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Young men with intellectual disabilities' perceptions of HPV and HPV vaccine: a qualitative study on how to communicate HPV vaccine information.
Get PDFThe success of vaccination programs relies on acceptance of recommended vaccines by communities and individuals. There is a paucity of evidence regarding how young men with intellectual disabilities actively produce or receive inclusive and accessible HPV information. As part of a larger qualitative study, we explored how young men with mild-moderate intellectual disabilities contend with information on HPV and how they negotiated safer sex prior to the introduction of the Scottish schools-based gender-neutral HPV vaccination program in 2019. Objectives included identifying strategies for reaching young men with intellectual disabilities; identifying modes of communication that enable young men with intellectual disabilities to discuss HPV; exploring knowledge, awareness, relevance, and participant experiences of HPV vaccination; perceived barriers and facilitators toward vaccination behavior; perceptions of publicly available HPV information and formats. Working with institutions of further education to identify participants, 18 young men chose to participate. Three focus group discussions using activity-oriented questions were conducted. Regardless of ability, a series of activities enabled them to explore questions about their knowledge of HPV and any experience of the vaccination program. Communication aids included familiar objects and symbols from daily life breaking down barriers and power inequities. Data were analyzed drawing on critical discourse analysis. Designed and tailored communication interventions were effective in reaching this population group. Adopting a participatory activity-oriented approach and spending significant time looking at pictures and artifacts enabled young men with mild-moderate intellectual disabilities to discuss behavioral risks and consequences of HPV and to identify design factors for accessible health information
Improved hybrid neural network based on CNN-BiLSTM-attention for co-estimation of SOC and SOE in lithium-ion batteries.
No full textAs the core of modern energy storage technology, lithium-ion batteries are widely used in fields such as electric vehicles, renewable energy storage, and portable electronic devices. Accurately estimating the state-of-charge (SOC) and state-of-energy (SOE) of lithium batteries is crucial for the safety and efficiency of battery management systems. This article proposes an improved convolutional neural network - bidirectional long short-term memory neural network-attention (CNN-BiLSTM-Attention) hybrid neural network model to estimate the SOC and SOE of lithium-ion batteries. To effectively capture long-term dependencies in time series, the BiLSTM is proposed based on long short-term memory neural networks. Meanwhile, by introducing an encoder-decoder attention mechanism, complex data can be processed more effectively, thereby improving the accuracy and reliability of estimation. The results indicate that CNN-BiLSTM-Attention has the smallest mean absolute error (MAE) and root mean square error (RMSE). Under the time condition of 35 °C, the model estimates the MAE and RMSE of SOC and SOE to be around 1 %, with SOC estimating an MAE of 0.97 %. In addition, the model exhibits robustness in data processing and effectively handles the bias of random data
Metasurface-engineered anode for solid oxide electrolysis cells.
Get PDFSolid oxide electrolysis (SOE) is a high temperature technology for the onsite production of oxygen and energy production in the form of hydrogen. Its' in-situ resource utilization potential is confirmed in the work by Hoffman et al. (2023) for oxygen production from carbon dioxide in the Martian conditions for a stack of ten cells. SOE at present is adapted for several feedstock materials, with steam electrolysers dominating in terrestrial applications. For either feedstock material, a combined use of the heat and electrical energy results in the superior electrical efficiency, compared to low-temperature electrolysis technologies. SOE allows a direct utilization of heat from nuclear, solar and alternative energy sources, including waste heat from high-temperature processes. Its high efficiency, scalability and use of solid materials make SOE a promising technology for solving Earth-bound energy production challenges, sustainable space exploration and long-term planetary colonization. At present, directions of development for solid oxide electrolysis cells (SOECs) include improving efficiency, durability and scalability for long-term operation in both terrestrial and space applications. Using metasurfaces for a single or multiple thin functional layers of the cell is one of the ways to enhance the electrical performance, as discussed in Jang et al. (2022) from the structural perspective. A metasurface represents an engineered geometrical pattern in the material layer at a millimetre to microscale, which translates into an additional electrochemically active surface area for a cathode or anode, that is in a direct contact with a fluid flow and changes its dynamics. Introducing metasurface patterns in electrochemically active layers opens a new space for design improvements, limited by the manufacturing capability. The current presentation provides a summary of complete simulation results for an anode with metasurfaces, varying in terms of types and sizes of elements. The study considers 1/16 sector model of a tubular solid oxide high-temperature steam electrolysis cell with a thick metal support layer, operating at 800 oC. Parallel flow conditions are considered, where the high temperature steam is supplied into the internal fluid channel, and the external flow of air is in a direct contact with the metasurface-patterned anode. Computation fluid dynamics (CFD) approach with the resolved electrolyte model in ANSYS Fluent is used to evaluate the current density characteristics for the selected SOEC design. The computational model is verified and validated with solid oxide fuel cell results. The study, first, evaluates the basic cell design and the geometrical pattern of rectangular elements, varying in height, length, width and distance from each other. The main outcome from this part of the work is the most beneficial combination of sizes for the rectangular pattern, reaching the increase in the electrical performance by 6.7%, compared to the base case at 1.5V of applied voltage, given 1.1V open circuit voltage. The second part of the conducted research is focused on the type of the geometrical element, considering rectangular, line and net-structured elements. From this part of the research, the net-structured metasurface is found to increase the current density by 8.5%, being the most efficient considered design, according to Kurushina et al. (2024). A net-structured surface is composed of both parallel and cross-flow line-elements. The observed electrical efficiency is linked to the patterns of air circulation along the engineered anode surface. Overall, the current study contributes to the field of knowledge by recommending net-structured metasurface-engineered anodes for practical applications, based on the performed simulations, optimization and comparison of design options. Future work in this direction could consider optimizing sizes of a net-structured metasurface for SOE
Finding balance in the digital era: the integration of information overload management and serendipity for Nigerian digital entrepreneurs.
