132 research outputs found

    Exploring the use of nature as an adjunct to psychological interventions for depression in young populations

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    Depression in adolescence is a global priority and it is critical to identify effective and accessible interventions. This systematic review aimed to synthesise experimental research on nature-based interventions (NBIs), to determine effects on depressive symptoms in young people. The secondary research question sought to understand characteristics of effective NBIs. A comprehensive systematic search was conducted across major and grey literature databases and papers were screened according to specified criteria. Participantsā€™ ages were required to be between 10 and 24 years and studies needed to use an experimental design, including a control group. Experimental conditions were defined by psychotherapeutic interventions with nature exposure and outcomes measured either clinical symptomatology or subjective states of depression. Ten papers were identified, quality assessed and summarised in a narrative synthesis. Thirteen significant effects were reported in nine studies, highlighting the potential for NBIs as effective interventions for depressive symptoms in young people. However, due to methodological biases, such as lack of randomisation or control over group differences and frequent use of passive control groups, there remains considerable uncertainty over the effectiveness of NBIs. Characteristics of effective NBIs are tentatively discussed, however, further work is needed to clarify which aspects specifically contribute to the beneficial effects observed. Future research should seek to address the limitations of small samples, selection biases and test NBIs against more comparable and evidence-based interventions. It is hoped future studies will consider the inclusion of clinical populations, to explore the utility of NBIs as a treatment option for adolescent depression

    Land Use and Land Cover Mapping in a Changing World

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    It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classiļ¬cation systems

    Systematic review of digital self-management therapeutics for irritable bowel syndrome; and, Exploring the feasibility of an Acceptance and Commitment Therapy smartphone app intervention for irritable bowel syndrome

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    BACKGROUND AND AIMS: Irritable Bowel Syndrome (IBS) is the most common gastrointestinal (GI) disorder; frequently associated with painful physical symptoms, psychological distress and impaired quality of life. While there is no existing ā€˜gold standardā€™ treatment for IBS; evidence highlighting the link between the gut and the brain has informed new treatment pathways: with a particular emphasis on psychological approaches in managing IBS. However, long waiting times and pressures on healthcare services often result in patientsā€™ needs not being adequately met. Digital self-management therapeutics are increasingly applied in the management of health conditions, including IBS. The first chapter of this thesis systematically reviews Randomised Controlled Trials (RCTs) exploring the efficacy of digital self-management therapeutics for IBS. Specifically, this is examined in relation to physical symptomology and quality of life outcomes. In the second chapter, smartphone-delivered Acceptance and Commitment Therapy (ACT) is considered as a therapeutic approach for the management of IBS. While preliminary studies have demonstrated efficacy of ACT for IBS, digital delivery of ACT for IBS has not previously been explored. This study explores the feasibility, acceptability, and efficacy of trialling an ACT smartphone application for IBS patients. METHODS: In Chapter One, the evidence base for digital self-management therapeutics for IBS is systematically reviewed. Relevant databases were searched using inclusion and exclusion criteria to identify studies for review. In Chapter Two, recruitment methods, psychometric measures, app building and contents of the intervention are discussed. 83 eligible participants were identified by four GI Consultants across NHS Lothian, NHS Grampian and Imperial College Healthcare NHS Trust. 44 participants downloaded the app, with 29 participants providing data at two-month follow-up. RESULTS: In Chapter One, the systematic search identified 12 relevant RCTs for review. Their methodological quality was appraised by two reviewers, using the Cochrane Risk of Bias (RoB 2) tool. These studies demonstrated moderate-large effects in improving physical symptomology and moderate-large effects in improving quality of life, and generally these improvements were maintained at longer-term follow-up. Evidence comparing treatments to active controls, and longer-term comparison to control groups, were lacking. In Chapter Two, both feasibility and efficacy of the trial were explored. The trial was deemed feasible in terms of recruitment and retention. Paired-sample t-tests demonstrated that use of the ACT self-management application showed significant improvements in IBS acceptance, quality of life, and GI-related anxiety. Similar to a previous ACT self-help trial, improvements in IBSrelated avoidance behaviours were not found. Improvements in GI physical symptomology were noted; however contrary to hypothesis, these improvements were not significant. DISCUSSION: Results from Chapter One indicate the efficacy of digital self-management interventions for IBS; in terms of both physical symptoms and quality of life, with maintained improvements at longer-term follow-up. Large heterogeneity in level of guidance, samples, varying definitions of adherence with interventions, high levels of attrition and methodological quality limit confidence in the resultsā€™ generalisability. Suggestions are offered for both future systematic reviews and empirical work in the field based on these findings. Results in Chapter Two provide preliminary evidence of the feasibility and efficacy of a digital ACT smartphone intervention for management of IBS. Attrition and adherence with the intervention are discussed in the context of these results, alongside clinical implications for use of such an intervention as part of a stepped-care approach to IBS. This may be informed by screening GI symptomology at baseline to assess suitability of this lowintensity intervention going forward. A future larger-scale trial is warranted to further explore these preliminary findings

