18624 research outputs found
Sort by
Enablers and potential barriers of female participation in tertiary education in Afghanistan : an analysis of contemporary issues
Get PDFAfghanistan’s education system has been devasted by more than four decades of sustained conflict (United Nations International Children’s Emergency Fund [UNICEF], 2019). Even completing primary school remains a distant dream for many children, especially those in rural areas, particularly females. According to UNICEF (2019), a key challenge for Afghanistan is that an estimated two-thirds of the female population do not attend school. Furthermore, as personal security in the country deteriorates, female enrolment in tertiary education is also declining (Pherali & Sahar, 2018). In addition, many families have fled their villages because of the ongoing unrest and are concentrated in cities where they live in poverty and have little access to educational services (Baiza, Nevertheless, according to the United Nations Educational, Scientific and Cultural Organization (2009), education is the most potent weapon for positive change in the world. Moreover, the increased participation of women in tertiary education improves economic growth and stability (McLean, 2020; UNICEF, 2011). This study examines problems concerning women’s access to tertiary education in Afghanistan and potential solutions to these problems. A mixed methods experimental sequential research design was used for this study (Creswell & Plano Clark, 2011). This study used an online survey of 120 undergraduate students and explored the lived experiences of 10 female undergraduates and 10 graduates through an online individual interview. Further, 10 lecturers were recruited from five disciplines and three universities through individual online interviews. Ethics approval was obtained to engage with only undergraduates/graduates currently living in Australia, because of safety considerations. As per Leighton et al. (2021), snowball sampling was also utilised for surveys and semi-structured interviews. This study aimed to understand the participants’ perspectives concerning the factors influencing their decision to pursue tertiary education and the obstacles preventing Afghan women from participating in tertiary education in Afghanistan. The findings from this study revealed that while women's education is universally acknowledged as essential for economic, cultural, social, and political development, its realisation in Afghanistan is impeded by entrenched cultural, societal, religious, and political factors. The study highlighted the benefits of tertiary education for Afghan women, including increased employment opportunities, higher income potential, and contributions to national development and community advancement. However, these are overshadowed by substantial challenges such as government policy and practices, university system and infrastructure and family and culture restrictions. As measures to increase female enrollment in tertiary education in Afghanistan, this study recommends addressing cultural and social norms through awareness campaigns challenging traditional gender roles. It also recommends engaging influential figures such as fathers, religious leaders, and community elders in advocating for women's education. It is imperative to provide women-friendly campuses, implement security measures, and provide gender-segregated classrooms (when culturally necessary) to ensure a safe and inclusive learning environment. Financial support can alleviate economic burdens, including scholarships and partnerships with international organisations.Doctor of Philosophy, Partia
Disentangling ecosystem necromass dynamics for biodiversity conservation
Get PDFGlobal environmental change has redistributed earth’s biomass and the inputs and dynamics of basal detrital resources in ecosystems, contributing to the decline of biodiversity. Yet efforts to manage detrital necromass for biodiversity conservation are often overlooked or consider only singular resource types for focal species groups. We argue there is a significant opportunity to broaden our perspective of the spatiotemporal complexity among multiple necromass types for innovative biodiversity conservation. Here, we introduce an ecosystem-scale perspective to disentangling the spatial and temporal characteristics of multiple and distinct forms of necromass and their associated biota. We show that terrestrial and aquatic ecosystems contain a diversity of necromass types, each with contrasting temporal frequencies and magnitudes, and spatial density and configurations. By conceptualising an ecosystem in this way, we demonstrate that specific necromass dynamics can be identified and targeted for management that benefits the unique spatiotemporal requirements of dependent decomposer organisms and their critical role in ecosystem biomass conversion and nutrient recycling. We encourage conservation practitioners to think about necromass quantity, timing of inputs, spatial dynamics, and to engage with researchers to deepen our knowledge of how necromass might be manipulated to exploit the distinct attributes of different necromass types to help meet biodiversity conservation goals. © The Author(s) 2024
Comparing the effects of cerebellar and prefrontal anodal transcranial direct current stimulation concurrent with postural training on balance and fatigue in patients with multiple sclerosis: a double-blind, randomized, sham-controlled trial
No full textFatigue and balance disorders are common challenges experienced by Multiple Sclerosis (MS) individuals. The purpose of this study was to compare the concurrent effects of cerebellar and prefrontal anodal trans-cranial direct current stimulation (a-tDCS) with postural training on balance and fatigue in MS patients. 51 patients were evaluated to randomly allocation to a-tDCS over cerebellum, a-tDCS over dorsolateral prefrontal cortex (DLPFC) and sham group. 46 individuals (n = 16 in experimental groups and n = 14 in control group) followed treatment. All the groups received 10 sessions of postural training. The experimental groups underwent a-tDCS with a current of 1.5 mA for a period of 20 min. While, in the sham group, tDCS was only activated for 30 s and then turned off. The treatment included 10 sessions for four weeks. Before and after intervention, fatigue and balance were assessed using Fatigue Severity Scale (FSS), Timed Up and Go (TUG) test and Berg Balance Score (BBS), respectively. There was found a significant reduction in fatigue in the group receiving a-tDCS over the prefrontal cortex with postural training compared to the other two groups (P 0.001). The results demonstrated that a-tDCS enhances the effects of postural training on balance and fatigue in MS patients. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024
Applications of soft computing methods in backbreak assessment in surface mines : a comprehensive review
Get PDFGeo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to the unintended fracturing of rock beyond the desired blast perimeter, can significantly impact project timelines and costs. This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations, specifically focusing on backbreak prediction. The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects. © 2024 Tech Science Press. All rights reserved
A systematic review of the wellbeing benefits of being active through leisure and fitness centres
