Showing papers by "Manchester Metropolitan University published in 2017"

The Impact of Entrepreneurship Education in Higher Education: A Systematic Review and Research Agenda

Abstract: 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.

Development of international terminology and definitions for texture-modified foods and thickened fluids used in dysphagia management: the IDDSI framework

Abstract: Dysphagia is estimated to affect ~8% of the world’s population (~590 million people). Texture-modified foods and thickened drinks are commonly used to reduce the risks of choking and aspiration. The International Dysphagia Diet Standardisation Initiative (IDDSI) was founded with the goal of developing globally standardized terminology and definitions for texture-modified foods and liquids applicable to individuals with dysphagia of all ages, in all care settings, and all cultures. A multi-professional volunteer committee developed a dysphagia diet framework through systematic review and stakeholder consultation. First, a survey of existing national terminologies and current practice was conducted, receiving 2050 responses from 33 countries. Respondents included individuals with dysphagia; their caregivers; organizations supporting individuals with dysphagia; healthcare professionals; food service providers; researchers; and industry. The results revealed common use of 3–4 levels of food texture (54 different names) and ≥3 levels of liquid thickness (27 different names). Substantial support was expressed for international standardization. Next, a systematic review regarding the impact of food texture and liquid consistency on swallowing was completed. A meeting was then convened to review data from previous phases, and develop a draft framework. A further international stakeholder survey sought feedback to guide framework refinement; 3190 responses were received from 57 countries. The IDDSI Framework (released in November, 2015) involves a continuum of 8 levels (0–7) identified by numbers, text labels, color codes, definitions, and measurement methods. The IDDSI Framework is recommended for implementation throughout the world.

Deep Voice: Real-time Neural Text-to-Speech

Abstract: We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For the segmentation model, we propose a novel way of performing phoneme boundary detection with deep neural networks using connectionist temporal classification (CTC) loss. For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original. By using a neural network for each component, our system is simpler and more flexible than traditional text-to-speech systems, where each component requires laborious feature engineering and extensive domain expertise. Finally, we show that inference with our system can be performed faster than real time and describe optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x speedups over existing implementations.

3D Printed Graphene Based Energy Storage Devices

Abstract: 3D printing technology provides a unique platform for rapid prototyping of numerous applications due to its ability to produce low cost 3D printed platforms. Herein, a graphene-based polylactic acid filament (graphene/PLA) has been 3D printed to fabricate a range of 3D disc electrode (3DE) configurations using a conventional RepRap fused deposition moulding (FDM) 3D printer, which requires no further modification/ex-situ curing step. To provide proof-of-concept, these 3D printed electrode architectures are characterised both electrochemically and physicochemically and are advantageously applied as freestanding anodes within Li-ion batteries and as solid-state supercapacitors. These freestanding anodes neglect the requirement for a current collector, thus offering a simplistic and cheaper alternative to traditional Li-ion based setups. Additionally, the ability of these devices’ to electrochemically produce hydrogen via the hydrogen evolution reaction (HER) as an alternative to currently utilised platinum based electrodes (with in electrolysers) is also performed. The 3DE demonstrates an unexpectedly high catalytic activity towards the HER (−0.46 V vs. SCE) upon the 1000th cycle, such potential is the closest observed to the desired value of platinum at (−0.25 V vs. SCE). We subsequently suggest that 3D printing of graphene-based conductive filaments allows for the simple fabrication of energy storage devices with bespoke and conceptual designs to be realised.

Trust and Credibility in Web-Based Health Information: A Review and Agenda for Future Research

