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Machine Learning-Based Stacking Ensemble Model for Prediction of Heart Disease with Explainable AI and K-Fold Cross-Validation:A Symmetric Approach
Get PDFOne of the most complex and prevalent diseases is heart disease (HD). It is among the main causes of death around the globe. With changes in lifestyles and the environment, its prevalence is rising rapidly. The prediction of the disease in its early stages is crucial, as delays in diagnosis can cause serious complications and even death. Machine learning (ML) can be effective in this regard. Many researchers have used different techniques for the efficient detection of the disease and to overcome the drawbacks of existing models. Several ensemble models have also been applied. We proposed a stacking ensemble model named NCDG, which uses Naive Bayes, Categorical Boosting, and Decision Tree as base learners, with Gradient Boosting serving as the meta-learner classifier. We performed preprocessing using a factorization method to convert string columns into integers. We employ the Synthetic Minority Oversampling TEchnique (SMOTE) and BorderLineSMOTE balancing techniques to address the issue of data class imbalance. Additionally, we implemented hard and soft voting using voting classifier and compared the results with the proposed stacking model. For the Artificial Intelligence-based eXplainability of our proposed NCDG model, we use the SHapley Additive exPlanations (SHAP) technique. The outcomes show that our suggested stacking model, NCDG, performs better than the benchmark existing techniques. The experimental results of our proposed stacking model achieved the highest accuracy, F1-Score, precision and recall of 0.91, 0.91, 0.91 and 0.91, respectively, and an execution time of 653 s. Moreover, we have also utilized K-Fold Cross-Validation method to validate our predicted results. It is worth mentioning that our prediction results and their validation strongly coincide with each other which proves our approach to be symmetric
Construction and initial validation of an academic impostor syndrome measure
Get PDFImpostor syndrome has been identified as a growing problem in professional and academic settings. It has been associated with diminished confidence and inhibited performance. In the context of education, it has been reported as maladaptive to enrolment, retention, integration, wellbeing, and academic performance. One inhibiting factor is the lack of validated measures specific to education. Hence, the primary aim and original contribution of this study is the construction and initial validation of such a measure. This construction process was initially informed by a trawl of the literature on general impostor syndrome, with ten domains emerging from the reviews to provide content validity. Items were constructed in consultation with students as end users and academics from national and international symposia and seminars. The studies were carried out at two UK higher education institutions, with N = 339 undergraduates. Through iterative processes including item analysis, principal component analysis, and factor analyses, ten items were selected from a pool of thirty. These covered the ten literature domains and associated with good factor loadings (> 0.45) and sound model fit indicators. Invariance testing of both student groups demonstrated equivalence of factor structure and factor loadings. To enhance the measure’s validity, the Five-Factor Model of Personality, Self-esteem, and Self-efficacy were included. The moderate correlations of these factors with academic impostor syndrome in expected directions may respectively signpost the approach and avoidance behaviours that counter or nurture the problem. The new measure is commended as a potentially useful tool for research and practice.</p
Mechanical stimulation in plants:Molecular insights, morphological adaptations, and agricultural applications in monocots
Get PDFMechanical stimulation, including wind exposure, is a common environmental factor for plants and can significantly impact plant phenotype, development, and growth. Most responses to external mechanical stimulation are defined by the term thigmomorphogenesis. While these morphogenetic changes in growth and development may not be immediately apparent, their end-results can be substantial. Although mostly studied in dicotyledonous plants, recently monocot grasses, particularly cereal crops, have received more attention. This review summarizes current knowledge on mechanical stimulation in plants, particularly focusing on the molecular, physiological, and phenological responses in cereals, and explores practical applications to sustainably improve the resilience of agricultural crops
How Foreign and Domestic Ownership Influenced Risk-Taking in GCC Banks
Get PDFThis study investigates the relationship between ownership structure (foreign and domestic) and bank risk-taking over the period 2014–2022. The analysis includes 66 banks operating in the GCC, divided into 44 domestically owned, and 22 foreign-owned banks. The research examines the relationship across two distinct periods: the pre-pandemic and the COVID-19 pandemic era, using the two-stage least squares (2SLS) method, and panel data techniques for robust analysis. The findings reveal that, in both periods, foreign-owned banks exhibited lower credit risk, greater cost efficiency, and less risk-taking compared to domestic counterparts. Domestic banks, while maintaining profitability, relied heavily on capital absorbency, which resulted in elevated credit risk and operational inefficiencies. These inefficiencies, observed among domestic banks, stem from inadequate monitoring of borrowers’ information and the occurrence of moral hazard. Foreign banks played a crucial role in supporting banking sector stability, as their presence enhanced the GDP growth. The results are in line with the “global advantage hypothesis”