Get PDFThis study explores the integration of information overload (IO) management and serendipitous information encounters among Nigerian digital entrepreneurs, offering novel insights into balancing efficiency and opportunity in digital ecosystems. Through a critical realism (CR) and grounded theory (GT) framework, the research analyses 26 semi-structured interviews with Nigerian business founders across sectors. Findings reveal three core strategies for mitigating IO: information immersion (deep engagement with curated data streams), limited engagement (strategic disconnection from overwhelming inputs), and detachment (selective avoidance or deferral of non-critical information). These strategies intersect with purposive and non-purposive information-seeking behaviours, demonstrating how entrepreneurs navigate information abundance while fostering serendipity. Theoretically, the research extends IO literature by integrating serendipity as a complementary-rather than conflicting-element of information management, while contributing to entrepreneurship studies through a developing country perspective. This study underscores the importance of contextualized, holistic approaches to information behaviour in fostering entrepreneurial resilience in information seeking and business idea generation
Digital technologies, resilience and innovation: a decolonial perspective of migrant women entrepreneurs in the global south.
Get PDFThis study investigates how migrant women entrepreneurs in Nigeria and South Africa use digital technologies to build resilience, drive innovation and assert agency within exclusionary systems. In particular, the study explores how these technologies are used alongside traditional knowledge to construct hybrid business models that are culturally grounded and socially impactful
Enhanced multi-scale signal decomposition transformer neural network for state of health estimation of lithium-ion batteries.
No full textThe accurate estimation of battery state of health (SOH) is important in the fields of electric vehicles, energy storage devices, and renewable energy. To address the accuracy challenges of SOH estimation caused by the phenomenon of small-scale capacity regeneration during battery aging, this paper proposes the efficient SOH estimation framework based on the combination of multi-scale signal decomposition and improved Transformer model. The improved sparrow search algorithm (ISSA) through coupling multiple strategies is used in the framework to automatically find the best hyperparameters for the encoder, decoder and optimizer structures in the Transformer model. Another important core of the proposed framework is the use the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technology, which effectively separates the characteristics of local capacity regeneration and overall capacity decline to enhance the predictability of input data. Finally, the feasibility and validity of the proposed framework are validated on public lithium-ion battery datasets. The results demonstrate the root mean square error (RMSE) consistently below 0.47 % across all test cases. Remarkably, the estimation error remains within 0.02 % even when only 50 % of the training data is utilized
Long COVID, long consequences: financial, employment and social security impacts in Scotland.
Get PDFLong COVID affects a significant number of households in the UK, with 8% of adults reporting Long COVID in Scotland in 2023. Prevalence of Long COVID in the most deprived fifth of the UK population (3.2%) is more than twice as high as in the least deprived fifth and it is more prevalent in minoritised and disadvantaged groups. Due to a lack of knowledge about the wider financial impacts of living with Long COVID and experiences of support, the current study aimed to provide an initial exploration of the these issues and recommendations for policy and practice. This study draws on evidence gathered through thirteen lived experience interviews and interviews and focus groups with twelve stakeholders from third sector organisations supporting people directly with Long COVID or with an influencing or advocacy role around poverty and inequality. Long COVID is a complex, fluctuating condition that consists of over 200 different symptoms. The severity of these symptoms affected participants in the current sample in different ways but in all cases constituted a significant impact on daily quality of life. All participants had been impacted by a loss of household income from employment because of Long COVID and the financial implications of contracting Long COVID were causing high levels of stress and anxiety for most
Project practitioners in practice: an investigation of project management competency in the oil and gas sector.
