University of the West of Scotland

This article examines the persistence of hierarchy within nursing education and argues that its replication in universities represents a serious contemporary issue for the discipline. Drawing on recent scholarship, sociological theory and critical reflection, it explores how managerialism and audit culture in nursing schools reproduce the hierarchical structures historically embedded in nursing. These structures, inherited from colonial, patriarchal and classed traditions, are sustained through institutional cultures that privilege visibility, control and compliance over intellectual freedom, reflection and care. The argument developed here is not that nurses welcome control, but that the profession has been socialised into systems of authority that constrain both clinical and academic practice. Within nursing faculties, this manifests in the surveillance of work, performance metrics and the erosion of academic autonomy. Such conditions, it is argued, damage not only individual wellbeing but also the legitimacy of nursing as a field of knowledge. Using the lens of Bourdieu's concept of habitus and Skeggs's analysis of value and respectability, this article highlights how hierarchy reproduces classed and gendered inequalities in nursing academia. It calls for a reimagining of leadership that prioritises relational ethics, trust and intellectual freedom. The issue is international in scope: the corporatisation of higher education and the managerial governance of academic labour affect nursing educators across diverse national contexts. Reclaiming autonomy, therefore, is not only a local struggle but a collective, global imperative. The future of nursing education depends on our ability to teach and lead without permission, to create academic spaces grounded in care, critical inquiry and the courage to dissent.

Due to increased awareness and better diagnostic methods the reported prevalence of autism has increased dramatically. Despite this, to our knowledge, no bibliometric analysis on the topic has been conducted. The aim was to analyse the existing literature on autism prevalence in the Web of Science (WoS). A total of 958 articles on the prevalence of autism were retrieved and analysed from various WoS databases between the years 1976 and 2024. Biblioshiny, an R-Studio package, and Excel spreadsheets were used for the statistical processing of the data. Publications on autism prevalence showed an average annual growth rate of 5.73 % with the highest activity period occurring between 2015 and 2021. The most relevant articles were primarily published in the "Journal of Autism and Developmental Disorders," featuring prominent authors such as Fombonne and Durkin. Collaboration among authors has increased. Researched topics include psychiatric disorders, comorbid conditions, and risk factors, while the most frequent keywords reflect populations such as "children" and "adolescents". The United States is the most productive country, followed by the United Kingdom and China, highlighting the global relevance of autism research. This study provides a global perspective, acknowledging both historical and current points of significance in the major research areas and trends concerning the worldwide prevalence of autism. • Bibliometric analysis of global autism research. • Demonstrates trends in worldwide prevalence rates of autism. • Acknowledges historical and current points of significance in major research areas.

Abstract The analysis of means (ANOM) is a statistical method and visualization tool mainly used in examining the equivalence of treatment means in experiments with fixed effects. On the other hand, in real-life situations, one often needs to consider covariates that are linearly related to the main variable. However, in these instances the standard ANOM approach neglects the role of these covariates when assessing group mean equivalence. A new proposal to counteract this lack, ANOM with Covariates (ANOMC), includes a regression estimator that adjusts for one covariate. The use of the ANOMC test is not sufficient; more investigations should be conducted on its utility in experiments where there are multi-covariates involved. Therefore, in this paper, we aim to find out the performance of the ANOMC test with two auxiliary variables with a family of ratio estimators. The proposed tests are ANOMC-MR1, ANOMC-MR2, ANOMC-MR3, ANOMC-MR4, and ANOMC-MR5 and will be evaluated in terms of Type I error rate and the power of the test. In order to evaluate and compare performance, a Monte Carlo simulation was conducted. Results nearly showed that ANOMC-MR2, ANOMC-MR3, ANOMC-MR4, and ANOMC-MR5 tests had higher performance than the ANOMC-MR1 test in most covariate case studied in the experimental simulation. Additionally, the proposed ANOMC tests are implemented on biomedical and mechanical engineering datasets to show practicality to the practitioners.

Understanding bipolar information is crucial as it enables individuals to make informed decisions that consider both extremes of a spectrum, leading to more balanced and effective outcomes. Interval-valued bipolar fuzzy set (IVBFS) has already been introduced in the literature as a great decision-making tool that can capture interval-valued bipolar information to properly address uncertainty. In this article, we introduce a hybrid of Interval-valued bipolar fuzzy set (IVBFS) and bipolar hypersoft sets (BHSS) called interval-valued bipolar fuzzy hypersoft set [Formula: see text], which merges the capabilities of IVBFS and BHSS. The rationale behind the design of the presented data structure is to manipulate and process information in decision-making scenarios when the data is bipolar, has multiple attributes that need to be addressed up to a sub-attributive level to get a proper representation of the data provided, and needs to be presented in the form of intervals. In [Formula: see text], two hyper soft sets (HSSs) are used, one providing positive interval-valued membership information and the other providing negative interval-valued membership information. We outline the essential features and basic operations of [Formula: see text] in this paper, examining its commutative, associative, distributive, and De Morgan laws to ensure a comprehensive analysis. To demonstrate the significance of [Formula: see text], we develop a preferential decision support algorithm for selecting the best alternative in e-learning, such as identifying the most suitable instructional method, which can effectively be formulated as a Multi-Attribute Decision-Making (MADM) problem. This approach allows for the systematic evaluation of various alternatives based on multiple parameters and sub-parameters, enabling a rational and well-informed decision. This algorithm helps select the best alternative from a given set of options, leveraging the versatile nature of [Formula: see text]. The presented study conducts both computation-based and structural comparisons to evaluate the adaptability and reliability of the proposed framework.