Auroral and Non-Auroral H3<sup>+</sup> Ion Winds at Uranus With Keck-NIRSPEC and IRTF-iSHELL
Get PDFTo date, no investigation has documented ionospheric flows at Uranus. Previous investigations of Jupiter and Saturn have demonstrated that mapping ion winds can be used to understand ionospheric currents and how these connect to magnetosphere-ionosphere coupling. We present a study of Uranus's near infrared emissions (NIR) using data from the Keck II Telescope's Near InfraRed SPECtrograph (NIRSPEC) and the InfraRed Telescope Facility's iSHELL spectrograph. H3+ emission lines were used to derive dawn-to-dusk intensity, ionospheric temperatures and ion densities to identify auroral emissions, with their Doppler shifts used to measure ion velocities. We confirm the presence of the southern NIR aurora in 2016, driven by elevated H3+ column densities up to 6.0 × 1016 m−2. While no auroral emissions were detected in 2014, we find a 14%–20% super rotation across the planet's disk in 2014 and a 7%–18% super rotation in 2016.<br/
Leadership and Sustainability in Community Development:Social Entrepreneurship in North Wales
Get PDFThe aim of the paper is to analyze leadership and sustainability in community enterprises. The geographical location for our study is Blaenau Ffestiniog in North Wales which is home to several innovative and successful community enterprises. An interpretivist study employing a thematic analysis has been undertaken. Interviews were held with individuals who hold leadership, management, and volunteer roles. A participative dissemination event was also conducted with community members to discuss and build upon the interview-based findings. The data collected was coded using the six-step approach to thematic analysis. The results of the analysis revealed that the concept of leadership was difficult to define in a community enterprise situation. The survival of these enterprises often relies on the entrepreneurial and collective leadership skills of key individuals. The implications for socio-economic policy are support for the acquisition and development of collective leadership skills which could lead to enhanced sustainability of community enterprises.</p
Computational Design and Evaluation of Peptides to Target SARS-CoV-2 Spike-ACE2 Interaction
Get PDFThe receptor-binding domain (RBD) of SARS-CoV-2 spike protein is responsible for the recognition of the Angiotensin-Converting Enzyme 2 (ACE2) receptor in human cells and, thus, plays a critical role in viral infection. The therapeutic value of targeting this interaction has been proven by a sizable body of research investigating antibodies, small proteins, aptamers, and peptides. This study presents a novel peptide that impinges the interaction between RBD and ACE2. Starting from a very large pool of structurally designed peptides extracted from our database, PepI-Covid19, a diverse set of peptides were studied using molecular dynamics simulations. Ten of the most promising were chemically synthesized and validated both in vitro and in a cell-based assay. Our results indicate that one of the peptides (PEP10) exhibited the highest disruption of the RBD/ACE2 complex, effectively blocking the binding of two molecules and consequently inhibiting the SARS-CoV-2 spike-mediated cell entry of viruses pseudotyped with the spike of the D614G, Delta, and Omicron variants. PEP10 can potentially serve as a scaffold that can be further optimized for improved affinity and efficacy
Harmonizing soil carbon simulation models, emission factors and direct measurements used in LCA of agricultural systems
Get PDFCONTEXTThe increasing demand for animal products, coupled with the need to reduce greenhouse gas (GHG) emissions from livestock production, highlights the urgency for effective mitigation strategies for livestock systems, including the cropping systems. Soil organic carbon (SOC) sequestration, a crucial approach for reducing atmospheric GHG concentrations, is often underrepresented in Life Cycle Assessments (LCA) of agricultural systems, largely due to methodological challenges in accurately accounting for soil carbon dynamics.OBJECTIVEThe objective of this study was to evaluate soil carbon simulation models, emission factors and direct measurements used in LCA, with the aim of developing a harmonized approach for including soil carbon change in agricultural LCAs. The goals were to: i) assess soil carbon simulation models, emissions factors and direct measurements used in LCAs of agricultural systems; ii) evaluate the strengths and weaknesses of these models; iii) provide recommendations for LCA practitioners; and iv) identify areas for future methodological improvements.METHODSA systematic review of soil carbon simulation models, emission factors and direct measurements used in LCAs of agricultural systems was conducted, obtaining 263 relevant articles from an