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Time to retire the raw analysis of individual responses
Get PDFThis letter critiques the data analysis of a study investigating peak oxygen uptake responses to cycling and running sprint interval training (Digby et al. 2023 J Strength Cond Res, 37(4), e313-e316). While the study effectively demonstrates the specificity principle in the context of sprint interval training, concerns arise regarding the methodology used to categorise participants as responders or non-responders. The letter highlights the disregard for the recommendations of a number of academics advocating for specific experimental designs and statistical analyses to examine inter-individual variability. Furthermore, the reliability of within-individual adaptive responses to training and the potential impact of measurement errors and biological fluctuations are considered. It is suggested that the (non-)responder categorisation adds nothing to the main findings of the study and should be avoided. The importance of using appropriate experimental designs and statistical analyses when investigating inter-individual variability is emphasised. An open-source beta-version simulator is introduced as an educational resource to demonstrate the limitations inherent in the responder counting approach
Special issue: Disciplinary perspectives in career development
Get PDFThis editorial sets the scene for the special issue, and provides an introduction to thinking about disciplinarity and interdisciplinarity in the field of career development
A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing
Get PDFSemantic segmentation of Remote Sensing (RS) images involves the classification of each pixel in a satellite image into distinct and non-overlapping regions or segments. This task is crucial in various domains, including land cover classification, autonomous driving, and scene understanding. While deep learning has shown promising results, there is limited research that specifically addresses the challenge of processing fine details in RS images while also considering the high computational demands. To tackle this issue, we propose a novel approach that combines convolutional and transformer architectures. Our design incorporates convolutional layers with a low receptive field to generate fine-grained feature maps for small objects in very high-resolution images. On the other hand, transformer blocks are utilized to capture contextual information from the input. By leveraging convolution and self-attention in this manner, we reduce the need for extensive downsampling and enable the network to work with full-resolution features, which is particularly beneficial for handling small objects. Additionally, our approach eliminates the requirement for vast datasets, which is often necessary for purely transformer-based networks. In our experimental results, we demonstrate the effectiveness of our method in generating local and contextual features using convolutional and transformer layers, respectively. Our approach achieves a mean dice score of 80.41%, outperforming other well-known techniques such as UNet, Fully-Connected Network (FCN), Pyramid Scene Parsing Network (PSP Net), and the recent Convolutional vision Transformer (CvT) model, which achieved mean dice scores of 78.57%, 74.57%, 73.45%, and 62.97% respectively, under the same training conditions and using the same training dataset
Assessing the validity and reliability of the International Anxiety Questionnaire and the International Depression Questionnaire in two bereaved national samples
Get PDFThe International Anxiety Questionnaire (IAQ) and International Depression Questionnaire (IDQ) are self-report measures of ICD-11 Generalized Anxiety Disorder (ICD-11 GAD) and ICD-11 Single Episode Depressive Disorder (ICD-11 DD). This study tested the psychometric properties of these scales in two samples of bereaved adults from the United Kingdom (UK) and the Republic of Ireland. Confirmatory factor analysis (CFA) was used to test the combined dimensionality and measurement invariance of the IAQ and IDQ across the UK (n = 1,012) and Irish (n = 1,011) samples. Differential item functioning (DIF) was tested using multiple indicator multiple cause (MIMIC) modelling while convergent validity was also assessed. CFA results supported a correlated two-factor model in both samples. The MIMIC model showed that the IDQ item "Had recurrent thoughts of death or suicide" showed DIF and the effect was small. Internal reliability of the scales were high and convergent validity was supported. The prevalence of ICD-11 GAD was 18.6% and 16.1% and ICD-11 DD was 13.8% and 10.5% in the UK and Irish samples, respectively. Findings of the study provide support for the validity, measurement invariance, and reliability of the IAQ and IDQ among two bereaved national samples
DBMA-Net: A Dual-Branch Multi-Attention Network for Polyp Segmentation
No full textIn the early prevention stage of colorectal cancer, the utilization of automatic polyp segmentation techniques from colonoscopy images has demonstrated efficacy in mitigating the misdiagnosis rate. Nonetheless, accurate polyp segmentation is always against with various challenges, including the presence of inconsistent size and morphological changes within polyp classes, limited inter-class contrast, and high levels of interference. In recent years, much methodologies based on convolutional neural networks (CNNs) have been widely introduced to enhance the precision of polyp segmentation. However, two significant hurdles persist: (1) These methods frequently suffer from an inadequate acquisition of contextual features, causing insufficient feature representation. (2) There is a deficiency in recognizing intricate information, such as precise polyp boundaries. Addressing these issues, this paper introduces a novel dual-branch multi-attention network, denoted as DBMA-Net. Specifically, proposed DBMA-Net primarily introduces a dual-encoding path that combines CNN and Transformer-based approaches to enrich feature representation. Additionally, an attention-based fusion module (AFM) is incorporated between the dual-encoding path, aimed at optimizing features by supplementing local information with global insights. Subsequently, two distinct attention mechanisms are introduced to enhance features: the attention-based enhancement module (AEM) and the multi-view attention module (MAM), to acquire stronger local features. These modules serve to enrich the finer details while extensively exploring and enhancing the lesion region, thereby further elevating segmentation accuracy. Following the above feature optimization, the enhanced feature maps are hierarchically integrated across multiple scales based on the proposed multi-scale feature integration module (MFIM) for accurate feature reconstruction. This strategy not only curtails feature loss but also aids in restoring featur..