Pain associated with claudication in peripheral arterial disease (PAD) is a key barrier to physical activity, limiting walking ability and impacting quality of life. Transcutaneous Electrical Nerve Stimulation (TENS) may offer non-pharmacological pain relief and has shown potential to improve walking performance. However, little is known about patients' experiences using TENS in everyday life. To explore the lived experience of using TENS at home among individuals with PAD and claudication. Six participants with PAD and claudication received training in the use of a TENS device for home use during daily walking activities. After four weeks, experiences were explored through a focus group and an individual interview. Data were analysed using thematic analysis. Four themes emerged: Pain, Expectations, Usability, and Physical and social functioning. While some participants reported reduced pain and improved walking ability, others expressed disappointment when TENS did not fully meet their expectations. Variability in use and perceived benefit was influenced by prior knowledge, usability challenges, and personal preferences. TENS may support self-management of claudication pain and enhance physical activity in some individuals with PAD. However, managing patient expectations and addressing device usability are essential to optimise outcomes. Healthcare practitioners may play a key role in supporting education, tailoring advice, and evaluating the appropriateness of TENS as part of individualised care plans.

The aim of the study was to identify central determinants of using a 3-year longitudinal evaluation of left ventricular (LV) morphological and functional (global, tissue-Doppler and strain) outcome measures obtained at rest and during both submaximal and maximal exercise in a group of highly trained male youth soccer players (SP) and recreationally active male participants (CON). Once a year for 3years, measurements were obtained in both the SP and CON groups (12.0±0.3 and 11.7±0.2years of age, respectively, at the onset of the study). Cardiac ultrasound measures were used to identify LV morphological indices at rest and functional parameters during submaximal and maximal exercise. Training status (P<0.0001) emerged as the only significant independent predictor of , when considering LV morphological variables. At maximal exercise, early diastolic filling (E) was a significant (P=0.001) predictor of , irrespective of the influence of training status. Training status emerged as the significant predictor of across all models that were developed in this study. Minimal LV structural and functional adaptations at both rest and exercise influence , beyond the impact of training status alone. The broader implication of these findings is that the influence of LV cardiac adaptations on over time is mediated by the stimulus of training; this association occurs independently from the impact of growth and maturation on .

Abstract A novel technique is developed for nonlinear optimization problem which is convex, separable and having multiple objective functions. In the development of the model all the objectives and the constraints of the multi objective model are linearly approximated over suitable intervals. The linear approximations are then aggregated to account for the original problem. The developed technique has been utilized for portfolio optimization problem. Firstly, the minimum variance model has been formulated and solved with machine leaning techniques. Secondly, the risk aversion model has been formulated and solved. The results obtained are combined into a multi objective framework of convex separable programming problem. All the three problems have been solved with the help of the XGBoost, neural network, and decision forest regression models. The renowned Python machine libraries of scikit-learn and keras have been utilized. The results identified portfolios that can return more financial benefits to the investors while investing in the capital market. The results of the proposed MOCSP approach are 22.5% improved in case of risk aversion model. Additionally, 17% improvement has been recorded in case of the minimum risk model. The MAE and RMSE for both XGBoost and decision forest regression have a frail value 0.0001. MAE and RMSE for the neural network regression have been recorded 1% and 2%, respectively. Both Accuracy and F1 score for XGBoost are 91%, for neural network regression are 98%, and for decision forest are 92%, respectively.

Peri-urbanization in rapidly growing cities of the Global South is increasingly driven not only by demographic growth but by escalating inner-city land and housing prices that push households and developers toward peripheral zones. Bangalore exemplifies this transition, where housing affordability pressures, speculative real estate investment, and weak land governance interact to transform agricultural landscapes into fragmented built-up clusters. Using satellite imagery (1991–2024), census data, and GIS-based land-use classification, this study quantifies peri-urban expansion across eight clusters in the Bangalore Metropolitan Region. The results show rapid built-up growth, agricultural land decline, and increasing spatial fragmentation, reflecting processes of extended urbanization beyond formal city boundaries. These transformations produce environmental stress, infrastructure deficits, and socio-spatial inequalities. The paper situates Bangalore within planetary urbanization debates and argues that peri-urban sustainability depends on land market regulation, spatial planning capacity, and data-driven governance.