64623 research outputs found
Sort by
Consistent and coherent treatment of uncertainties and dependencies in fatigue crack growth calculations using multi-level Bayesian models
Full text linkEngineers perform fatigue assessments to support structural integrity management. Given that the purpose of these calculations is linked to problems of decision making under various sources of uncertainty, probabilistic methods are often more useful than deterministic alternatives. Guidance on the direct probabilistic application of procedures in existing industrial standards is currently limited and dependencies between marginal probabilistic models are generally not considered, despite their potential significance being acknowledged. This paper proposes the use of Bayesian data analysis as a flexible and intuitive approach to coherently and consistently account for uncertainty and dependency in fatigue crack growth rate models. Various Bayesian models are established and presented, based on the same data as the existing models in BS 7910 (a widely used industrial standard). The models are compared in terms of their out of sample predictive accuracy, using methods with a basis in information theory and cross-validation. The Bayesian models exhibit an improved performance, with the most accurate predictions resulting from multi-level (hierarchical) models, which account for variation between constituent test datasets and partially pool information
How does habit form? Guidelines for tracking real-world habit formation
No full textAdvances in understanding how habit forms can help people change their behaviour in ways that make them happier and healthier. Making behaviour habitual, such that people automatically act in associated contexts due to learned context-response associations, offers a mechanism for maintaining new, desirable behaviours even when conscious motivation wanes. This has prompted interest in understanding how habit forms in the real world. To reliably inform intervention design, habit formation studies must be conceptually and methodologically sound. This paper proposes methodological criteria for studies tracking real-world habit formation, or potential moderators of the effect of repetition on formation. A narrative review of habit theory was undertaken to extract essential and desirable criteria for modelling how habit forms in naturalistic settings, and factors that influence the relationship between repetition and formation. Next, a methodological review identified exemplary real-world habit formation studies according to these criteria. Fourteen methodological criteria, capturing study design (four criteria), measurement (six criteria), and analysis and interpretation (four criteria), were derived from the narrative review. Five extant studies were found to meet our criteria. Adherence to these criteria should increase the likelihood that studies will offer revealing conclusions about how habits develop in real-world settings
The Post-Pandemic Lecture: Views from Academic Staff across the UK
Full text linkCOVID-19 forced the closure of UK universities. One effect of this was a change in how lectures, and their recordings, were made and used. In this research, we aimed to address two related research questions. Firstly, we aimed to understand how UK universities replaced in-person lectures and, secondly, to establish what academic staff believed the post-pandemic lecture would look like. In a mixed-methods study, we collected anonymous quantitative and qualitative data from 87 academics at 36 UK institutions. Analysis revealed that respondents recognised the value and importance of interactive teaching and indicated that the post-pandemic lecture would and should make greater use of this. Data also revealed positive views of lecture capture, in contrast to pre-pandemic studies, and demonstrated that staff recognised their value for those who were unable to attend, or who had specific learning differences. However, staff also recognised the value of asynchronous lecture videos within a blended or flipped approach. This study provides evidence that the pandemic has engendered changes in attitudes and practices within UK higher education that are conducive to educational reform
Ion-driven nanograin formation in early-stage degradation of tri-cation perovskite films
Full text linkThe operational stability of organic–inorganic halide perovskite based solar cells is a challenge for widespread commercial adoption. The mobility of ionic species is a key contributor to perovskite instability since ion migration can lead to unfavourable changes in the crystal lattice and ultimately destabilisation of the perovskite phase. Here we study the nanoscale early-stage degradation of mixed-halide mixed-cation perovskite films under operation-like conditions using electrical scanning probe microscopy to investigate the formation of surface nanograin defects. We identify the nanograins as lead iodide and study their formation in ambient and inert environments with various optical, thermal, and electrical stress conditions in order to elucidate the different underlying degradation mechanisms. We find that the intrinsic instability is related to the polycrystalline morphology, where electrical bias stress leads to the build-up of charge at grain boundaries and lateral space charge gradients that destabilise the local perovskite lattice facilitating escape of the organic cation. This mechanism is accelerated by enhanced ionic mobility under optical excitation. Our findings highlight the importance of inhibiting the formation of local charge imbalance, either through compositions preventing ionic redistribution or local grain boundary passivation, in order to extend operational stability in perovskite photovoltaics
Relational Communication Style in Business, Marketing and Service Interactions