    Exploring psychological peritraumatic risk factors and safety behaviours as key mechanisms in the onset and maintenance of PTSD; a Systematic Review and Meta-Analysis

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    Background After Post-Traumatic Stress Disorder (PTSD) was first conceptualised in 1980, decades of valuable research have contributed to the development of cognitive theories and evidence-based treatments, which are used as front-line treatments in NHS services in line with NICE guidance. However, there are some elements of the cognitive model which are under-researched, such as the role of safety behaviours in the development and maintenance of PTSD. Research is also yet to provide clarity on the role of psychological peritraumatic risk factors for PTSD in adults. Method This portfolio contains two systematic reviews and meta-analyses. The first review concerns the relationship between safety behaviours and PTSD in adults, and includes six studies (n = 628). The second review explores peritraumatic risk factors for PTSD in adults, and includes 63 studies (n=20,335). Results The first paper regarding safety behaviours yielded a large effect r=0.62, supporting the idea that engaging in safety behaviours is associated with the development and/or maintenance of PTSD in adults. Regarding the second paper, peritraumatic subjective threat and peritraumatic dissociation yielded moderate estimates of population effect size (r=.39, r=.39), and peritraumatic data-driven processing yielded a small estimated population effect size (r=.26). Both studies were affected by high levels of heterogeneity. Each paper discusses the outcome of moderator analyses, limitations, clinical implications and suggestions for future research. Conclusion Overall, there were a small number of studies available for inclusion in the first review, despite safety behaviours forming an important part of the cognitive model for over twenty years. While the findings are in line with the Ehlers and Clark model (2020), more research is needed to clarify the directionality of the relationship. The second meta-analysis highlighted the need for more studies to investigate the predictive risk of a wider range of peritraumatic emotions e.g. guilt, shame, anger and disgust

    Land Use and Land Cover Mapping in a Changing World

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    It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classiļ¬cation systems

    High Frequency Physiological Data Quality Modelling in the Intensive Care Unit

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    Intensive care medicine is a resource intense environment in which technical and clinical decision making relies on rapidly assimilating a huge amount of categorical and timeseries physiologic data. These signals are being presented at variable frequencies and of variable quality. Intensive care clinicians rely on high frequency measurements of the patient's physiologic state to assess critical illness and the response to therapies. Physiological waveforms have the potential to reveal details about the patient state in very fine resolution, and can assist, augment, or even automate decision making in intensive care. However, these high frequency time-series physiologic signals pose many challenges for modelling. These signals contain noise, artefacts, and systematic timing errors, all of which can impact the quality and accuracy of models being developed and the reproducibility of results. In this context, the central theme of this thesis is to model the process of data collection in an intensive care environment from a statistical, metrological, and biosignals engineering perspective with the aim of identifying, quantifying, and, where possible, correcting errors introduced by the data collection systems. Three different aspects of physiological measurement were explored in detail, namely measurement of blood oxygenation, measurement of blood pressure, and measurement of time. A literature review of sources of errors and uncertainty in timing systems used in intensive care units was undertaken. A signal alignment algorithm was developed and applied to approximately 34,000 patient-hours of simultaneously collected electroencephalography and physiological waveforms collected at the bedside using two different medical devices

