18624 research outputs found
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Influence of selected environmental variables on the germination of Eucalyptus costata
No full textEucalyptus costata is a widespread eucalypt species used in restoration works. It is common to the Mallee, a vegetation type in the semiarid temperate region of southern Australia, characterised by hot summers and mild winters, generally reliable but highly variable rainfall, and nutrient poor soils composed primarily of sands. An understanding of the germination biology of Eucalyptus costata may assist in its successful establishment. We studied the impacts of light, temperature, salinity, moisture and sowing depth on the germination of E. costata in the laboratory and considered the results in a restoration context. Germination was lowest under the highest temperature regime (35°C/25°C), high salt concentrations (>100 mM) and low water potentials (taking 14 MPa of humidity per day to germinate). Sowing depths greater than 2 cm reduced seedling emergence. These results suggest that E. costata’s germination requirements are shallow sowing in low salinity soils during periods of cool or mild wet weather, consistent with winter and spring planting
Comparing machine learning models for thyroid prediction
No full textMachine learning is an effective method for making predictions and diagnosing thyroid disease by analyzing large patient datasets to identify patterns and correlations. It can improve disease diagnosis accuracy and identify patients at risk. The paper focuses on machine learning’s use to analyze and classify thyroid disease using Aadesh Medical College and Hospital data, encompassing demographics of patients, histories of illness, and findings from the lab. This analysis can train machine learning models to predict disease onset and severity and create personalized treatment plans. The dataset we have used includes patient demographics, thyroid profile (T3 and TSH levels), complete blood count (CBC) results, and comorbidities (such as diabetes, hypertension, and cardiovascular disease). Among the approaches we employ are decision tree optimization, frontier boosting, logistical regression estimation, support vector machine, and linear regression (LR). In order to enhance the models’ training, we have also used a number of hyperparameters, including C, “break_ties,” “cache_size,” “class_weight,” “coef0,” “degree,” “gamma,” “kernel,” “max_iter,” “probability,” “random_state,” “shrinking,” “tol,” and "verbose." The decision tree (regressor) model's accuracy of 97.53% produces the best results, and another standout aspect of our work is the achievement of over 96.29% accuracy levels for the linear regression models. Other research studies may have used these methods, but their reported accuracies for these two models do not match ours. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025
Psychological wellbeing and predictors of early help-seeking behaviours of international students of undergraduate nursing programs : a scoping review
No full textBackground: Australian Universities are the principal providers of nurse education for international students. While researchers have recognised the important issue of poor psychological well-being among nursing students, few have explored the factors which impact international nursing students. Aim: This review aimed to map the reported factors impacting psychological health, wellness and early help-seeking behaviours of international students enrolled in Bachelor of Nursing programmes. Methods: A scoping review with a five-stage methodological framework to interpret and synthesise the available literature was utilised. The following databases were searched: CINAHL Complete, PsycINFO, MEDLINE, ERIC and Scopus for studies published in English from 2012 to 2022. Additional sources were also sought through a review of the references of included studies. Four studies met the inclusion criteria. Results: Results revealed that all included studies were from Australia and utilised a quantitative approach. Factors impacting psychological health and wellbeing, as well as help-seeking behaviours included but were not limited to migrating alone, language spoken, marginalisation and the COVID-19 pandemic. Published literature on this topic is limited, with a notable absence from other countries. Conclusion: This review highlights a gap in evidence concerning ways to support international nursing students. Patient or Public Contribution: There were no patient or public contributions in the design, conduct, analysis or preparation of this manuscript. © 2025 The Author(s). Nursing Open published by John Wiley & Sons Ltd
Intimate partner violence and sexual and reproductive health outcomes of women : an Australian population cohort study
