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Exploring the development of early career nurses: insights 4 years after graduation
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To explore how Early Career Nurses perceive their preparedness for nursing practice, the teaching and learning experiences, and the role of professional experience placements on their professional development.
Design
A qualitative study using a hermeneutic phenomenological approach.
Method
The study involved 25 Early Career Nurses who participated in follow-up interviews 4 years post-graduation in Australia between 2022 and 2024. Data were collected through semi-structured interviews and analysed using Thematic Analysis.
Results
Three key themes emerged: gaps in preparedness, the power of being embedded and too many balls to juggle. Participants indicated a mixed sense of preparedness with significant gaps in clinical skills. They emphasised the critical role of professional experience placements and mentorship to bridge the gap between theoretical knowledge and practical application. Placements and mentorship opportunities were considered essential to develop confidence and competence for effective nursing practice.
Conclusion
The study highlights the necessity for nursing curricula to address significant gaps in clinical skills, particularly in surgical and emergency nursing. By incorporating more simulation-based learning, interprofessional education and robust mentorship programmes, nursing education can better prepare graduates for the realities of clinical practice. These enhancements will help ease the transition from academic training to clinical practice, reducing reality shock and fostering a more confident, competent and resilient nursing workforce
Global transcriptome changes during growth of a novel Penicillium coffeae isolate on the wheat stripe rust fungus, Puccinia striiformis f. sp. tritici
No full textWheat stripe rust caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is currently the most destructive disease of wheat. The major control methods which include the deployment of resistant wheat cultivars and application of chemical fungicides are losing efficiency as the fungus evolves. Natural antagonists of Pst may be an avenue for alternative and environmentally sustainable control of the disease in the field. Here we describe a novel fungus found growing on Pst pustules. We identified the fungus as a novel isolate of the plant endophyte Penicillium coffeae. We present a high-quality reference genome and a comparative transcriptomic analysis used to investigate how the fungus deploys its genes during growth amongst Pst spores. The gene content of the P. coffeae ANU01 genome is suggestive of a generalist that makes use of diverse substrates. An abundance of genes related to lipid, amino acid and carbohydrate metabolism indicate that P. coffeae ANU01 has evolved the ability to exploit nutrient stores in Pst urediniospores. P. coffeae ANU01 deploys a number of biosynthetic gene clusters during growth on Pst spores, potentially to inhibit urediniospores germination and halt defence responses. A number of genes encoding carbohydrate active enzymes are also highly upregulated, suggesting targeting and degradation of Pst urediniospores structures. Alongside carbohydrates, P. coffeae ANU01 appears to target spore lipids as a nutrient source, secreting several highly upregulated lipases. Our findings broaden the understanding of growth associated with rust spores as an evolutionary strategy and provide insight into the genes potentially required for this process
Learning from life, Enabling artificial intelligence: Scientific historical insights from the Nobel Prize in physics
Get PDFThe 2024 Nobel Prize in Physics recognized John Hopfield and Geoffrey Hinton for their transformative contributions to artificial neural networks, sparking widespread debate within the academic community. Why was a physics prize awarded to researchers in artificial intelligence (AI)? How have their achievements influenced the historical trajectory of AI? This article adopts a history-of science perspective to trace the evolution of neural network technologies, from Hopfield networks to the Boltzmann machine. It examines the interdisciplinary nexus between physics and AI, highlighting its broader implications for future scientific advancements
A review of airborne observations for space debris re-entry break-up and dispersion measurements
No full textAirborne observations provide opportunities to collect rare and unique data from space debris re-entries. To date, six different observations of space debris have been undertaken, collecting spectral and spatial data to help understand debris break-up and dispersion. The data is important to help validate computational space debris break-up models. A particular focus to date has been on the spectral data collection, and results have helped understand the sequence of debris break-up by identifying individual elements as they are released in time. Due to the resourcing required, a limited amount of work has been undertaken on trajectory analysis for dispersion measurements. A noted downside of the observations to date is that all the resourcing has gone into the observations and data collection, and then relied on self-motivated researchers to undertake detailed analysis. The ever-increasing amount of space debris drives the need for a better understanding of space debris break-up and dispersion and there is a clear need for invaluable flight data for further model validation
A very young τ-Herculid meteor cluster observed during a 2022 shower outburst
Get PDFContext. To date, only a few meteor clusters have been instrumentally recorded. This means that every new detection is an important contribution to the understanding of these phenomena, which are thought to be evidence of the meteoroid fragmentation in the Solar System.
Aims. On May 31 2022 at 6:48:55 UT, a cluster consisting of 52 meteors was detected within 8.5 seconds during a predicted outburst of the τ-Herculid meteor shower. The aim of this paper is to reconstruct the atmospheric trajectories of the meteors and use the collected information to deduce the origin of the cluster.
Methods. The meteors were recorded by two video cameras during an airborne campaign. Due to only the single station observation, their trajectories were estimated under the assumption that they belonged to the meteor shower. The mutual positions of the fragments, together with their photometric masses, were used to model the processes leading to the formation of the cluster.
Results. The physical properties of the cluster meteors are very similar to the properties of the τ-Herculids. This finding confirms the assumption of the shower membership used for the computation of atmospheric trajectories. This is the third cluster that we have studied in detail, but the first one in which we do not see the mass separation of the particles. The cluster is probably less than 2.5 days old, which is too short for such a complete mass separation. Such an age would imply disintegration due to thermal stress. However, we cannot rule out an age of only a few hours, which would allow for other fragmentation mechanisms
Sexual health nursing
No full textSexual health nurses are employed to work in a range of practice settings and work with diverse population groups. Sexual and reproductive health care is considered a human right and is fundamental to positive well-being. The nurses role in sexual and reproductive health varies between settings within and across different jurisdictons. Work settings include dedicated sexual health clinics, family planning services, community health centres, women’s health services, correctional services, general practices and tertiary education settings. In some juristictions, nurses also provide care in publicly funded sexual health clinics aimed at providing services to specific priority population groups to increase their access to services and reduce the prevalence of adverse sexual and reproductive health outcomes including sexually transmitted infections and unplanned pregnancy
The Last of Us, Interactive Media and the Virtual Apocalypse
No full textFew figures in interactive media are as consistently used as the zombie. Not only do they can be aesthetically horrifying, but they are also remarkably ubiquitous antagonists for players tactically—ranging from the shuffling brain dead to the far more predatory. Furthermore, zombie apocalypses are an intriguing trope when it comes to video games and board games, as while television shows such as the Walking Dead can show characters coming to terms with difficult, even cruel choices, games can have the players actually do the choosing themselves. This chapter will look at three examples of this: the Last of Us series; the Dead Rising series (particularly 1 and 2); and finally, the board game Dead of Winter to show how each of them attempts to convey through the player avatars the themes that often come up in zombie apocalypse texts—such as betrayal, violence, trauma, and the loss of humanity
2024 IEEE Region 10 Conference (TENCON 2024)
Get PDFDistributed Denial of Service (DDoS) attacks continue to cause a substantial threat to network infrastructure and services. In this paper, we propose an approach called DDoS Layered Model Stacking (DDoS_LMS) to improve DDoS detection accuracy. Our model uses advanced ensemble machine-learning techniques to enhance the robustness and reliability of detection systems. We evaluate our model using a dataset of network traffic, including both legitimate and attack traffic. Multiple machine learning models are employed, such as Logistic Regression, k-nearest Neighbors (k-NN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Naive Bayes. Our proposed model, which combines the strengths of these individual classifiers, achieves exceptional results with 0.9872 accuracy, 0.9829 precision, 0.9847 recall, and 0.9837 F1 score. The DDoS_LMS notably outperforms individual models and proves its efficiency in detecting DDoS attacks
Epileptic seizure prediction with machine learning on EEG data
No full textEpilepsy affects over 50 million people globally, posing a significant challenge due to the unpredictability of seizures, which impacts patients' quality of life. Predicting epileptic seizures in advance can improve living standards through timely interventions and risk reduction. However, accurate seizure prediction remains unsolved. This research aims to enhance seizure prediction accuracy using machine learning methods applied to epileptic electroencephalography (EEG) signals. Key methods developed include personalized channel selection, efficient feature extraction, and accurate classification models. Three predictive models were developed, each showing a remarkable performance in terms of accuracy, sensitivity, and specificity, leading to notably enhanced epileptic seizure prediction rates. The first model employs a permutation entropy-based personalized channel selection, significantly improving accuracy but requiring careful consideration of factors that influence the channel selection. The optimal channels for predicting seizures may vary across different stages of the condition. Hence, the second model employs a personalized classification for the entire channels from each patient, emphasizing the superiority of Synchroextracting Transform (SET) over the popular short-time Fourier transform for accurately extracting information. SET, when combined with a one-dimensional convolutional neural network (1D-CNN), achieves a 100% accuracy, sensitivity, and specificity for the Bonn University database (82800 datapoints), surpassing a multilayer perceptron with a quicker computational speed. Meanwhile, when considering real-time monitoring of epileptic EEG, CNNs may not be suitable due to their computational costs and substantial memory requirements. Therefore, in the third model, a sparse representation combined with SET and basic traditional machine learning techniques like k-nearest neighbors are adopted. This approach has also been proven to be notably effective, with a 100% accuracy on the Bonn University database for seizure prediction. The three models developed in this research address the challenges arising from individual variability in brain functions and the high-dimensional nature of EEG data for epileptic seizure prediction. Future research should focus on optimizing these models for specific real-time EEG monitoring systems and conditions
Supporting teachers to teach creative writing in the classroom through reflexivity: A systematic review of the literature
No full textTeaching creative writing is an area of English and literacy education that teachers might find difficult due to its subjectiveness. Over the past few decades, approaches to teaching creative writing have been a focus of educational research. These studies have attempted to define creative writing, creative approaches to teaching writing as well as creativity and writing. We conducted a systematic literature review to explore what some of these approaches entailed. After identifying 25 studies that met certain criteria, we applied the theory of reflexivity to recommend some methods to support teachers when teaching creative writing in the classroom