Full text linkVerbal communication (i.e. text or spoken language use) can be evaluated under two aspects, namely content and style. Thereby, communication style is independent of the content (what is conveyed) and consist of stylistic elements concerning how a sentence is expressed. This dissertation concentrates on three different aspects which influence how interactants communicate aka factors influencing communication style: (1) the relationship between interactants i.e. doctors and patients, (2) the interaction as a representation of a relationship i.e. marketing communication, and (3) individual differences i.e. empathy.First, synthesizing marketing communication research in a meta-analytic review, shows that communication style in marketing can be summarized containing five dimensions, namely, arousal, composure, dominance, intimacy and task-orientation. The influence of the five communication styles on marketing outcomes, attitude, intention and behavior is established and boundary conditions are examined.Second, the framework of communication styles deduced in the meta-analysis is used to examine how these dimensions influence perceptions of patients about their relationship with a health service professional. Using customer reviews of patients and exploiting text analysis to extract the dimensions in the verbatim review comments provides evidence for the dimensional structure of communication styles in doctor-patient relationships. Further, the individual and joint effects of the dimensions are linked to patient satisfaction.Third, the concept of empathy, being a relevant variable in communication, is examined through bibliometric techniques. Applying bibliographic coupling shows that research on empathy in the business field is built on five clusters, two concept related clusters (perspective taking and empathic concern), and three content related clusters (ethics, sales and leadership). Content and co-word analysis provides additional insights, e.g. similarities or differences in conceptualization, definitions, keywords or methodologies, within and across clusters. Further, for all three topics regarded in this dissertation, research gaps are identified and translated into future research directions
SEMANTIC DATA INNOVATION HUBS: ANSWER AS A SERVICE
Full text linkThe open data market size is estimated at €184 billion and forecast to reach between €199.51 and €334.21 billion in 2025. In this paper, we conceptualise the semantic data innovation platform, which will be able to answer inter-disciplinary questions via semantic reasoning over open data. We use 750 open animal healthcare datasets to exemplify this work, covering mainly poultry, swine, ruminants, and other livestock, which are complemented by open data from complementary domains, such as geographic location, medicine and virology. We aggregate the domain knowledge (classes) and enable the logical links (properties) between these classes. The prototype encapsulates the complexity of animal healthcare knowledge into ontology, which can answer complex questions using semantic reasoning on the datasets (answer-as-a-service)
A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer’s Disease and Mild Cognitive Impairmen
Full text link—Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects, and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs. Clinical Relevance— The preliminary findings from this study show that deep learning applied to the analysis of the EEG can classify subjects with accuracies close to 100%
The Use of Comparative Genomic Analysis for the Development of Subspecies-Specific PCR Assays for Mycobacterium abscessus
Full text linkMycobacterium abscessus complex (MABC) is an important pathogen of immunocompromised patients. Accurate and rapid determination of MABC at the subspecies level is vital for optimal antibiotic therapy. Here we have used comparative genomics to design MABC subspecies-specific PCR assays. Analysis of single nucleotide polymorphisms and core genome multilocus sequence typing showed clustering of genomes into three distinct clusters representing the MABC subspecies M. abscessus, M. bolletii and M. massiliense. Pangenome analysis of 318 MABC genomes from the three subspecies allowed for the identification of 15 MABC subspecies-specific genes. In silico testing of primer sets against 1,663 publicly available MABC genomes and 66 other closely related Mycobacterium genomes showed that all assays had >97% sensitivity and >98% specificity. Subsequent experimental validation of two subspecies-specific genes each showed the PCR assays worked well in individual and multiplex format with no false-positivity with 5 other mycobacteria of clinical importance. In conclusion, we have developed a rapid, accurate, multiplex PCR-assay for discriminating MABC subspecies that could improve their detection, diagnosis and inform correct treatment choice
We're on mute! Exclusion of nurses' voices in national decisions and responses to COVID-19: An international perspective
Full text linkNurses are the largest healthcare workforce and have had direct, intense and sustained contact with COVID-19 patients throughout the pandemic playing an essential and frontline role in the COVID-19 response. Nurses have worked tirelessly and undertaken multiple roles during the pandemic including education, treatment, prevention, vaccination and research often in uncertain situations and to the detriment of their physical and mental health. They have also managed and cared for distressed patients and their families, and many have been redeployed to other roles often outside of their usual duties, all factors which have affected their well-being. They have publicly been lauded as ‘heroes’. Yet, their voices and perspectives are seldom heard or included in COVID-19 decision-making and in the development of interventions and responses at all levels from individual health services to national policymaking. Indeed, it has felt like these voices have been muted and excluded. Nurses' unique knowledge, expertise, needs and lived experiences are vital to the COVID-19 response. Without their inclusion, COVID-19 decision-making and initiatives are unlikely to be successful and patient outcomes poorer
Few-shot Website Fingerprinting attack with Meta-Bias Learning
No full textWebsite fingerprinting (WF) attack aims to identify which website a user is visiting from the traffic data patterns. Whilst existing methods assume many training samples, we investigate a more realistic and scalable few-shot WF attack with only a few labeled training samples per website. To solve this problem, we introduce a novel Meta-Bias Learning (MBL) method for few-shot WF learning. Taking the meta-learning strategy, MBL simulates and optimizes the target tasks. Moreover, a new model parameter factorization idea is introduced for facilitating meta-training with superior task adaptation. Expensive experiments show that our MBL outperforms significantly existing hand-crafted feature and deep learning based alternatives in both closed-world and open-world attack scenarios, at the absence and presence of defense
Data sources:
Access Repository Dashboard
Do you manage University of Surrey? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!