initial pool of 29,151. In addition to direct measurements, fifteen soil carbon simulation models and three methods based on emission factors were identified and categorized into three tiers based on complexity and data requirements. A modified Delphi participatory process was used to evaluate each method against established criteria through expert workshops.RESULTS AND CONCLUSIONSThe results showed an inverse relationship between applicability and accuracy of methods, making the choice of methodology critical to achieving high-quality LCA results. Recommendations emphasize selecting methods based on objectives and data availability, while being aware of the effect of the initial soil carbon level and the assessment time period when using soil carbon simulation models. In addition, this study identified current methodological challenges in assessing soil C dynamics in LCA of agricultural systems.SIGNIFICANCEThis research provides a foundation for improving LCA practices and supports better decision-making in mitigating climate impacts of agricultural systems
A systematic review of behaviour change techniques employed in interventions aimed to change physical activity behaviour in autistic individuals
Get PDFBackgroundAutistic individuals experience disproportionately poor physical and mental health outcomes, many of which can be mitigated through lifestyle modification such as increasing levels of physical activity. While behaviour change interventions hold promise in promoting physical activity, their effectiveness in autistic populations remains underexplored, particularly in relation to theoretical foundations and intervention content.ObjectiveTo systematically review behaviour change techniques applied to physical activity interventions for autistic individuals, evaluate application of psychological theory, and adaptations made for autism.MethodsA systematic search of five databases was conducted in accordance with PRISMA guidelines. Eligible studies were intervention-based, targeted physical activity behaviour as a primary outcome, and included autistic participants. Data were narratively synthesised, and intervention components were coded using the Behaviour Change Taxonomy (BCTTv1). Intervention efficacy was evaluated using a ‘promise ratio’ and statistical comparisons were conducted to assess associations between intervention promise, Behaviour Change Techniques, theory use, and autism-specific adaptations.ResultsThirty-three studies were included (n=26 child-focused; n=7 adult-focused). Eleven studies reported explicit use of behaviour change theory, with no significant association between theory use and intervention promise. A total of 266 BCTs were coded; most frequently used was instruction on how to perform the behaviour, though not associated with efficacy. In adults, promising techniques included goal setting and behavioural rehearsal; in children, demonstration and reinforcement were effective. Autism-specific adaptations were significantly associated with intervention promise and included sensory considerations and structured environments.ConclusionsTheory-informed, autism-adapted interventions show potential for promoting physical activity in autistic populations. Future research should prioritise high-quality designs, meaningful involvement of autistic individuals, and rigorous application of behavioural theory.<br/
Connections Between the Quiet Corona Magnetic Topology and the Velocity Field of Propagating Disturbances
Get PDFThe magnetic field of the low corona above quiet Sun regions is extremely challenging to observe directly, and the topology is difficult to discern from extreme ultraviolet (EUV) image data due to the lack of distinct loops that are present in, for example, active regions. We aim to show that the velocity field of faint propagating disturbances (PD) observed on-disk in the quiet corona can be interpreted in terms of the underlying magnetic topology. The PD are observed in Atmospheric Imaging Assembly/Solar Dynamics Observatory (AIA/SDO) time series in three channels: 304, 171, and 193 Å corresponding to the high chromosphere, transition region/low corona, and the corona, respectively. An established Time-Normalised Optical Flow method enhances the PD and applies a Lucas–Kanade algorithm to gain their velocity field. From the velocity field, we identify the source and sink locations of the PDs, and compare these locations between channels and with the underlying photospheric network. Source regions tend to be located above the photospheric network, and sink regions with the internetwork. Sink regions in the internetwork suggest either that closed field can be concentrated rather than evenly distributed in the internetwork, or that fieldlines opening into the corona can sometimes be concentrated above internetwork regions. We find regions of almost exact alignment between channels, and other regions where similar-shaped structures are offset by a few pixels between channels. These are readily interpreted as vertical or non-vertical alignment of the magnetic field relative to the observer viewing from above. Regions of isolated source regions in the cold (304 Å) or hotter (171 and 193 Å) channels can be interpreted in terms of the magnetic topology, but support for this is weaker. These results offer support for the future use of PD velocity fields as a coronal constraint on magnetic extrapolation models.</p