17628 research outputs found
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Technological trends in rainwater harvesting for micro‑hydropower: A scientometric review
No full textThis paper constitutes a newly begun scientometric perspective on the global RWH literature in the 1994–2024 period alongside an integration of the young RWH system-MHP connectivity. In contrast to earlier reviews, which focus either on RWH or small-scale hydropower, this review analyses how both fields overlap to encourage hybrid water energy options, especially so in a decentralised and rural context. In the multi-dimensional bibliometric analysis keyword clustering, author co-citation, and burst detection, this evaluation exposes hidden knowledge paths, research shortages, and developing interdisciplinary topics on integrated RWH-MHP systems. Covering 3,036 records from Web of Science, the results demonstrate that there has been a substantial increase in the number of publications in the previous decade, headed by China, the US, and India. Funding Key sponsors like the National Natural Science Foundation of China have been a big help to boost this sector. Thematic trends reflect recent concentration on "green infrastructure," "mechanisms," and "water management," as well as issues highlighting technical factors such as energy conversion efficiency, flow regulation, and storage optimisation of hybrid systems. However, technology dissemination is limited by climate changes, high costs of investment, and lack of regulatory support. It is necessary to generally build standardised design frameworks with examples in the field and to develop stronger policy practice links to strengthen the water–energy resilience. Future research should focus on system optimisation, cost effective transferring of technology, and cross-sectoral integration according to the sustainable development goals
Effect of consumer brand involvement on brand advocacy: a moderated mediation model
Full text linkThis study explored the mechanisms and contexts through which consumer brand involvement can influence brand advocacy. A moderated mediation model was empirically tested with a random sample of 387 Chinese consumers in Hong Kong. The results indicated that consumer brand involvement exerts a direct impact on brand advocacy and an indirect influence through a positive interaction between relation motivational orientation and affective brand commitment, and task motivational orientation and normative brand commitment. The findings contribute to the brand advocacy and customer brand engagement literature. This study also makes a theoretical contribution to the involvement – commitment model. Practical implications of the findings are discussed
Adversarial Machine Learning in IoT Security: A Comprehensive Survey
Full text linkThis survey presents a comprehensive analysis of Adversarial Machine Learning (AML) as a critical approach for enhancing the security of Internet of Things (IoT) ecosystems, with a focus on its integration into Intrusion Detection Systems (IDSs). It synthesizes existing research, identifies current limitations, and outlines future directions for effective AML deployment. The survey explores emerging technologies to improve IDS robustness, adaptability, and threat anticipation capabilities. It further addresses the challenges of implementing AML in resource-constrained IoT environments. A structured, multi-phase attack framework inspired by the MITRE ATT&CK model is proposed to illustrate the evolving lifecycle of adversarial threats. Ethical and regulatory considerations are examined, emphasizing responsible deployment and accountability. The cross-domain applicability of AML is discussed across various environments, including smart grids, autonomous systems, cloud, and edge computing. Real-world use cases are analyzed, with attention to adversarial datasets and evaluation practices. Additionally, the role of Human-in-the-Loop (HITL) systems is highlighted for enhancing interpretability and trust in AML-powered IDSs. This survey serves as a valuable resource for researchers and practitioners aiming to advance AML in securing IoT ecosystems with innovative, ethical, and practical defense strategies
Principles for the use of Generative AI in healthcare education
No full textThe Council’s Innovation in Education Strategic Policy group has been leading the development of high-level principles for integrating Generative Artificial Intelligence (AI) into healthcare education, recognising its potential to enhance teaching, learning, and student support. As AI tools become increasingly prevalent, the healthcare education sector must collaborate to balance innovation with ethical considerations. Academic integrity and professional standards must be upheld whilst fostering equitable access to AI-driven advancements.As leaders of professionally regulated programmes, our duty is verify that student’s work is their own, thereby producing healthcare professionals who are highly skilled in healthcare delivery, appraising research evidence and developing innovative practice. Alignment to codes of professional practice is essential and ethical debates around governance, and a robust approach to AI in healthcare practice and education is essential. Intellectual dishonesty such as plagiarism has become a growing concern in education and practice and has resulted in universities and colleges rethinking how they assess students using more authentic methods such as oral examination, in-class tests and group discussions. This is welcomed but, we must also evaluate how effective students are in verifying information derived from AI tools thus promoting transparency in its use.Across academic institutions there has been a plethora of guidance for students and teachers on how to use large language models, such as chat GPT, Co-pilot and DeepSeek. The high-level principles proposed in this report are aligned to regulatory frameworks and academic institutional guidance. We have also provided practical examples of how AI can support innovative approaches to healthcare education. This is intended to add to the evidence base and understand how students, educationalists and healthcare practitioners are applying AI to their professional practice thus advancing knowledge in this area.This report signifies the centrality of innovation to the values and strategic priorities of the Council of Deans of Health. I would like to thank members of the working group formed to develop the principles; as well as the wider Innovation in Education Strategic Policy Group and Council members from across the UK who submitted illustrative examples
Critical weight percent of graphene nanoplatelets (GNPs) that optimizes water barrier and transport properties of ethylene vinyl acetate (EVA) for improved photovoltaic module packaging reliability
Full text linkEthelyne Vinyl Acetate (EVA) is used to encapsulate crystalline silicon photovoltaic module, but its poor water barrier property (WBP) limits its deployment in encapsulating and edge sealing of perovskite solar cells (PSC). As composites demonstrate boost in WBP when infused with the appropriate weight percentage (wt%) nano-filler, this research improves WBP of EVA by impregnating it with graphene nanoplatelets (GNPs). This investigation delivers a chart for determining the magnitude of water vapour transmission rate (WVTR) of wt%EVA-GNP composites within the composition range of 0–14 wt%GNP at 19°C, 36°C and 50°C temperatures. Eight film samples were developed and used as test vehicles. SEM with EDX attachment, FT-IR and Raman spectroscopies were deployed to investigate the microstructural properties while ASTM E96 wet-cup method was used to study the WVTR of the vehicles. The vehicles’ mass transport properties were computed using relevant constitutive models while water ingress into them are modeled with COMSOL Multiphysics software. The water barrier properties of EVA are improved through GNP loading. Loading EVA with 8 wt%GNP at 19°C decreased its WVTR from 2.4 g m−2 day−1 to 0.37 g m−2 day−1. Increasing wt%GNP loading above 8 wt% results in a decrease in the amount of moisture accumulated in the composite but no further decrease in its WVTR. Results from FT-IR demonstrate that at about 10 wt%GNP loading, interfacial bond between GNP and EVA is maximum owing to even dispersion of GNP. Findings from Raman Spectra demonstrate that exfoliation, dispersion and interfacial adhesion are maximum in composite in the domain of 6 wt% to 10 wt% GNP infusion. Consequently, 8 wt%-EVA-GNP composite is found optimal and proposed a good composite for sealing the edges of photovoltaic module
Filling the gaps – New molecular and morphological data of gregarine apicomplexans colonising freshwater invertebrates
Full text linkGregarines (Apicomplexa: Gregarinasina) are widespread protist symbionts of invertebrates, occupying roles across the symbiotic spectrum from mutualism to parasitism. Despite their ecological importance, they remain far less studied than other apicomplexans, leaving many aspects of their diversity, host specificity, and evolutionary history unresolved. This is particularly true for freshwater taxa for which only a handful of small subunit (SSU) rDNA sequences from species colonising freshwater hosts are available in public databases. In this study, we screened ten freshwater invertebrates (Arthropoda and Annelida) from streams and rivers in North-Rhine Westphalia, Germany, for gregarine infections. Nine eugregarine species were detected and described by combining light and electron microscopy with SSU rDNA sequencing data. We provide new host and locality records, ultrastructural observations, and molecular data for these gregarine species. The SSU phylogenetic analyses reveal a novel well-supported subclade within Gregarinoidea and support the reassignment of the family Metameridae to the Actinocephaloidea. Our findings expand the available molecular and morphological data for freshwater eugregarines and contribute to a clearer picture of their evolutionary relationships
Polypharmacy Guidance: appropriate prescribing, making medicines safe, effective and sustainable
Full text linkWritten by Scottish Government, experts from NHS Scotland and experts by experience and in collaboration with SIGN, the guidance updates the previous 2018 edition to ensure appropriate prescribing of medicines to optimise treatment outcomes and achieve the best care. The guidance is endorsed by the Royal Pharmaceutical Society, the Royal College of General Practitioners, the Royal College of Physicians of Edinburgh and the National Centre for Sustainable Delivery.Polypharmacy represents a growing global challenge due to aging populations with increasing prevalence of multimorbidity. Care of adults with multiple medical conditions is often overly complex and rarely person-centred. This leads to poor health outcomes, unsustainable levels of expenditure and avoidable environmental damage, all of which disproportionately affects the most vulnerable in society. The updated polypharmacy guidance continues to place the individual at the centre and places greater emphasis on shared decision-making to actively engage them in safe and effective care using the 7-Steps model for medicines reviews. The revised guidance has been expanded to provide additional practical support to multidisciplinary teams (MDT) implementing the 7-Steps medication review and includes: Updated Cumulative Toxicity table and anticholinergic burden guidance to help target harm reduction Updated Drug Efficacy (Numbers Needed to Treat) tables to help discussion with the individual regarding the relative potential benefits of a range of common therapeutic interventions Enhanced clinical hot topics considering specific patient groups such as those with mental health conditions, frailty and comorbidities (often more prevalent in deprived areas) Additional case studies to support learning for use by the MDT across different settings, e.g. GP practice, secondary care clinics and palliative care.Updated National Polypharmacy Indicators further developed and prioritised to support national progress, understand prevalence and monitor clinical outcomes including a suite for care homes Revised case finding indicators to support identification of individuals (including those at risk of medicine related harm) for review in practice.Revised Medication Sick Day Guidance including new medicines, such as those used in the management of diabetes that can cause har
Invisible Yet Indispensable: Why Clinical Research Nursing Remains a Neglected Policy Priority
No full textQuantized‐Based Data‐Driven Iterative Learning Heading Control for Unmanned Surface Vehicles With Data Dropouts
No full textThis paper studies a model-free adaptive iterative learning heading control problem for unmanned surface vehicles (USVs) with quantized data and data dropouts. First, a compact form dynamic linearization model is established for USVs using a dynamic linearization technique and a redefined scheme. To address data dropout issues, a comprehensive compensation strategy is developed. In addition, a logarithmic quantization mechanism is introduced to reduce the transmission burden. Based on these elements, a quantized model-free adaptive iterative learning control approach is designed. The convergence of the heading control error of USVs governed by the proposed method is rigorously proven, and its effectiveness is verified through simulation results
Doubt Regarding Abuse-Related Appraisals and Identification with the Aggressor as Predictors of Complex PTSD in Child Abuse Survivors
No full textBackground: Child abuse (CA) is a significant risk factor for trauma-related psychopathology, with potential outcomes that extend beyond posttraumatic stress disorder (PTSD) to include complex PTSD (CPTSD)—a condition characterized by disturbances in self-organization (DSO). This trauma can also lead to identification with the aggressor (IWA), where survivors internalize the perpetrator’s beliefs, perspectives, and behaviors, as well as doubt regarding abuse-related appraisals (DARA), which reflects uncertainty in interpreting aspects of the abuse. Although IWA and DARA have been proposed as potential contributors to trauma-related symptomatology, their predictive roles have not been empirically examined. Objective: This two-wave study explored the implications of IWA and DARA for subsequent PTSD and DSO symptoms. Method: The current study was conducted among 273 adult female CA survivors, aged 18–53 (M = 33.01, SD = 9.78). Participants completed online self-report measures assessing IWA and DARA at the first measurement (T1) and PTSD and DSO symptoms at two time points (T1 and T2). Results: The results revealed positive associations between IWA and DARA at T1 and PTSD and DSO symptoms at T2. Analyses further indicated that the IWA component, which involves the replacement of one’s agency with that of the perpetrator at T1, predicted variance in PTSD and DSO at T2 (ES= 0.15 and 0.15, respectively). Additionally, the DARA component, which reflects doubt regarding the abuse at T1, predicted variance in DSO symptoms at T2 (ES= 0.17). These effects remained significant even after accounting for polyvictimization, PTSD, and DSO at T1. Conclusions: IWA and DARA may be important psychological factors contributing to survivors’ vulnerability to trauma-related psychopathology
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