Abstract: Background: Internet sources are becoming increasingly important in seeking health information, such that they may have a significant effect on health care decisions and outcomes. Hence, given the wide range of different sources of Web-based health information (WHI) from different organizations and individuals, it is important to understand how information seekers evaluate and select the sources that they use, and more specifically, how they assess their credibility and trustworthiness. Objective: The aim of this study was to review empirical studies on trust and credibility in the use of WHI. The article seeks to present a profile of the research conducted on trust and credibility in WHI seeking, to identify the factors that impact judgments of trustworthiness and credibility, and to explore the role of demographic factors affecting trust formation. On this basis, it aimed to identify the gaps in current knowledge and to propose an agenda for future research. Methods: A systematic literature review was conducted. Searches were conducted using a variety of combinations of the terms WHI, trust, credibility, and their variants in four multi-disciplinary and four health-oriented databases. Articles selected were published in English from 2000 onwards; this process generated 3827 unique records. After the application of the exclusion criteria, 73 were analyzed fully. Results: Interest in this topic has persisted over the last 15 years, with articles being published in medicine, social science, and computer science and originating mostly from the United States and the United Kingdom. Documents in the final dataset fell into 3 categories: (1) those using trust or credibility as a dependent variable, (2) those using trust or credibility as an independent variable, and (3) studies of the demographic factors that influence the role of trust or credibility in WHI seeking. There is a consensus that website design, clear layout, interactive features, and the authority of the owner have a positive effect on trust or credibility, whereas advertising has a negative effect. With regard to content features, authority of the author, ease of use, and content have a positive effect on trust or credibility formation. Demographic factors influencing trust formation are age, gender, and perceived health status. Conclusions: There is considerable scope for further research. This includes increased clarity of the interaction between the variables associated with health information seeking, increased consistency on the measurement of trust and credibility, a greater focus on specific WHI sources, and enhanced understanding of the impact of demographic variables on trust and credibility judgments. [J Med Internet Res 2017;19(6):e218]

Mechanical basis of bone strength: influence of bone material, bone structure and muscle action

Abstract: This review summarises current understanding of how bone is sculpted through adaptive processes, designed to meet the mechanical challenges it faces in everyday life and athletic pursuits, serving as an update for clinicians, researchers and physical therapists. Bone’s ability to resist fracture under the large muscle and locomotory forces it experiences during movement and in falls or collisions is dependent on its established mechanical properties, determined by bone’s complex and multidimensional material and structural organisation. At all levels, bone is highly adaptive to habitual loading, regulating its structure according to components of its loading regime and mechanical environment, inclusive of strain magnitude, rate, frequency, distribution and deformation mode. Indeed, the greatest forces habitually applied to bone arise from muscular contractions, and the past two decades have seen substantial advances in our understanding of how these forces shape bone throughout life. Herein, we also highlight the limitations of in vivo methods to assess and understand bone collagen, and bone mineral at the material or tissue level. The inability to easily measure or closely regulate applied strain in humans is identified, limiting the translation of animal studies to human populations, and our exploration of how components of mechanical loading regimes influence mechanoadaptation.

Skeletal Muscle Remodeling in Response to Eccentric vs. Concentric Loading: Morphological, Molecular, and Metabolic Adaptations

Abstract: Skeletal muscle contracts either by shortening or lengthening (concentrically or eccentrically, respectively); however, the two contractions substantially differ from one another in terms of mechanisms of force generation, maximum force production and energy cost. It is generally known that eccentric actions generate greater force than isometric and concentric contractions and at a lower metabolic cost. Hence, by virtue of the greater mechanical loading involved in active lengthening, eccentric resistance training (ECC RT) is assumed to produce greater hypertrophy than concentric resistance training (CON RT). Nonetheless, prevalence of either ECC RT or CON RT in inducing gains in muscle mass is still an open issue, with some studies reporting greater hypertrophy with eccentric, some with concentric and some with similar hypertrophy within both training modes. Recent observations suggest that such hypertrophic responses to lengthening vs. shortening contractions are achieved by different adaptations in muscle architecture. Whilst the changes in muscle protein synthesis in response to acute and chronic concentric and eccentric exercise bouts seem very similar, the molecular mechanisms regulating the myogenic adaptations to the two distinct loading stimuli are still incompletely understood. Thus, the present review aims to, (a) critically discuss the literature on the contribution of eccentric vs. concentric loading to muscular hypertrophy and structural remodeling, and, (b) clarify the molecular mechanisms that may regulate such adaptations. We conclude that, when matched for either maximum load or work, similar increase in muscle size is found between ECC and CON RT. However, such hypertrophic changes appear to be achieved through distinct structural adaptations, which may be regulated by different myogenic and molecular responses observed between lengthening and shortening contractions.

Oxygen Vacancies Evoked Blue TiO2(B) Nanobelts with Efficiency Enhancement in Sodium Storage Behaviors

