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Institution

University of Bedfordshire

EducationLuton, Bedford, United Kingdom
About: University of Bedfordshire is a education organization based out in Luton, Bedford, United Kingdom. It is known for research contribution in the topics: Population & Social work. The organization has 3860 authors who have published 6079 publications receiving 143448 citations. The organization is also known as: University of Luton.


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Journal ArticleDOI
18 Jun 2012-Analyst
TL;DR: Many of the methods presented in this review are Machine Learning and Statistical techniques that are extendable to other forms of computer-based biomedical analysis, including mass spectrometry and magnetic resonance.
Abstract: Applying Fourier-transform infrared (FTIR) spectroscopy (or related technologies such as Raman spectroscopy) to biological questions (defined as biospectroscopy) is relatively novel. Potential fields of application include cytological, histological and microbial studies. This potentially provides a rapid and non-destructive approach to clinical diagnosis. Its increase in application is primarily a consequence of developing instrumentation along with computational techniques. In the coming decades, biospectroscopy is likely to become a common tool in the screening or diagnostic laboratory, or even in the general practitioner's clinic. Despite many advances in the biological application of FTIR spectroscopy, there remain challenges in sample preparation, instrumentation and data handling. We focus on the latter, where we identify in the reviewed literature, the existence of four main study goals: Pattern Finding; Biomarker Identification; Imaging; and, Diagnosis. These can be grouped into two frameworks: Exploratory; and, Diagnostic. Existing techniques in Quality Control, Pre-processing, Feature Extraction, Clustering, and Classification are critically reviewed. An aspect that is often visited is that of method choice. Based on the state-of-art, we claim that in the near future research should be focused on the challenges of dataset standardization; building information systems; development and validation of data analysis tools; and, technology transfer. A diagnostic case study using a real-world dataset is presented as an illustration. Many of the methods presented in this review are Machine Learning and Statistical techniques that are extendable to other forms of computer-based biomedical analysis, including mass spectrometry and magnetic resonance.

216 citations

Journal ArticleDOI
TL;DR: In this article, the effectiveness of power ultrasound for the microbial decontamination of minimally processed fruits and vegetables was studied, and the results showed that the frequency of ultrasound treatment (25, 32-40, 62-70 kHz) had no significant effect on decontaminant efficiency.
Abstract: Summary The effectiveness of power ultrasound for the microbial decontamination of minimally processed fruits and vegetables was studied. Reductions in Salmonella typhimurium attached to iceberg lettuce obtained by cleaning with water, chlorinated water, ultrasound with water and ultrasound with chlorinated water were 0.7, 1.7, 1.5 and 2.7 logs, respectively, for small-scale (2 L) trials. The cleaning action of cavitation appears to remove cells attached to the surface of fresh produce, rendering the pathogens more susceptible to the sanitizer. For large-scale (40 L) trials, the addition of chlorine to water in the tank gave a systematic difference in Escherichia coli decontamination efficiency. However, the frequency of ultrasound treatment (25, 32–40, 62–70 kHz) had no significant effect on decontamination efficiency (P > 0.69). With the potentially high capital expenditure together with the expensive process of optimization and water treatment, it is unlikely that the fresh produce industry would be willing to take up this technology. Furthermore, the additional one log reduction achieved by applying ultrasound to a chlorinated water wash does not completely eliminate the risk of pathogens on fresh produce.

216 citations

Journal ArticleDOI
03 Oct 2019-Sensors
TL;DR: This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer- aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS
Abstract: Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

214 citations

Journal ArticleDOI
Abstract: SUMMARY Research findings from several countries suggest that academic work has become comparatively stressful, with potentially serious consequences for the workforce and the quality of higher education. This article reports the findings of a study that examined work demands, work-life balance and wellbeing in UK academic staff. Job demands and levels of psychological distress were high and working during evenings and weekends was commonplace. Most academics surveyed, however, were at least moderately satisfied with their jobs. Work-life balance was generally poor and most respondents wished for more separation between their work and home lives. Academics who reported more work-life conflict and perceived a greater discrepancy between their present and ideal levels of work-life integration tended to be less healthy, less satisfied with their jobs, and more likely to have seriously considered leaving academia. On the whole, academics that perceived more control over their work, more schedule flexibility a...

214 citations

Journal ArticleDOI
TL;DR: The current findings strengthen the claim that burnout in elite athletes may not simply be “motivation gone awry” as Gould has suggested, but an inevitable consequence of exhibiting a maladaptive motivational profile.
Abstract: The purpose of this study was to investigate athlete burnout from a social-cognitive perspective by examining the relationship between social cognitive motivational variables at the start of a season and signs of burnout in elite athletes at the end of the season. Participants were 141 (F=60, M=81) elite winter sport athletes competing in Alpine skiing, Biathlon, Nordic Combined, Nordic skiing, and Speed skating. Participants completed a comprehensive motivation assessment package at the start of the season and a further burnout inventory at season's end. Results indicated that motivational dispositions, measures of the achievement climate, perceived ability and dimensions of perfectionism were associated with burnout in a conceptually consistent manner. Furthermore, the elite athletes could be grouped into two motivational profiles based on variables measured at the start of the season, one being adaptive and the other maladaptive. At season's end, the two different motivational profiles yielded distinctively different responses on an inventory assessing signs of burnout. The current findings strengthen the claim that burnout in elite athletes may not simply be "motivation gone awry" as Gould has suggested, but an inevitable consequence of exhibiting a maladaptive motivational profile.

214 citations


Authors

Showing all 3892 results

NameH-indexPapersCitations
Jie Zhang1784857221720
Oscar H. Franco11182266649
Timothy J. Foster9842032338
Christopher P. Denton9567542040
Ian Kimber9162028629
Michael J. Gidley8642024313
David Carling8618645066
Anthony Turner7948924734
Rhys E. Green7828530428
Vijay Kumar Thakur7437517719
Dave J. Adams7328319526
Naresh Magan7240017511
Aedin Cassidy7021817788
David A. Basketter7032516639
Richard C. Strange6724917805
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20236
202248
2021345
2020363
2019323
2018329