No full textObjective: To examine sexual and reproductive health outcomes of women who report intimate partner violence (IPV) and compare these outcomes to women who did not report IPV. Methods: Utilising the Cohort of women born in 1973–1978 and aged 18–23 years when recruited to participate in the National Australian Longitudinal Study on Women's Health, we conducted an analysis in 2022–2023 of the relationships between exposure to IPV and reproductive and sexual health outcomes for this cohort over a decade (1996 to 2006). Logistic regression analyses were undertaken, mixed effects regression models were applied where feasible. Results: The current study indicates exposure to IPV significantly increases the likelihood of forced sex, reporting endometriosis, infertility, miscarriage, pregnancy termination, along with greater odds of infertility, termination, and miscarriage increasing with greater exposure to IPV. Women reporting IPV also report a greater likelihood of STIs such as chlamydia, herpes, and genital warts, in addition to a higher incidence of abnormal Pap tests. Women reporting IPV were also more likely to have a larger number of births, with births occurring earlier than those who did not report IPV. Conclusion: Addressing the global issue of IPV, healthcare organisations must offer robust support, including clear guidelines and protocols for managing IPV and the associated health risks among women. This should extend to providing access to resources and referral systems among those identified as experiencing IPV. Interdisciplinary collaboration remains essential to create a holistic approach to managing IPV and the associated health consequences to promote positive sexual and reproductive health outcomes for women. © 2025 The Author
Stability analysis and prediction of Bimslope failures using numerical modelling and hybrid meta-heuristic models
No full textBimsoil slope (Bimslope) or Soil-Rock-Mixture slopes are complex geological formations made up of geotechnically important ‘blocks’ enclosed in a finer-textured ‘matrix.’ These geomaterials possess heterogeneous properties owing to changes in the block and matrix properties that can arise from natural weathering processes or various anthropogenic activities. The present study focuses on the stability analysis of a Bimslope under varying block properties (orientation and volumetric block proportion), matrix type (Loamy sand, Sandy loam, and Silt loam) and matrix water content. The study indicates that the failure of a Bimslope is not governed by only one physical parameter but rather by a critical combination of block properties, matrix type and matrix water content. When the water content (< 25% saturation) and block content (< 25%) are less, the failure is governed by matrix only. With the increase in block content, the failure is governed by block however under fully saturated (100% saturation) conditions, the failure is solely governed by matrix type irrespective of block properties. In addition to that, the data generated using numerical analysis was used to predict the factor of safety using hybridized ensembled methods considering the XGB model and three other novel meta-heuristic algorithms (MHA): political optimization (POA), the Leopard Seal algorithm (LSA), and the Giant Armadillo Algorithm (GAA). It has been found that among all the models, the GAA-XGB algorithm exhibits better results with R2 and RMSE values as 0.99628, 0.055436 in training and 0.985664, 0.063092 in the testing stage. Furthermore, SHAP-based analysis has been performed to identify the most influential parameters. © The Author(s) 2025
A rapid evidence assessment on prevention and reporting in nurses experiences of workplace violence
No full textObjectives: Workplace violence (WPV) is a profound issue in nursing. It adversely affects the physical and emotional well-being of healthcare professionals, compromises patient safety and care quality and creates a hostile and stressful work environment leading to burnout, decreased job satisfaction and a high turnover rate among nursing staff. Aim: The aim of this paper is to examine prevention and reporting among nurses experiencing WPV. Methods: A quantitative rapid evidence assessment of the literature including a search of ProQuest Central, EBSCO and Web of Science was conducted. Results: This rapid evidence assessment investigates two thematic areas: prevention and reporting. Each discussion section is thematically organised based on the key themes found in the literature. Conclusions: Papers conclude several facilitators and barriers at personal, organisational and societal levels in their experiences of preventing and reporting WPV. Effectively preventing WPV requires organisational support through the implementation of robust training programmes and the presence of visible security staff. Addressing barriers to reporting WPV, such as inadequate training by management and nurses experiencing burnout, necessitates organisational changes. These changes should include improved education on mitigating WPV beyond high-risk setting, as well as fostering closer collaboration between security personnel and nursing staff. © The Author(s) 2025
Will the cryptocurrency exuberance last? An empirical assessment using AI and the ARDL approach
No full textCryptocurrency is the most innovative financial and technological breakthrough of this generation. Investment in cryptocurrency grew from USD 11.18 billion in December 2016 to USD 2.147 trillion in April 2024; however, the rationality of investor exuberance is uncertain. This paper explores stakeholders' perceptions of cryptocurrency using a machine-learning approach based on artificial intelligence (AI). In particular, we employ a lexicon-based emotion-detection sentiment analysis to investigate stakeholder perceptions, using 2.3 million open-source data points. We divide the findings into positive, neutral, and negative stakeholder perception pillars based on factors such as trustworthiness in cryptocurrency, motives, cryptocurrency awareness and knowledge, ownership, socioeconomic characteristics of users, and usage. Our analysis reveals that 51 percent of the stakeholders have a positive perception of cryptocurrency, whereas 40 percent have a neutral perception and 9 percent a negative perception. After identifying the perceptions, we investigate the relationship