Abstract: © 2017 WILEY-VCH Verlag GmbH & Co KGaA, Weinheim Oxygen vacancies (OVs) dominate the physical and chemical properties of metal oxides, which play crucial roles in the various fields of applications Density functional theory calculations show the introduction of OVs in TiO 2 (B) gives rise to better electrical conductivity and lower energy barrier of sodiation Here, OVs evoked blue TiO 2 (B) (termed as B-TiO 2 (B)) nanobelts are successfully designed upon the basis of electronically coupled conductive polymers to TiO 2 , which is confirmed by electron paramagnetic resonance and X-ray photoelectron spectroscopy The superiorities of OVs with the aid of carbon encapsulation lead to higher capacity (2105 mAh g −1 (B-TiO 2 (B)) vs 1027 mAh g −1 (W-TiO 2 (B)) at 05 C) and remarkable long-term cyclability (the retention of 944% at a high rate of 10 C after 5000 times) In situ X-ray diffractometer analysis spectra also confirm that an enlarged interlayer spacing stimulated by OVs is beneficial to accommodate insertion and removal of sodium ions to accelerate storage kinetics and preserve its original crystal structure The work highlights that injecting OVs into metal oxides along with carbon coating is an effective strategy for improving capacity and cyclability performances in other metal oxide based electrochemical energy systems

Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation

Abstract: This paper assesses topic coherence and human topic ranking of uncovered latent topics from scientific publications when utilizing the topic model latent Dirichlet allocation (LDA) on abstract and full-text data. The coherence of a topic, used as a proxy for topic quality, is based on the distributional hypothesis that states that words with similar meaning tend to co-occur within a similar context. Although LDA has gained much attention from machine-learning researchers, most notably with its adaptations and extensions, little is known about the effects of different types of textual data on generated topics. Our research is the first to explore these practical effects and shows that document frequency, document word length, and vocabulary size have mixed practical effects on topic coherence and human topic ranking of LDA topics. We furthermore show that large document collections are less affected by incorrect or noise terms being part of the topic-word distributions, causing topics to be more coherent and ranked higher. Differences between abstract and full-text data are more apparent within small document collections, with differences as large as 90% high-quality topics for full-text data, compared to 50% high-quality topics for abstract data.

Deep Voice: Real-time Neural Text-to-Speech

Abstract: We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For the segmentation model, we propose a novel way of performing phoneme boundary detection with deep neural networks using connectionist temporal classification (CTC) loss. For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original. By using a neural network for each component, our system is simpler and more flexible than traditional text-to-speech systems, where each component requires laborious feature engineering and extensive domain expertise. Finally, we show that inference with our system can be performed faster than real time and describe optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x speedups over existing implementations.

Value of Augmented Reality at Cultural Heritage Sites: A Stakeholder Approach

Abstract: As the pace of augmented reality (AR) adoption quickens, cultural heritage sites have begun to focus on the opportunities provided by this new and innovative technology. However, small organizations often fear making large investments in AR without a proof of concept due to the risk of failure. Therefore, it is imperative to explore the perceived value of AR from multiple stakeholders’ perspectives to ensure the long-term viability of technological innovations in small cultural heritage organizations. The present study uses a small museum in the UK to explore the perceived value of the implementation of AR within the museum context using a stakeholder approach. Qualitative data were gathered from twenty-four stakeholders via focus groups and interviews. This case study shows that AR has economic, experiential, social, epistemic, cultural and historical, and educational value from both internal and external stakeholders’ perspectives. AR is considered to be a way to preserve history, enhance visitor satisfaction, generate positive word-of-mouth, attract new target markets and contribute to a positive learning experience. This paper contributes to the knowledge gap in the area of stakeholders’ perceived value of AR for cultural heritage tourism.

The Role of Nrf2 in Cardiovascular Function and Disease

Abstract: Free radicals, reactive oxygen/nitrogen species (ROS/RNS), hydrogen sulphide, and hydrogen peroxide play an important role in both intracellular and intercellular signaling; however, their production and quenching need to be closely regulated to prevent cellular damage. An imbalance, due to exogenous sources of free radicals and chronic upregulation of endogenous production, contributes to many pathological conditions including cardiovascular disease and also more general processes involved in aging. Nuclear factor erythroid 2-like 2 (NFE2L2; commonly known as Nrf2) is a transcription factor that plays a major role in the dynamic regulation of a network of antioxidant and cytoprotective genes, through binding to and activating expression of promoters containing the antioxidant response element (ARE). Nrf2 activity is regulated by many mechanisms, suggesting that tight control is necessary for normal cell function and both hypoactivation and hyperactivation of Nrf2 are indicated in playing a role in different aspects of cardiovascular disease. Targeted activation of Nrf2 or downstream genes may prove to be a useful avenue in developing therapeutics to reduce the impact of cardiovascular disease. We will review the current status of Nrf2 and related signaling in cardiovascular disease and its relevance to current and potential treatment strategies.

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

Abstract: The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

Identifying and overcoming obstacles to the implementation of sustainable development at universities

Abstract: Although there have been proven and successful developments in the field of Higher Education for Sustainable Development over the past 15 years or so, there are still numerous challenges to be over...