    Modern Statistical Models and Methods for Estimating Fatigue-Life and Fatigue-Strength Distributions from Experimental Data

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    Engineers and scientists have been collecting and analyzing fatigue data since the 1800s to ensure the reliability of life-critical structures. Applications include (but are not limited to) bridges, building structures, aircraft and spacecraft components, ships, ground-based vehicles, and medical devices. Engineers need to estimate S-N relationships (Stress or Strain versus Number of cycles to failure), typically with a focus on estimating small quantiles of the fatigue-life distribution. Estimates from this kind of model are used as input to models (e.g., cumulative damage models) that predict failure-time distributions under varying stress patterns. Also, design engineers need to estimate lower-tail quantiles of the closely related fatigue-strength distribution. The history of applying incorrect statistical methods is nearly as long and such practices continue to the present. Examples include treating the applied stress (or strain) as the response and the number of cycles to failure as the explanatory variable in regression analyses (because of the need to estimate strength distributions) and ignoring or otherwise mishandling censored observations (known as runouts in the fatigue literature). The first part of the paper reviews the traditional modeling approach where a fatigue-life model is specified. We then show how this specification induces a corresponding fatigue-strength model. The second part of the paper presents a novel alternative modeling approach where a fatigue-strength model is specified and a corresponding fatigue-life model is induced. We explain and illustrate the important advantages of this new modeling approach.Comment: 93 pages, 27 page

    Optimal demand-supply energy management in smart grids

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    Everything goes down if you do not have power: the financial sector, refineries and water. The grid underlies the rest of the countryā€™s critical infrastructure. This thesis focuses on four specific problems to balance demand-supply gap with higher reliability, efficiency and economical operation of the modern power grid. The first part investigates the economic dispatch problem with uncertain power sources. The classic economic dispatch problems seek thermal power generation to meet the demand most efficiently. However, this project exploits two different power sources such as wind and solar power generation into the standard optimal power flow framework. The stochastic nature of renewable energy sources (RES) is modeled using Weibull and Lognormal probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. The calculation of best power dispatch is proposed using a cost function. The second part investigates demand-side management (DSM) strategies to minimize energy wastage by changing the time pattern and magnitude of utility load at the consumer side. The main objective of DSM is to flatten the demand curve by encouraging end-users to shift energy consumption to off-peak hours or to consume less power during peak times. It is more appropriate to follow the generation pattern in many cases instead of flattening the demand curve. It becomes more challenging when the future grid accommodates the penetration of distributed energy resources in a greater manner. In both scenarios, there is an ultimate need to control energy consumption. Effective DSM strategies would help to get an accurate balance between both ends, i.e., the supply-side and demand-side, effectively reducing power demand peaks and more efficient operation of the whole system. The gap between power demand and supply can be balanced if power peak loads are minimized. The third part of the thesis then focuses on modeling the consumption behavior of end-users. For this purpose, a novel artificial intelligence and machine learning-based forecasting model is developed to analyze big data in the smart grid. Three modules namely feature selection, feature extraction and classification are proposed to solve big data problems such as feature redundancy and high dimensionality to generate quality data for classifier training and better prediction results. The last part of this thesis investigates the problem of electricity theft to minimize non technical losses and power disruptions in the power grid. Electricity theft with its many facets usually has an enormous cost to utilities compared to non-payment because of energy wastage and power quality problems. With the recognition of the internet of things (IoT) technologies and data-driven approaches, power utilities have enough tools to combat electricity theft and fraud. An integrated framework is proposed that combines three distinct modules such as data preprocessing, data class balancing and final classification to make accurate electrical consumption theft predictions in smart grids. The result of our solution to balance the electricity demand-supply gap can provide helpful information to grid planners seeking to improve the resilience of the power grid to outages and disturbances. All parts of this thesis include extensive experimental results on case studies, including realistic large-scale instances

    Industrial Applications: New Solutions for the New Era

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    This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section
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