between cryptocurrency prices and stakeholder perceptions using the autoregressive distributed lag (ARDL) framework with time-series data from August 2017 to July 2023. The long- and short-term results confirm that positive and negative perceptions have statistically significant effects on cryptocurrency prices. Individual investors comprise the largest share of those with a positive perception, as 54 percent have a positive view of cryptocurrency. Institutional investors, however, have the largest share of those with a neutral perception because of the lack of a well-established regulatory framework for cryptocurrency. However, 39 percent of institutional investors hold a positive perception is growing, a sign of a growing trend, as they are among the major investor groups with an interest in investing in crypto. Other stakeholders, such as the government, academia, and other miscellaneous groups, have a negative perception. Our results demonstrate that cryptocurrency has affected social change, social inclusion, and sustainability. Moreover, our findings offer social insights about crypto stakeholders’ perceptions about the design of strategies to promote cryptocurrency and the establishment of a sustainable crypto ecosystem. © 2025 Borsa İstanbul Anonim Şirket
Self-confidence and perceived importance of pre-arrival sport students at an English higher education institution : exploring gender and programme of study differences
No full textTransitional issues into higher education (HE) are evident for all students with many encountering academic and social obstacles. Of particular importance is the need to understand the pre-entry confidence and perceived importance of students and move away from relying on exit metrics to dictate practice. Three hundred and sixty-eight first-year undergraduate sports students at a post-92 United Kingdom (UK) university completed a Pre-Arrival Survey. Findings highlight significant gender and programme of study differences in pre-arrival confidence and perceived importance. Practical implications are proposed to develop practice that supports with the integrating of student populations into HE and the continued learning of skills needed for a successful university education. © 2025 The Author
Enhanced expression of mitochondrial magmas protein in ovarian carcinomas: magmas inhibition facilitates antitumour effects, signifying a novel approach for ovarian cancer treatment
No full textMitochondrial-associated granulocyte macrophage colony-stimulating factor (Magmas) is a unique protein located in the inner membrane of mitochondria, with an active role in scavenging reactive oxygen species (ROS) in cellular systems. Ovarian cancer (OC), one of the deadliest gynaecological cancers, is characterised by genomic instability, affected by ROS production in the tumour microenvironment. This manuscript discusses the role of Magmas and efficacy of its novel small molecule inhibitor BT#9 in OC progression, metastasis, and chemoresistance. Magmas expression levels were significantly elevated in high-grade human OC compared to benign tumours by immunohistochemistry. The inhibition of Magmas by BT#9 enhanced ROS production and reduced mitochondrial membrane permeability, basal respiration, mitochondrial ATP production, and cellular functions, such as the proliferation and migration of OC cell lines in vitro. Oral administration of BT#9 in vivo significantly reduced tumour growth and spread and enhanced the survival of mice without having any effect on the peritoneal organs. These data suggest that Magmas is functionally important for OC growth and spread by affecting ROS levels and that the inhibition of Magmas activity by BT#9 may provide novel clinical benefits for patients with this malignancy. © 2025 by the authors
Discussion : determination of Atterberg Limits using the vane shear test method [Bull. Min. Res. Exp. (2024) 174: 1–10]
No full textThis article presents a discussion of the original research reported in the paper by Kayabalı et al. (2024) (the Authors) that was recently published in the Bulletin of the Mineral Research and Exploration Vol. 174, pp. 1–10. Using liquid limit (LL), plastic limit (PL) and vane shear strength against water content [i.e., su(VST)–w] results obtained for 100 fine-grained soil samples, the Authors performed multiple regression analyses to produce four strength-based correlations for predicting the LL and PL values based on su(VST)–w measurements. The Authors’ dataset primarily consisted of residual soils formed through the weathering of igneous rocks, along with a few lacustrine soil samples, all sourced from the vicinity of Ankara, Türkiye. This discussion article examines the veracity of the Authors’ claims regarding the predictive performance of their proposed correlations when applied to other fine-grained soils. Using a sizable independent database of dissimilar fine-grained soils compiled from the research literature, it is conclusively shown that, contrary to the Authors’ assertions, their proposed correlations generally produce poor LL and PL predictions when employed beyond the calibration soil types. This outcome is not unexpected, since the Authors’ data-driven correlations were deduced based on a specific dataset with limited diversity in terms of soil physico-chemical and mineralogical attributes. The article closes with a discussion of the plausible explanations for the poor applicability of the Authors’ correlations when applied to dissimilar fine-grained soils. © 2025 General Directorate of Mineral Research and Exploration (MTA). All rights reserved