Stress and Subjective Well-Being Among First Year UK Undergraduate Students

Abstract: Transition to university is stressful and successful adjustment is imperative for well-being. Historically research on transitional stress focussed on negative outcomes and ill health. This is the first UK study applying a positive psychology approach to investigate the characteristics that facilitate adjustment among new university students. A range of psychological strengths conceptualised as covitality factors, shown individually to influence the stress and subjective well-being (SWB) relationship were assessed among 192 first year UK undergraduates in week three of their first semester and again 6 months later. Path analyses revealed that optimism mediated the relationship between stress and negative affect (a component of SWB) over time, and academic self-efficacy demonstrated significant relationships with life satisfaction and positive affect. Contrary to predictions, stress levels remained stable over time although academic alienation increased and self-efficacy decreased. Optimism emerged as a key factor for new students to adjust to university, helping to buffer the impact of stress on well-being throughout the academic year. Incorporating stress management and psycho-educational interventions to develop strengths is discussed as a way of promoting confidence and agency in new students to help them cope better with the stress at university.

Cold Fusion: Training Seq2Seq Models Together with Language Models

Abstract: Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task. We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying i) faster convergence and better generalization, and ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.

Recent loss of closed forests is associated with Ebola virus disease outbreaks

Abstract: Ebola virus disease (EVD) is a contagious, severe and often lethal form of hemorrhagic fever in humans. The association of EVD outbreaks with forest clearance has been suggested previously but many aspects remained uncharacterized. We used remote sensing techniques to investigate the association between deforestation in time and space, with EVD outbreaks in Central and West Africa. Favorability modeling, centered on 27 EVD outbreak sites and 280 comparable control sites, revealed that outbreaks located along the limits of the rainforest biome were significantly associated with forest losses within the previous 2 years. This association was strongest for closed forests (>83%), both intact and disturbed, of a range of tree heights (5–>19 m). Our results suggest that the increased probability of an EVD outbreak occurring in a site is linked to recent deforestation events, and that preventing the loss of forests could reduce the likelihood of future outbreaks.

The Relationship between the Physical Activity Environment, Nature Relatedness, Anxiety, and the Psychological Well-being Benefits of Regular Exercisers.

Abstract: Research from a variety of scientific fields suggests that physical activity in nature and feelings of connection to nature enhance psychological health and well-being. This study investigated the psychological health and well-being impact of the physical activity environment for those already undertaking the recommended weekly amount of physical activity. This topic is important for the design of health and well-being environments and interventions involving physical activity. Participants (N = 262) aged 18–71 years (M = 34.5, SD = 13.1) who met the UK physical activity guidelines completed the Nature Relatedness Scale, the trait section of the State Trait Inventory for Cognitive and Somatic Anxiety and the Psychological Well-Being Scale. Analysis via Multivariate ANOVA indicated that participants who engaged in outdoor physical activity reported significantly lower somatic anxiety levels and higher Nature Relatedness experience (NRexp). Significant results were not evident for wellbeing. Hierarchical regressions revealed that the psychological well-being facet of autonomy, NRexp, and outdoor physical activity predicted lower somatic anxiety, whereas indoor physical activity predicted higher somatic anxiety. Results indicate that somatic anxiety is lower for outdoor physical activity participation, and that outdoor activity, in conjunction with autonomy and NRexp, predicts lower anxiety levels. The findings extend previous work by demonstrating the impact of the physical activity environment on anxiety levels, as well as the contribution of outdoor physical activity and well-being facets to the previously established Nature Relatedness-anxiety relationship.

Nitrogen deposition and plant biodiversity: past, present, and future

Abstract: Reactive nitrogen (N) deposition from intensive agricultural and industrial activity has been identified as the third greatest threat to global terrestrial biodiversity, after land-use and climate change. While the impacts of N deposition are widely acknowledged, their magnitude is poorly quantified. We combine N deposition models, empirical response functions, and vegetation mapping to simulate the effects of N deposition on plant species richness from 1900 to 2030, using the island of Great Britain as a case study. We find that current species richness values – when averaged across five widespread habitat types – are approximately one-third less than without N deposition. Our results suggest that currently expected reductions in emissions will achieve no more than modest increases in species richness by 2030, and that emissions cuts based on habitat-specific “critical loads” may be an inefficient approach to managing N deposition for the protection of plant biodiversity. The effects of N deposition on biodiversity are severe and are unlikely to be quickly reversed.