19,364 research outputs found
Epilepsy Mortality: Leading Causes of Death, Co-morbidities, Cardiovascular Risk and Prevention
a reuptake inhibitor selectively prevents seizure-induced sudden death in the DBA/1 mouse model of sudden unexpected ... Bilateral lesions of the fastigial nucleus prevent the recovery of blood pressure following hypotension induced by ..
Recursive Singular Spectrum Analysis for Induction Machines Unbalanced Rotor Fault Diagnosis
One of the major challenges of diagnosing rotor symmetry faults in induction machines is severe modulation of fault and supply frequency components. In particular, existing techniques are not able to identify fault components in the case of low slips. In this paper, this problem is tackled by proposing a novel approach. First, a new use of singular spectrum analysis (SSA), as a powerful spectrum analyser, is introduced for fault detection. Our idea is to treat the stator current signature of the wound rotor induction machine as a time series. In this approach, the current signature is decomposed into several eigenvalue spectra (rather than frequency spectra) to find a subspace where the fault component is recognisable. Subsequently, the fault component is detected using some data-driven filters constructed with the knowledge about characteristics of supply and fault components. Then, an inexpensive peak localisation procedure is applied to the power spectrum of the fault component to identify the exact frequency of the fault. The fault detection and localisation methods are then combined in a recursive regime to further improve the diagnosisâ performance particularly at high rotor speeds and small rotor faults. The proposed approach is data-driven and is directly applied to the raw signal with no suppression or filtration of the frequency harmonics with a low computational complexity. The numerical results obtained with real data at several rotation speeds and fault severities, unveil the effectiveness and real-time feature of the proposed approach
The Linear and Nonlinear Relationship between Infrastructure and FDI in India
The study examines the linear and nonlinear relationship between Infrastructure and FDI, to understand whether there is a significant difference or not concerning the FDI equity inflows to infrastructure projects. The ARDL and Granger causality methods to cointegration; propose the existence of long-run function in two-directional causalities between foreign direct investment and infrastructure, whereas the nonlinear autoregressive distributed lag (ARDL) validates the asymmetries in the relationship between FDI and Infrastructure. The outcomes of the study are that foreign direct investment inflows are significant to improve the infrastructure projects in various sectors, in the short-run and long run. As enlightening infrastructure is dynamic to attract FDI, outcomes will be predominantly valuable to policymakers and related to the emerging markets
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Quantitative Character and the Composite Account of Phenomenal Content
I advance an account of quantitative character, a species of phenomenal character that presents as an intensity (cf. a quality) and includes experience dimensions such as loudness, pain intensity, and visual pop-out. I employ psychological and neuroscientific evidence to demonstrate that quantitative characters are best explained by attentional processing, and hence that they do not represent external qualities. Nonetheless, the proposed account of quantitative character is conceived as a compliment to the reductive intentionalist strategy toward qualitative states; I argue that an account of perceptual experience that combines a tracking account of qualitative character with my functionalist proposal of quantitative character permits replies to some notoriously difficult problems for tracking representationalism without sacrificing its chief virtues
Interactive Sonic Environments: Sonic artwork via gameplay experience
The purpose of this study is to investigate the use of video-game technology in the design and implementation of interactive sonic centric artworks, the purpose of which is to create and contribute to the discourse and understanding of its effectiveness in electro-acoustic composition highlighting the creative process. Key research questions include: How can the language of electro-acoustic music be placed in a new framework derived from videogame aesthetics and technology? What new creative processes need to be considered when using this medium? Moreover, what aspects of 'play' should be considered when designing the systems? The findings of this study assert that composers and sonic art practitioners need little or no coding knowledge to create exciting applications and the myriad of options available to the composer when using video-game technology is limited only by imagination. Through a cyclic process of planning, building, testing and playing these applications the project revealed advantages and unique sonic opportunities in comparison to other sonic art installations. A portfolio of selected original compositions, both fixed and open are presented by the author to complement this study. The commentary serves to place the work in context with other practitioners in the field and to provide compositional approaches that have been taken
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Know Your Bugs: A Collaborative Evaluation of a Community Health Education Module That Aims to be Accessible to Adults with Learning Disabilities
The right to health should be a fundamental right of everyone. However, despite initiatives to improve the health of adults with learning disabilities, concerns about poorer health and health inequalities remain, and have been exacerbated by the COVID-19 pandemic. Accessible health promotion can help to overcome barriers to healthy behaviour but the effectiveness of health education in infection prevention and self-care is unknown. This research aimed to understand the health education experiences of adults with learning disabilities regarding a module designed to improve knowledge about self-care, infection prevention and antibiotic use.
Beginning with a scoping review of âwhat worksâ, this research involved observation of the learning context in two locations and semi-structured interviews with 18 course participants to explore health knowledge and behaviour change in the short, medium and longer term. Data were analysed iteratively, addressing the realist concept of context/mechanism/outcome configurations.
Participants had a positive learning experience and gained knowledge about microbes, hand hygiene, self-care, and antibiotic use. Some participants reported behaviour change regarding handwashing and self-care. The contexts that influenced learning were personal, social, physical, active, and external. Mechanisms that interacted with these contexts to trigger learning included: accessible teaching methods, interactive resources, relaxed and effective participant interactions, facilitation of independent thinking and planning, appropriate involvement of supporters, and an inclusive and engaging educator style.
Knowledge gain and changed behaviour intentions were achieved through an engaging, interactive, and focused learning environment, underpinned by a complex and changing combination of interactions. However, further research is needed to understand effective ways of communicating health information in an education context, to understand the impact of education on behaviour change, and to identify ways in which the longer-term retention of learning can be achieved. The research proposes a draft model that can guide effective community health education provision
Predicting diabetic neuropathy risk level using artificial neural network and clinical parameters of subjects with diabetes.
Background: A risk assessment tool has been developed for automated estimation of level of neuropathy based on the clinical characteristics of patients. The smart tool is based on risk factors for diabetic neuropathy which utilises vibration perception threshold (VPT) and a set of clinical variables as potential predictors. Methods: Significant risk factors included: age, height, weight, urine albumin to creatinine ratio (ACR), HbA1c, total cholesterol and duration of diabetes. The continuous scale VPT was recorded using a Neurothesiometer and classified into three categories based on the clinical thresholds in volts (V): low risk (0 to 20.99 V), medium risk (21 to 30.99 V) and high risk (â„ 31 V). Results: The initial study had shown that by just using patient data (n=5088) an accuracy of 54% was achievable. Having established the effectiveness of the âclassicalâ method a special Neural Network based Proportional Odds Model (NNPOM) was developed which provided the highest level of prediction accuracy (>70%) using the simulated patient data (n=4158). Conclusion: In the absence of any assessment devices or trained personnel it is possible to establish with reasonable accuracy a diagnosis of diabetic neuropathy by means of the clinical parameters of the patient alone
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Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction
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