscle and improving mobility, could help alleviate sarcopenia in the elderly, there would be both great humanitarian benefits and large cost savings for health care systems.

strongly over the past decades in terms of activity, with revenue passenger kilometers increasing from 109 to 8269 billion km yr -1 , and in terms of climate change impacts, with CO 2 emissions increasing by a factor of 6.8 to 1034 Tg CO 2 yr -1 . Over the period 2013-2018, the growth rates in both terms show a marked increase. Here, we present a new comprehensive and quantitative approach for evaluating aviation climate forcing terms. Both radiative forcing (RF) and effective radiative forcing (ERF) terms and their sums are calculated for the years 2000-2018. Contrail cirrus, consisting of linear contrails and the cirrus cloudiness arising from them, yields the largest positive net (warming) ERF term followed by CO 2 and NO x emissions. The formation and emission of sulfate aerosol yields a negative (cooling) term. The mean contrail cirrus ERF/RF ratio of 0.42 indicates that contrail cirrus is less effective in surface warming than other terms. For 2018 the net aviation ERF is +100.9 milliwatts (mW) m -2 (5-95% likelihood range of (55, 145)) with major contributions from contrail cirrus (57.4 mW m -2 ), CO 2 (34.3 mW m -2 ), and NO x (17.5 mW m -2 ). Non-CO 2 terms sum to yield a net positive (warming) ERF that accounts for more than half (66%) of the aviation net ERF in 2018. Using normalization to aviation fuel use, the contribution of global aviation in 2011 was calculated to be 3.5 (4.0, 3.4) % of the net anthropogenic ERF of 2290 (1130, 3330) mW m -2 . Uncertainty distributions (5%, 95%) show that non-CO 2 forcing terms contribute about 8 times more than CO 2 to the uncertainty in the aviation net ERF in 2018. The best estimates of the ERFs from aviation aerosol-cloud interactions for soot and sulfate remain undetermined. C 2 -warming-equivalent emissions based on global warming potentials (GWP* method) indicate that aviation emissions are currently warming the climate at approximately three times the rate of that associated with aviation CO 2 emissions alone. CO 2 and NO x aviation emissions and cloud effects remain a continued focus of anthropogenic climate change research and policy discussions.

Assessing Biodiversity Declines Understanding human impact on biodiversity depends on sound quantitative projection. Pereira et al. (p. 1496 , published online 26 October) review quantitative scenarios that have been developed for four main areas of concern: species extinctions, species abundances and community structure, habitat loss and degradation, and shifts in the distribution of species and biomes. Declines in biodiversity are projected for the whole of the 21st century in all scenarios, but with a wide range of variation. Hoffmann et al. (p. 1503 , published online 26 October) draw on the results of five decades' worth of data collection, managed by the International Union for Conservation of Nature Species Survival Commission. A comprehensive synthesis of the conservation status of the world's vertebrates, based on an analysis of 25,780 species (approximately half of total vertebrate diversity), is presented: Approximately 20% of all vertebrate species are at risk of extinction in the wild, and 11% of threatened birds and 17% of threatened mammals have moved closer to extinction over time. Despite these trends, overall declines would have been significantly worse in the absence of conservation actions.

Using a teaching model framework, we systematically review empirical evidence on the impact of entrepreneurship education (EE) in higher education on a range of entrepreneurial outcomes, analyzing 159 published articles from 2004 to 2016. The teaching model framework allows us for the first time to start rigorously examining relationships between pedagogical methods and specific outcomes. Reconfirming past reviews and meta-analyses, we find that EE impact research still predominantly focuses on short-term and subjective outcome measures and tends to severely underdescribe the actual pedagogies being tested. Moreover, we use our review to provide an up-to-date and empirically rooted call for less obvious, yet greatly promising, new or underemphasized directions for future research on the impact of university-based entrepreneurship education. This includes, for example, the use of novel impact indicators related to emotion and mind-set, focus on the impact indicators related to the intention-to-behavior transition, and exploring the reasons for some contradictory findings in impact studies including person-, context-, and pedagogical model-specific moderators.

Understanding the scale, location and nature conservation values of the lands over which Indigenous Peoples exercise traditional rights is central to implementation of several global conservation and climate agreements. However, spatial information on Indigenous lands has never been aggregated globally. Here, using publicly available geospatial resources, we show that Indigenous Peoples manage or have tenure rights over at least ~38 million km2 in 87 countries or politically distinct areas on all inhabited continents. This represents over a quarter of the world’s land surface, and intersects about 40% of all terrestrial protected areas and ecologically intact landscapes (for example, boreal and tropical primary forests, savannas and marshes). Our results add to growing evidence that recognizing Indigenous Peoples’ rights to land, benefit sharing and institutions is essential to meeting local and global conservation goals. The geospatial analysis presented here indicates that collaborative partnerships involving conservation practitioners, Indigenous Peoples and governments would yield significant benefits for conservation of ecologically valuable landscapes, ecosystems and genes for future generations. Land management and ownership by Indigenous Peoples are critical components of conservation strategies, but information on these has previously never been aggregated. Here, global data is compiled to show that Indigenous Peoples have tenure rights or manage a quarter of the world’s land area and 40% of all protected areas and intact ecosystems.

Regular physical activity helps to improve physical and mental functions as well as reverse some effects of chronic disease to keep older people mobile and independent. Despite the highly publicised benefits of physical activity, the overwhelming majority of older people in the United Kingdom do not meet the minimum physical activity levels needed to maintain health. The sedentary lifestyles that predominate in older age results in premature onset of ill health, disease and frailty. Local authorities have a responsibility to promote physical activity amongst older people, but knowing how to stimulate regular activity at the population-level is challenging. The physiological rationale for physical activity, risks of adverse events, societal and psychological factors are discussed with a view to inform public health initiatives for the relatively healthy older person as well as those with physical frailty. The evidence shows that regular physical activity is safe for healthy and for frail older people and the risks of developing major cardiovascular and metabolic diseases, obesity, falls, cognitive impairments, osteoporosis and muscular weakness are decreased by regularly completing activities ranging from low intensity walking through to more vigorous sports and resistance exercises. Yet, participation in physical activities remains low amongst older adults, particularly those living in less affluent areas. Older people may be encouraged to increase their activities if influenced by clinicians, family or friends, keeping costs low and enjoyment high, facilitating group-based activities and raising self-efficacy for exercise.

Although virtual reality (VR) is an emerging technology in tourism, little research has been conducted on what factors make consumers visit destinations presented by VR. To address this gap in the literature, this study developed a theoretical framework including authentic experience, cognitive and affective responses, attachment, and visit intention with VR tourism using a stimulus-organism-response (SOR) theory. The results revealed significant impacts of authentic experience on cognitive and affective responses, indicating that authentic experience is an important factor in VR tourism. The study identified cognitive and affective responses as significant mediators in predicting attachment and visit intention. The results demonstrated that the intention to visit places shown in VR tourism was influenced by attachment to VR. Cognitive response had a stronger influence than affective response on the intention to visit a destination in VR. This study sheds light on why potential tourists visit destinations shown in VR.