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Role of sex hormones in the regulation of islet hormone release by liver-expressed antimicrobial peptide-2 and acyl-ghrelin
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A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques
Get PDFAbstractAimsThe objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease.MethodsIn this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. This study incorporated five distinct datasets: Heart UCI, Stroke, Heart Statlog, Framingham and Coronary Heart dataset obtained from online sources. For the implementation of the framework, RapidMiner tool was used. The three‐step approach includes pre‐processing of the dataset, applying feature selection method on pre‐processed dataset and then applying classification methods for prediction of results. We addressed missing values by replacing them with mean, and class imbalance was handled using sample bootstrapping. Various machine learning classifiers were applied out of which random forest with AdaBoost dataset using 10‐fold cross‐validation provided the high accuracy.ResultsThe proposed model provides the highest accuracy of 99.48% on Heart Statlog, 93.90% on Heart UCI, 96.25% on Stroke dataset, 86% on Framingham dataset and 78.36% on Coronary heart disease dataset, respectively.ConclusionsIn conclusion, the results of the study have shown remarkable potential of the proposed framework. By handling imbalance and missing values, a significantly accurate framework has been established that could effectively contribute to the prediction of cardiovascular disease at early stages
Dietary inflammation, sleep and mental health in the United Kingdom and Japan: A comparative cross‐sectional study
Get PDFDiet has been repeatedly shown to affect mental and sleep health outcomes. However, it is well known that there are cross‐cultural differences in dietary practices as well as the prevalence of mental and sleep health outcomes. Given that the dietary inflammatory potential of diets has been linked to mental and sleep health outcomes, in the current study we sought to assess the inflammatory status of habitual diets and examine its relationship with mental and sleep health outcomes in both the United Kingdom and Japan. Our aim was to determine if the associations between the dietary inflammation index (DII) score and these health outcomes could elucidate any potential cross‐cultural differences in health. Online survey data was collected from 602 participants (aged 18–40 years) in the United Kingdom (n = 288) and Japan (n = 314). Participants self‐reported their dietary intakes, as well as current mental health and sleep patterns. The DII score was calculated (score range − 2.79 to 3.49) We found that although participants in the United Kingdom reported better overall mental wellbeing, participants in Japan reported less severe depression, anxiety and stress and better subjective sleep quality, less sleep disturbances and daytime dysfunction, despite sleeping shorter, and a better adherence to an anti‐inflammatory diet. Moreover, across the United Kingdom and Japan, adherence to more anti‐inflammatory diets predicted higher levels of subjective sleep quality, fewer sleep disturbances, less use of sleep medicine and less daytime dysfunction. In conclusion, there are several differences between mental and sleep health outcomes in the United Kingdom and Japan, which could be attributable to the inflammatory potential of respective regional diets. Future studies are warranted to examine the mental and sleep health benefits of adhering to anti‐inflammatory traditional Japanese diets in clinical and subclinical cohorts
The role of Music in supporting young children’s holistic learning and wellbeing in the context of Froebel’s Mother songs
No full textResearchers have criticised the pragmatic focus on the value of music education for its contribution to the acquisition of children’s academic skills such as literacy and numeracy development in schools across the international contexts driven by neoliberalism. In the context of Froebel’s Mother Songs, this paper via documentary research focuses on a Froebelian approach to music education in early childhood context to counterpart the neoliberal pragmatism in educational landscapes. The Froebelian perspective brings in implications for early childhood practice, research, and policy making by addressing the important role of music in supporting young children's holistic learning and wellbeing in responding to the neoliberal pressures on children and practitioners in the 21st century
A Survey of Multi-Agent Systems for Smartgrids
No full textThis paper provides a survey of the literature on the application of Multi-agent Systems (MAS) technology for Smartgrids. Smartgrids represent the next generation electric network, as communities are developing self-sufficient and environmentally friendly energy production. As a cyber-physical system, the development of the vision of Smartgrids requires the resolution of major technical problems; this has fed over a decade of research. Due to the stochastic, intermittent nature of renewable energy resources and the heterogeneity of the agents involved in a Smartgrid, demand and supply management, energy trade and control of grid elements constitute great challenges for stable operation. In addition, in order to offer resilience against faults and attacks, Smartgrids should also have restoration, self-recovery and security capabilities. Multi-agent systems (MAS) technology has been a popular approach to deal with these challenges in Smartgrids, due to their ability to support reasoning in a distributed context. This survey reviews the literature concerning the use of MAS models in each of the relevant research areas related to Smartgrids. The survey explores how researchers have utilized agent-based tools and methods to solve the main problems of Smartgrids. The survey also discusses the challenges in the advancement of Smartgrid technology and identifies the open problems for research from the view of multi-agent systems
Monetary-fiscal policies design and financial shocks in currency unions
Get PDFThis paper analyzes the design of monetary and fiscal policies in a currency union by focusing on the capacity to react to symmetric and asymmetric financial shocks. The model is constructed in order to mimic the institutional design adopted for the policy making in the EMU. The paper shows how a currency union set-up like the one adopted by the EMU can easily cope with symmetric financial shocks. However, it shows how in the face of asymmetric shocks more space for fiscal interventions is crucial, especially in more peripheral member countries
A low-code framework for automated test models generation
Get PDF© 2024, [Elsevier]. This is an author produced version of a paper published in SoftwareX uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it. The methodology under the term model-based software engineering (MBSE)gained importance already around 20 years ago, after the publication of theMDA initiative by the OMG. This development methodology continues toevolve, giving rise to recent proposals such as low-code or no-code. Somethingthat has not changed, as recent surveys point out, is the need for powerfultesting approaches and tools for these new methodologies. In MBSE, testinputs are models, so it is key to have frameworks for model generation.However, the main shortcomings of existing model-generation frameworksare their performance limitations and the need for domain-specific knowledge,which seriously hampers their industrial adoption. In this paper, we presentthe Yekta low-code framework that allows to generate models in a simpleway through the application of metaheuristic algorithms
Schools and the Mental Health Crisis:Education on the Frontline
No full textThis chapter introduces the topic of mental health and wellbeing in schools and the overarching theoretical framework for the book. The evolving role of schools from nurturing environments to frontline support for pupils’ mental health and wellbeing is scrutinised in the context of demand, capacity and constraints in a post-pandemic world. Alongside this is a critical consideration of schools’ and education staff’s role, responsibilities and boundaries. The disparate mental health needs of pupils within the school population are discussed, as well as key risk and protective factors to help facilitate timely identification and appropriate support pathways for vulnerable pupils. Key topics and case studies which comprise the remainder of the book are introduced to navigate the reader, and this concludes the chapter