Get PDFThe aim of this study is to critically examine project management competency requirements from the perspectives of practitioners in the oil and gas industry. Furthermore, the study will explore how project managers enter the profession and how they acquire the competencies required to practice. The research will also explore whether the APM competency framework adequately reflects the role of practicing project managers. The study used both practice theory and career theory as a lens to explore the socially constructed nature of project management and give insight into how project managers develop themselves and their careers. The research takes a social constructionism approach and employs a qualitative methodology. Data has been gathered through semi-structured interviews with project management practitioners in the oil and gas sector to explore practitioner's concepts of competence and explore their route into the profession and project managers use their experience to build their careers. The findings suggest that rather than being an accidental profession there are defined routes into the profession that are established, though informal career paths. This is contrary to much of the literature that contends that one of the key challenges for project management development is the lack of career pathways. The findings of this study indicates that there is a defined path into the project management through a process of career crafting. Project management competency is achieved largely through informal learning and competency development through social learning. The findings here align with practice theory and the methods in which social networks develop and reproduce practice. The focus on social learning and interaction in practice theory where practices are developed, replicated, sustained and constantly renegotiated suggests that practice theory is valuable lens for understanding the process of project management competency development. The study also identifies a misalignment in the conceptualisation of practitioner and professional body understanding of project management competency. There is a degree of misalignment between the competencies identified by the practitioner and those outlined in the APM competency framework. The skills most highly valued by practitioners are soft skills, but also meta skills which are higher level cognitive skills which allow for the integration of skills and the ability to be flexible and apply in situational contexts. The findings indicate that competency frameworks need to be more adaptable, context specific and incorporate meta skills. This study is significant as it addresses a number of knowledge gaps in terms of project managers' career paths, competency development, key competencies and a misalignment between the current APM competency framework and project managers' perception competence. The study suggests that there are established career routes into project management and that it is not an accidental profession. This may inform how the shape and structure of project management career paths are viewed and may provide insight into developing more structured career paths for project managers. The work contributes to theoretical insights into project management by using practice theory and career theory to validate the socially constructed nature of project management
Deconstructing critical thinking skills provision: the normative and the transformative.
Get PDFCritical thinking skills are at the core of Higher Education and EAP practice; however, there is little consensus in defining the term and its elusive nature. We approached this landscape from a social-constructivist perspective aiming at deconstructing views and practices as well as generating ideas and alternative avenues for research and practice. We conducted a small-scale survey on how EAP practitioners view the relevant provision at their institutions, how they think teaching critical thinking skills can be more focused and effective, and how they view their role in this transition. We used this data as a springboard for our workshop at BALEAP 2023 Conference to initiate a cycle of de- and re-construction of EAP practice. This reflective report adopts Kolb's cycle of reflective practice to analyse the outcomes of this process. Our results indicate that the emerging themes link under two larger concepts: instructional approaches and acknowledging cultural diversity. We identified a positive move towards more holistic, post-method instructional approaches to meet learners' needs without losing sight of active student engagement. The results also highlight that diverse views and perceptions of CT skills due to cultural and educational differences were acknowledged and deficit models and/or stereotyping were rejected and identified as main challenges EAP tutors face in their practice
MSLKCNN: a simple and powerful multi-scale large kernel CNN for hyperspectral image classification.
Get PDFDeep learning-based hyperspectral image (HSI) classification models typically utilize multiple feature extraction layers to learn the features of land covers. Nevertheless, they encounter challenges, e.g., 1) Transformers require substantial computational resources, and 2) these layers are carefully assembled and designed. Recently, large kernel convolutional neural networks (LKCNNs) show excellent performance in natural visual tasks. To tackle these limitations and explore the capability of LKCNNs for HSI classification, we present a novel simple and powerful multi-scale large kernel convolutional neural network architecture (MSLKCNN) with the largest kernel size as large as 15 × 15, in contrast to commonly used 3 × 3, for HSI classification. MSLKCNN avoids these specialized designs, comprising a noise suppression module (NSM) and a multi-scale large kernel convolution (MSLKC). Specifically, NSM is first used to suppress the noise and reduce the number of the bands before extracting the features. Then, MSLKC, as the only feature extraction layer of MSLKCNN, joints three parallel convolutions to capture the features of various types (i.e. spectral, spectral-spatial) and ranges (i.e., small local, larger local, and global) from the dimension of scale: (C1) convolution with a kernel size of 1 × 1 is used to extract spectral features; (C2) multi-scale large kernel depthwise separable convolution (MLKDC) is proposed to learn the spectral-spatial features of different ranges including short-range, middle-range, and long-range; and (C3) multi-scale dilated depthwise separable convolution (MDDC) is designed to aggregate the spectral-spatial features between land covers at various distances. Extensive experimental results on three public HSI datasets demonstrate the competitiveness of the proposed MSLKCNN compared with several state-of-the-art methods
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