Knowledge, Attitude and Practices of energy utilisation behaviours: A study of residential building occupants
Get PDFPurposeThis study aims to assess residential energy consumption knowledge, attitudes, and practices in Abuja Municipality, providing insights for effective conservation strategies, reducing costs and mitigating environmental impact.Design/methodology/approachData for this study was collected through a cross-sectional survey conducted among a representative sample of the Nigerian population between February and April 2021. A total of 462 questionnaire responses were collected and subsequently analysed using SPSS. Descriptive statistics, including frequency count, percentages, mean, and standard deviation, were calculated. Additionally, inferential statistics were performed using Chi-Square analysis, with significant level set at p = 0.05 to draw meaningful conclusions from the data.FindingsThe study results indicate that out of the total respondents, 244 individuals (67.4%) demonstrated a profound knowledge of and good practice in energy utilisation. In comparison, 118 individuals (32.6%) exhibited poor knowledge and practice in energy saving. Moreover, the findings reveal a significant association between the sociodemographic factors of the respondents, building type, and their overall practice in energy utilisation. Statistical analysis shows significant? 2 values for each case: 8.563 (p = 0.003), 66.736 (p = 0.000), 60.866 (p = 0.000), 23.487 (p = 0.000), 37.877 (p = 0.000), and 92.334 (p = 0.000), respectively, where p < 0.05. These results highlight the importance of considering sociodemographic profiles and building characteristics when assessing general energy utilisation practices.Originality/valueThe research offers valuable insights into Nigerian energy usage behaviours and attitudes towards energy saving in residential buildings, contributing significantly to the knowledge base
Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)
Get PDFForests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained
Evaluation of a pilot online education program to develop midwives’ knowledge, skill and confidence in perinatal mental health in rural South Australia
Get PDFOne in five women will experience perinatal anxiety and/or depression. In South Australia, a rural health service identified a high proportion of women with risk of perinatal mental health challenges and sought additional education for midwives. In response, a six-week facilitated, online perinatal mental health education program (e-PMHEP) was piloted. Aim The aim of this study was to evaluate the effectiveness of the (e-PMHEP) for rural midwives, nurses and Aboriginal maternal infant care practitioners. Method Program evaluation incorporated a validated online pre/post survey to assess self-reported knowledge, skill and confidence regarding perinatal mental healthcare. Additional questions sought feedback on satisfaction and feasibility. Findings Sixteen participants from rural South Australia engaged in the project from June to August 2022. Twelve participants completed the online pre/post survey. The overall pre/post knowledge scores were statistically significant (t=2.73, 8df, p=0.025) with improvement from the pre to post-test. Pre/post data also showed a measurable increase in confidence and skills. All respondents agreed that the content addressed their learning needs and would recommend this program to other practitioners. Discussion The e-PMHEP appeared beneficial in developing knowledge, skills and confidence regarding perinatal mental healthcare in rural midwives and practitioners. Only a third of practitioners routinely developed a mental health care plan with women. Key strengths of the program included the accessible content, and the combination of an experienced mental health clinician and a facilitator with lived experience. Conclusion Providing an accessible, facilitated online perinatal mental health education program could be beneficial for rural midwives
Exploring advances in nanofiber-based face masks: a comprehensive review of mechanical, electrostatic, and antimicrobial functionality filtration for the removal of airborne particulate matter and pathogens
Get PDFThe filtration of airborne particulate matter (PM) and aerosols utilizing nonwoven fibrous materials has received significant research concern due to the continuing global pandemics, especially the outbreak of coronavirus disease (COVID-19), and particularly for face masks as a measure of personal protection. Although spun-bond or melt-blown nonwoven fabrics are among the pioneer materials in the development of polymer microfiber-based face masks or air filters on a large scale, relatively new nonwoven manufacturing processes like electrospinning and solution blow spinning (SBS) are gaining momentum among manufacturers of filter membranes. The high filtration performance of nanofiber face masks is due to their high surface area to volume ratio which increases the interaction between the nanofiber and PM and improves the electrostatic charge distribution of electret filters, allowing enhanced capture capability based on electrostatic deposition. Moreover, the small diameter of nanofibrous filters improves the breathability of the face mask by providing the slip effect, which in turn reduces the pressure drop through the membrane. This paper provides a comprehensive review of contemporary advances in nanofiber face masks, detailing the working mechanism involved, reviewing recent experimental studies, and discussing improvements in filtration efficiency for three main nanofibrous air filtration strategies, including mechanical and electrostatic filtration and antimicrobial functionality. Furthermore, prospective research is introduced which considers the synergistic combination effects of the three filtration mechanisms in designing a multifunctional nanofiber structure that can efficiently capture a wide range of PM with higher filtration efficiency and lower drops in pressure. New trends in the antimicrobial activity of smart material-based nanofibrous membranes in the fight against infectious airborne agents are also described
Exponential input-to-state stability for Lur’e systems via Integral Quadratic Constraints and Zames-Falb Multipliers
Get PDFAbsolute stability criteria which are sufficient for global exponential stability are shown, under a Lipschitz assumption, to be sufficient for the a priori stronger exponential input-to-state stability property. Important corollaries of this result are: i) absolute stability results obtained using Zames-Falb multipliers for systems containing slope-restricted nonlinearities provide exponential input-to-state-stability under a mild detectability assumption; and ii) more generally, many absolute stability results obtained via integral quadratic constraint (IQC) methods provide, with the additional Lipschitz assumption, this stronger property
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