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The Experience of Becoming a Teacher in the English Further Education Sector:A Systematic Review of Evidence
Get PDFState of Health Estimation of Lithium-Ion Batteries Based on Hybrid Neural Networks with Residual Connections
No full textAccurately estimating the state of health (SOH) of lithium-ion batteries is essential for ensuring the stability and safety of the battery. Although the hybrid neural network model demonstrates strong performance in estimating the SOH, network degradation becomes a significant issue as the depth of the neural network increases, potentially undermining the accuracy of the estimation. This paper presents a hybrid neural network estimation model with residual connections to address the issue of network degradation. First, the model utilizes a combination of convolutional neural networks and an attention mechanism to automatically extract feature information highly correlated with SOH from the partial charging data of lithium-ion batteries. Subsequently, a multi-layer gated recurrent unit (GRU) is employed to capture temporal information within the extracted features. To address the issue of network degradation that arises from stacking multiple layers of neural networks, residual connections are incorporated into the multi-layer GRUs, mitigating the accumulation of errors within deep networks. Finally, three distinct datasets are employed to validate the proposed model. The experimental results demonstrate that the model exhibits an average mean absolute error and root mean square error of less than 1.8% on both of these datasets.</p
Perceptions of sedentary behaviour in people with chronic obstructive pulmonary disease (COPD) following a recent hospital-managed exacerbation:A qualitative exploration
Get PDFBackgroundIndividuals with chronic obstructive pulmonary disease (COPD) often lead sedentary lives, which is linked to negative health outcomes. Understanding the causes of this behaviour is essential for designing effective interventions. In the time following a hospital discharge, people with COPD may be especially sedentary and develop habits that contribute to this behaviour. Therefore, this is an important point at which to evaluate the reasons behind sedentary behaviour.MethodsFrom one acute hospital in England, 12 participants with a recent COPD exacerbation were recruited. Following discharge, semi-structured interviews were conducted to identify perceptions of and barriers and facilitators to reducing sedentary behaviour. Reflexive thematic analysis was employed.FindingsTwo themes developed: “Focusing on survival” and “Loneliness, social isolation and lack of purpose”. Factors contributing to sedentary behaviour include the need for rest, social isolation, symptom management, fear of dying or being readmitted to hospital from over-exertion, adherence to health professional advice, and lack of motivation and purpose. Concerns about socioeconomic disparities were noted. Participants were ready to embrace positive lifestyle changes.ConclusionOur study found some people with COPD, recently discharged from hospital, may adopt a sedentary lifestyle to manage symptoms and daily activities. Interviews highlight the need to tackle socioeconomic disparities, social support, and feelings of social disconnection. Misconceptions about sedentary behaviour being part of recovery underline the need for education for individuals with COPD and health professionals. The findings suggest strategies to reduce sedentary time, such as enjoyable activities, community involvement, and incorporating sedentary behaviour reduction into pulmonary rehabilitation
Ensemble Learning for Diabetes Early Prediction Case Study:A Systematic Review
No full textDiabetes has always been the focus of attention globally, with its high impact on mortality. Over the years, machine learning and ensemble learning have been found to be potential investigations to conduct an early prediction of diabetes. Due to rapid development in these fields, it is necessary to provide a comprehensive review with overview information for early detection studies of diabetes, particularly in the employment of ensemble learning that has the benefit of merging multiple algorithms. However, most of the previous review studies were less in-depth and not mainly focused on ensemble learning applications. In this paper, a systematic review study in the area of ensemble learning for diabetes prediction is presented to overcome this issue, which included a total of 98 studies published between 2014 and 2024. Based on the methodologies, data extraction, and study processes, the result of key findings especially appraises the current state of knowledge, such as highlighting the trends in the case study, the critical review of each ensemble technique, and the advantages as well as the disadvantages of ensemble learning. Additionally, the identification of limitations is revealed in the tasks of the dataset, and the analysed studies also confirmed the opportunities for future work are in the directions of data sources, ensemble deep learning, and data preprocessing. Thus, the results of this work aim to provide a better understanding of the field area with major findings and new insights for further expansion in this scope of ensemble learning in diabetes early detection
Phenomenological Observations of Thick Liquid Sheet Breakup Mechanisms in a Transient Flat-fan Water Spray at High Frame Rates
No full textThis study investigates the transient breakup processes during liquid atomisation, which is key for applications that require control of droplet size and velocity distributions, such as drug delivery or fuel sprays. Using high-speed imaging, the breakup of a liquid flat fan spray under controlled conditions has been observed. Water was injected vertically at 20 bar pressure and 15 ms injection duration through a slit nozzle (effective orifice diameter of 0.48 mm) into quiescent air at ambient conditions. An ultra high-speed camera (Kirana, Specialised Imaging Ltd), equipped with an industrial lens and laser background illumination were utilised to capture detailed images of the spray at various stages of the breakup process. The data was collected at frame rates ranging from 20 kHz to 5 MHz, with images of the liquid sheet, breakup, and dispersed droplets captured at different times during the injection event. The results revealed three distinct stages in the breakup process. In the first stage, a laminar liquid sheet with thick rims forms, and Squire waves develop. In the second stage, the spray stabilises, with low-amplitude wave fluctuations. In the third stage, the sheet undergoes kinematic thinning, causing instabilities and the formation of bag-like structures and perforations. These create small satellite droplets, which interact with vortical flows, moving upstream, opposite to the main flow. Some droplets glide or splash, forming capillary patterns, while others puncture the sheet, leading to further breakup. The study categorises these phenomena, contributing to a better understanding of spray breakup that can aid in the development of predictive models for improving spray atomisation system design
Educational Escape Museum with IoT, Animation and Creative Writing Elements
No full textAs museums increasingly compete to engage visitors, innovative methods are needed to deliver knowledge in captivating ways. Escape rooms (ERs) offer an effective solution, combining interactive challenges with educational content to create memorable and engaging learning experiences. This paper presents The Curse of Medusa, an educational escape room designed for a museum context. The game integrates a variety of technologies, including the Internet of Things (IoT), animation, creative writing, and Amazon Alexa, to provide an immersive, multimodal experience. The objective of the project is twofold: to educate participants on a specific historical theme selected by museum experts and to introduce them to modern technological tools. The paper details the design methodology, hardware and software used, and the narrative plot of the escape room, along with an evaluation conducted through questionnaires based on the Experience Pyramid Model. Feedback was collected from archaeologists, adult volunteers, and students. The game includes combinatorial and multi-sensory puzzles that require players to observe, cooperate, listen, read, interact, and engage in tasks such as singing and writing. The use of Amazon Alexa as an Intelligent Personal Assistant (IPA) introduces interdisciplinary interaction elements that enhance gameplay. Experts praised the escape room's originality and balance between education, play, and technology. Although the paper does not present a formal evaluation of learning outcomes, feedback from both experienced and novice players indicated high levels of satisfaction with the game's design, materials, and challenges.</p
Child Labour in Pakistan: Causes and Government Initiatives for Elimination
No full textChild labour remains a critical challenge in developing countries, particularly in Pakistan, where poverty, limited educational access, and socio-economic disparities continue to drive its prevalence. This study, conducted in 2017, employed a mixed-methods approach to explore the underlying causes of child labour and assess the effectiveness of government initiatives aimed at its elimination. Quantitative data revealed a strong correlation between child labour, high poverty rates, and restricted access to education, while qualitative responses provided deeper insights into systemic issues such as weak law enforcement and inadequate welfare infrastructure. Despite policy efforts, poor implementation and unequal resource distribution—especially in rural areas—have hindered progress. The study recommends a comprehensive strategy that includes expanding access to quality education, strengthening legal enforcement, and fostering collaboration among government bodies, civil society, and the private sector. Addressing these root causes is essential to breaking the cycle of poverty and ensuring a better future for affected children
Whole organism integrated DNA methylation and transcriptomics analysis of butterfly metamorphosis
No full textMetamorphosis is one of the most fascinating developmental processes in the natural world. The underlying molecular events resulting in the transformation of a caterpillar into a butterfly, including the epigenetic and transcriptomic programs, remain elusive. By integrating DNA and RNA long-read sequencing of the entire body of five consecutive stages from larval to late-pupal development in a laboratory butterfly, we characterise the fundamental metabolic and developmental transitions taking place. We identify a progression from lipid metabolism in larvae towards an up-regulation of muscle formation and mitochondrial energy generation in pupae. Intra-genic CpG methylation correlates with, but does not appear to dictate, gene expression. The level of 5-hydroxymethlcytosine modification detected was very low. The use of long-read mRNA sequencing provided access to complete transcript isoform sequences, and differential isoform usage was notably detected in genes for energy metabolism, and for muscle and neural development during the metamorphic process
Human-robot collaborative disassembly task planning for retired power battery based on Stackelberg game and multi-agent deep reinforcement learning
No full textWith the increasing adoption of electric vehicles (EVs), the volume of retired power batteries has surged accordingly. In the context of sustainable development and a circular economy, the disassembly process of retired power battery for reuse is regarded as a crucial approach to addressing resource shortages and environmental pollution. To achieve the efficient disassembly of retired power batteries in human-robot collaborative (HRC) scenarios. Firstly, a mapping network between task units and task executors is established using a multilayer perceptron (MLP) neural network, based on the complexity of the tasks and the state of workers. Secondly, a Stackelberg Double Deep Q-Network (SDDQN) algorithm is proposed by integrating the leader-follower characteristics of the Stackelberg model with the Deep Q-Network (DQN) algorithm to address the task planning problem in HRC disassembly. Finally, the effectiveness of the proposed method is validated through two case studies. Compared to Nash Q-learning, Independent Q-learning, and the conventional DDQN algorithm, it demonstrates superior performance in terms of task completion time and average cumulative rewards. Additionally, the proposed method exhibits strong robustness against unexpected environmental disturbances.</p