Get PDFThe aim of this systematic review was to provide an overview of the scientific evidence for psychosocial wellbeing benefits for individuals who are active through settings like leisure centres, gymnasiums or swimming pools. The level of physical activity required to achieve wellbeing outcomes through centre usage was a focal point. Nine electronic databases (AUSPORT, SPORTDiscus, EMBASE, MEDLINE, CINAHL complete, PsycINFO, Web of Science, PubMed, Scopus) were systematically searched to identify relevant literature, including all articles published in English from January 2011 to December 2021. A total of 1667 manuscripts were identified of which 31 articles were included in this review. Mental health was the most investigated psychological outcome, followed by stress reduction and relaxation; bonding with family/friends was the most frequently studied social outcome. Regular physical activity at leisure/fitness centres may be associated with increased social and psychological wellbeing. Participation in group programmes seems to be superior to individual activities in achieving health benefits due to its social nature. Findings from this review confirm that outcomes of being active through leisure/fitness centres go beyond physical benefits. However, scientific evidence is limited and more longitudinal studies with larger samples, and a focus on the dose–response relationship issue are recommended. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
Absolute value inequality svm for the PU learning problem
Get PDFPositive and unlabeled learning (PU learning) is a significant binary classification task in machine learning; it focuses on training accurate classifiers using positive data and unlabeled data. Most of the works in this area are based on a two-step strategy: the first step is to identify reliable negative examples from unlabeled examples, and the second step is to construct the classifiers based on the positive examples and the identified reliable negative examples using supervised learning methods. However, these methods always underutilize the remaining unlabeled data, which limits the performance of PU learning. Furthermore, many methods require the iterative solution of the formulated quadratic programming problems to obtain the final classifier, resulting in a large computational cost. In this paper, we propose a new method called the absolute value inequality support vector machine, which applies the concept of eccentricity to select reliable negative examples from unlabeled data and then constructs a classifier based on the positive examples, the selected negative examples, and the remaining unlabeled data. In addition, we apply a hyperparameter optimization technique to automatically search and select the optimal parameter values in the proposed algorithm. Numerical experimental results on ten real-world datasets demonstrate that our method is better than the other three benchmark algorithms. © 2024 by the authors
Using email interviews to reflect on women’s careers at a regional university
Get PDFThe article investigates asynchronous narrative research via email as a flexible and agentic method of collecting data that may empower female participants. A case study was used that focused on the challenges for academic and professional women at an Australian regional university. Twenty-one women responded by email to a range of questions about working conditions and career progression. The data demonstrated that participants found this methodology empowering, encouraging agentic behaviour as they could respond at a time that suited them and in as much detail as they desired. They could also leave their narratives and return to them after some reflection. While lacking the non-verbal markers that often add to meanings in face-to-face interviews, the participants’ writing gave voice and form to their lived experience that has been missing from academic literature. This research method may be vital in the continuing COVID-19 environment where it can be difficult to access geographically dispersed participants. © The Author(s) 2023
Impact of hillslope agriculture on soil compaction and seasonal water dynamics in a temperate vineyard
Get PDFMajor losses of agricultural production and soils are caused by erosion, which is especially pronounced on hillslopes due to specific hydrological processes and heterogeneity. Therefore, the aim of this study was to assess the impact of agricultural management on the compaction, infiltration, and seasonal water content dynamics of the hillslope. Measurements were made at the hilltop and footslope, i.e., soil water content and potential were measured using sensors, wick lysimeters were used to quantify water flux, while a mini-disk infiltrometer was used to measure the infiltration rate and calculate the unsaturated hydraulic conductivity (K_unsat). Soil texture showed differences between hillslope positions, i.e., at the hilltop after 50 cm depth, the soil is classified as silty clay loam, and from 75 cm onward, the soil is silty clay, while at the footslope, the soil is silt loam even at the deeper depths. The results show a higher K_unsat at the footslope as well as higher average water volumes collected in wick lysimeters compared to the hilltop. Average water volumes showed a statistically significant difference at p < 0.01 between the hilltop and the footslope. The soil water content and water potential sensors showed higher values at the footslope at all depths, i.e., 8.0% at 15 cm, 8.4% at 30 cm, and 27.3% at 45 cm. The results show that, even though the vineyard is located in a relatively small area, soil heterogeneity is present, affecting the water flow along the hillslope. This suggests the importance of observing water movement in the soil, especially today when facing extreme weather (e.g., short-term high-intensity rainfall events) in order to protect soil and water resources. © 2024 by the authors
Caring self-efficacy of personal care attendants from english-speaking and non-english-speaking countries working in australian residential aged care settings
Get PDFObjectives: This study compared the caring self-efficacy between personal care attendants (PCAs) from English-speaking and non-English-speaking countries, controlling for potential sociodemographic and work-related covariates. PCAs’ perceptions of their caring self-efficacy were further explored. Methods: An independent samples t-test was used to determine the mean difference in the caring self-efficacy score between the two groups. A multivariate analysis was conducted to adjust for covariates. Thematic analysis was conducted on open-ended responses. Results: The results showed that caring self-efficacy was significantly influenced by whether participants primarily spoke English at home rather than where they were born. Younger age and everyday discrimination experiences were negatively associated with caring self-efficacy. Both groups perceived that inadequate resources and experiencing bullying and discrimination reduced their caring self-efficacy. Discussion: Access to organisational resources and training opportunities and addressing workplace bullying and discrimination against PCAs, particularly younger PCAs and those from non-English-speaking backgrounds, could improve their caring self-efficacy. © The Author(s) 2023
Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm
Get PDFComplement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy. © 2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd