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Achieving energy autonomy in smart farm systems through renewable thermal energy: A scenario-based environmental and economic assessment
Full text linkThe global transition toward renewable energy has been accelerating in response to climate change. South Korea has increasingly expanded the adoption of renewable energy, especially solar power. Smart farms are one example of renewable energy applications but require both electricity and heat demand. Previous studies have shown that the target smart farm does not achieve 100% energy self-sufficiency rate under current conditions. Therefore, renewable energy-based autonomous energy systems and their operational strategy are required to reduce grid dependency with full energy autonomy. This study proposes a scenario-based energy autonomy framework that integrates life-cycle assessment and techno-economic analysis for smart farms with coupled electricity and heat demands using measured operational data. Analysis results indicated that the proposed energy autonomous scenario could reduce CO2 emissions by up to approximately 92% compared to a centralized grid-based system. It also improved economic viability, lowering the LCOE from 0.287/kWh compared with the existing operating model. Sensitivity analysis identified electricity and wood pellet prices as the most influential economic factors. Consequently, a 60% solar-40% biomass configuration provides the optimal balance between environmental performance and economic efficiency. These findings demonstrate the practical feasibility of applying renewable and low-carbon technologies to smart farms within agricultural systems
Co-production with marginalised workers: working with homecare workers and managers caring for people approaching end-of-life
Full text linkBackground: Co-production is important due to its effectiveness in creating relevant and meaningful outputs for use in social and healthcare practice, however, frontline staff such as homecare workers (also known as aides, personal assistants or domiciliary care workers providing paid care within the home) are a key group within the social care workforce who are under-represented in this approach. Here, we report our coproduction process engaging with this workforce to develop training resources for workers providing end-of-life homecare. Aim: To co-produce training resources with homecare workers and their managers to support and educate workers delivering end-of-life homecare using evidence from our larger qualitative interview study. Methods: We conducted a series of 12 co-production workshops with UK-based homecare workers and managers (partners) to design training resources and recommendations for homecare providers informed by research findings. We adopted the five key principles of co-production: Sharing of power; Including all perspectives and skills; Respecting and valuing knowledge; Reciprocity; and Building and maintaining relationships. A co-production advisory group of homecare workers as well as the workshop partners gave valuable oversight throughout the workshop series. Results: 77 partners (31 homecare workers, 46 managers) participated in 12 workshops (one face-to-face; 11 online). Our approach enabled power-sharing, inclusivity, respect, collaboration and reciprocity, relationship-building, and identification of effective flexible approaches to co-production. Specific forms of training resources were co-created. Training recommendations (content, delivery formats, access during working hours, etc.) were also developed together. Challenges were non-attendance and lack of engagement by some partners during sessions. Conclusion: These workshops are the first, to our knowledge, to successfully co-produce end-of-life care training resources with homecare workers and managers, a poorly represented workforce in co-production. Challenges included inconsistent attendance and poor engagement by a minority of partners. The five key principles of co-production enabled true engagement with the process, thereby enriching the final outputs
Financial status and pain in the last week of life: Insights from a nationally representative mortality follow-back survey in England and Wales
Full text linkPain relief is a priority for patients and families living with advanced illness. There is evidence of socioeconomic inequality in palliative and end-of-life care, yet little is known about how pain might differ according to financial status. We analysed data collected in a mortality follow-back study carried out in England and Wales in 2023, to examine the association between subjective financial status and being affected by pain during the last week of life. Pain was proxy-reported by a family member using the Integrated Palliative Care Outcome Scale (IPOS). We report IPOS pain prevalence, its association with financial status, and whether this was mediated by access to care or moderated by location of care. Of 1194 decedents, 415 (37.9%) were severely or overwhelmingly affected by pain in the last week of life. Compared to decedents financially ‘living comfortably’, those ‘doing alright’ (IRR 1.20, 95%CI 1.01–1.43), and ‘just about getting by’ (IRR 1.31, 95%CI 1.04–1.65) were more affected by pain in the last week of life. Difficulty accessing care did not mediate this association. This association was moderated by location of care (F(14) =107.9, p < 0.001) and was not seen in care homes. Sensitivity analysis suggested high potential for unmeasured confounders (E-values between 1.70–1.95 for the main exposure categories). This study demonstrates that lower financial status is associated with being more severely or overwhelmingly affected by pain in the last week of life
AI-based Topic Modelling for Financial Disruption Analysis: Herd Behaviour and Sentiment Shift in the NFT Market Crash on X (Twitter)
No full textThis study examines herd behaviour and fandom dynamics in the NFT market during the 2021 crash by analyzing 184,257 X posts across pre-crash, peak-decline, and post-crash phases. Using LDA, NMF and BERTopic alongside RoBERTa sentiment analysis, we capture evolving topics and emotional trajectories in slang-rich social media discourse. We found that sentiment shifted from strong pre-crash optimism (82.8%) to polarised reactions during the crash, followed by predominantly neutral, risk-averse tones (54.9%) post-crash. Topics reveal transitions from investment enthusiasm and project hype to security concerns, fund recovery, and scam awareness, reflecting changing community priorities. The findings show how fandom and herd behaviour jointly shape sentiment in volatile digital asset markets, highlighting the role of social identity and collective behavioural mechanisms in emotional contagion and instability. Managerially, the results highlight the need for transparency, fraud prevention, and proactive sentiment monitoring to support trust, early risk detection, and more informed engagement
Gatekeepers of the Urban Margin: Residents’ Associations and Everyday Governance in Peri-urban Accra, Ghana
No full textDeep seafloor exposures of subaerial volcanic lithofacies on the Tonga trench slope: Ground-truthing forearc structure
No full textVolcanic sequences exposed on the Tonga forearc and trench slope were investigated using a submersible to document seafloor outcrops and characterise volcanic lithofacies. These observations were used to establish lithofacies relationships and interpret depositional environments and volcanic processes. At ~5080 m below sea level, the sequences record both effusive and explosive volcanism, including lava flows, pumice fall and pyroclastic density current deposits. When integrated with over 70 years of regional geological data, these lithofacies are interpreted as Eocene subaerial rhyolitic volcanism, forming part of the forearc basement. Visual observation of seafloor exposures can help ground-truth, bridging the gap between sampling biases of dredging, the limited spatial coverage of drilling campaigns, and large-scale geophysical surveys. The data provide new documentation of volcanic lithofacies across previously inaccessible depths of the Tonga forearc and trench, offering rare insights into volcanic deposits that may otherwise be poorly preserved in the geological record or difficult to recover. The presence of subaerially derived volcanic sequences at these depths supports non-accretionary forearc models and highlights the value of targeted submersible investigations
Supporting nursing students with dyslexia
No full textDyslexia is characterised by difficulties with the accuracy and/or speed of word reading and spelling. Nursing students with dyslexia can experience challenges with assessment methods and learning resources, clinical placements and their emotional and psychological well-being. The decision to disclose dyslexia can be fraught with fear of discrimination or stigmatisation, but disclosure is essential for accessing reasonable adjustments, such as extended time for exams and assistive technologies. This article explores the challenges experienced by nursing students with dyslexia and the strategies higher education institutions and practice settings can implement to support them. These include detecting dyslexia early, offering reasonable adjustments and psychological support, training nurse educators, practice assessors and practice supervisors, and fostering a culture of inclusivity
“You feel alone in the world”: Support experiences of parentally bereaved children – A constructivist grounded theory
No full textIn the UK, a parent dies every 22 minutes, significantly affecting children’s mental health, education, and social well-being. Yet little is known about children’s lived experiences of bereavement and support. Developed with public involvement, this qualitative study employed constructivist grounded theory. In-depth virtual interviews were conducted with eleven parentally bereaved children aged ten to eighteen. Iterative data analysis used the constant comparative method. Analysis constructed five themes: (1) What helps, (2) Talking on your terms, (3) A tornado of emotions, (4) Difficulties accessing support, and (5) Stepping up at home. Participants reported value when involved in family matters but often concealed emotions to protect others. Friendships were strained, and participants felt forgotten and pressured to “move on” over time. Some children struggle to discuss parental death for fear of burdening others. Greater societal awareness and sustained, sensitive support are vital to navigate life without a parent
Short Text Mining Technology for Plastic Industry Integrating Text Clustering and Sentiment Analysis
No full textThe transition to the circular economy aims to replace the traditional linear "take-make-dispose" model with a regenerative system focused on reuse, recycling, and sustainable resource management. Achieving this transition is challenging and complex, requiring both systemic change and innovative technological solutions. One of the most visible and problematic materials in the circular economy is plastic—an essential but raising significant sustainability concerns for the environment. Public opinion plays a key role in shaping policies and practices in the circular economy. The analysis of public opinion is essential because it can offer a valuable and accurate reflection of the attitude of the public, which will support the decision-making of the related stakeholders.This thesis proposes a text-mining framework rooted in the analysis of public opinion for the plastic industry, by integrating text clustering and sentiment analysis. First, we developed a web-crawler system to collect user comments related to plastic recycling. The system targets discussions under news articles and extracts a rich dataset of short texts that reflects a diverse range of public opinions. Subsequently, a method combining text classification and sentiment analysis is proposed to automatically identify and extract user opinions. This framework analyses sentiment changes across different topics and time frames, which helps to understand the changes in public attitudes towards different aspects of plastic use and policy. While analysing the process, we found that following certain paths can help improve the accuracy and efficiency of this analysis. To break through the limitations of traditional classification models on short text classification, we proposed an innovative method that uses Latent Dirichlet Allocation (LDA) for topic modelling and Bidirectional Long Short-Term Memory (Bi-LSTM) networks for sequence learning. This hybrid model fully exploits the advantages of unsupervised topic discovery and deep contextual embedding. To improve the accuracy of sentiment analysis, this paper adopts a dynamic sentiment lexicon approach. A new sentiment analysis method is implemented using a dynamic, domain-specific lexicon.This research highlights the potential of text mining with advanced machine learning models for domain-specific knowledge with a case study in the plastic industry. The proposed framework can be applied to any domain or industry to offer accurate, dynamic and timely insights for public opinion
