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Transient and periodic shear wave propagation in a solid–fluid coupled system
Get PDFA coupled system composed of a Newtonian fluid located on a sinusoidally forced elastic solid is studied analytically and numerically. The focus is on the transient evolution from the beginning of the forced oscillations and on the periodic behaviour established once the transient has vanished. The analytical solution is expressed as series summations that elucidate the propagation and reflections of elastic transverse waves through the solid layer and the viscous dissipation of oscillations in the fluid layer. Short-term transients in both the fluid and the solid form at every interaction between an elastic wave and a solid boundary. The long-term transient, quantified by the power balance in the fluid layer, instead pertains to the formation of all the elastic waves in the solid layer. The system can be viewed as a generalized transient Stokes layer generated by the elastic waves or as a damped resonant oscillator when the velocity at the fluid–solid interface increases significantly with respect to the forcing amplitude. A parametric study is carried out for three applications of technological interest, i.e. the indirect measurement of fluid viscosity, the turbulent drag reduction by travelling shear waves and the sensing and manipulation of biological flows
Assessing thoughts, feelings and behaviours related to hypoglycaemia: psychometric evaluation of the hypoglycaemia cues questionnaire (HypoC- Q)
Get PDFAims
To describe the design and examine the psychometric properties of the Hypoglycaemia Cues Questionnaire (HypoC-Q) for assessing thoughts, feelings, and behaviours related to hypoglycaemia among adults with type 1 diabetes (T1D).
Methods
The HypoC-Q was designed iteratively, informed by exploratory interviews with 17 adults with T1D with impaired awareness of hypoglycaemia and/or recurrent severe hypoglycaemia, and consultation with diabetologists. Psychometric analyses were completed on baseline data from the Hypo-METRICS study. Data from adults with T1D, reporting at least one hypoglycaemic event, were eligible if they had completed the baseline HypoC-Q. Completion rates, latent structure, internal consistency, construct and known-groups validity were examined.
Results
In Hypo-METRICS, 154 participants (62% females; mean ± SD age 44 ± 15 years; T1D duration: 23 ± 16 years) were eligible. All completed all 40 HypoC-Q items, demonstrating its acceptability. Exploratory factor analysis identified four scales with satisfactory internal consistency (α = 0.69–0.81): 1) low concern (7 items), 2) burnout (6 items), 3) missing cues (5 items), and 4) delaying treatment (9 items); plus eight items, treated separately. Construct validity was supported by significant moderate correlations between ‘burnout’ and fear of hypoglycaemia and diabetes distress, and between ‘missing’ and ‘delay’ with impaired awareness of hypoglycaemia; all three distinguished between those with intact and impaired awareness (known-groups validity); but not by history of severe hypoglycaemia.
Conclusions
The HypoC-Q is an acceptable, valid, and reliable measure of thoughts, feelings, and behaviours related to hypoglycaemia among adults with T1D. It is available for informing and assessing the effect of interventions to reduce hypoglycaemia exposure and impact
Soviet Indology and the rise of “insurgent philology”
Get PDFThe article discusses the development of “insurgent philology” in early Soviet Russia and its relationship to Oriental Studies. After some general historical and theoretical points, the character of early Soviet Indology is discussed, with particular reference to the work of F. I. Shcherbatskoi and the influence of neo-Kantianism. A discussion of changes in Indology in the early USSR follows, with particular attention given to A. P. Barannikov. Buddhologist and member of the “Bakhtin Circle,” M. I. Tubianskii is considered as a transitional figure, with particular reference to his work on Rabindranath Tagore. Connections with the emergence of an “insurgent” trend among anti-caste intellectuals (Phule, Thass, Ambedkar) are made and some final reflections on the relevance of Bakhtin’s work to this field are given.
O artigo discute o desenvolvimento da “filologia insurgente” no início da Rússia Soviética e sua relação com os Estudos Orientais. Após alguns pontos históricos e teóricos gerais, discute-se o caráter da Indologia soviética inicial, com referência especial ao trabalho de F. I. Shcherbatskoi e à influência do neokantianismo. Em seguida, discutem-se as mudanças na Indologia no início da URSS, com atenção especial para A. P. Barannikov. O budologista e membro do “Círculo de Bakhtin”, M. I. Tubianskii, é considerado uma figura de transição, com referência especial ao seu trabalho sobre Rabindranath Tagore. São feitas conexões com o surgimento de uma tendência “insurgente” entre os intelectuais anticastas (Phule, Thass, Ambedkar) e são apresentadas algumas reflexões finais sobre a relevância do trabalho de Bakhtin para esse campo
Revisiting the gender gap in innovation: A qualitative comparative analysis of high-tech new ventures in China
Get PDFPrevailing deficit logic suggests that women entrepreneurs underperform in innovation due to resource disadvantages; yet, growing evidence shows that gender does not consistently exert a statistically significant effect. We address this puzzle through a contextual–configurational approach, examining how entrepreneurs not only do gender but also do context in achieving a high innovation outcome. Using qualitative comparative analysis on a sample of high-tech new ventures in China, we identify three distinct innovation archetypes and make several key contributions. Thus, by integrating multiple theoretical perspectives, we develop a contextualised understanding of innovation in high-tech new ventures shaped by the interplay between gender, intangible resources and environmental dynamism. In addition, we reveal that the conjunction of women, gender and resource disadvantage can, under conditions of environmental turbulence, foster high levels of innovation, and finally, we reconcile the ongoing debate regarding the role of gender in innovation, offering a more holistic understanding of gender differences in entrepreneurship
10 tips on performing economic evaluation in kidney disease
Get PDFNephrology has benefited from a growing body of high-quality clinical evidence, including clinical trials of pharmacological therapies and health service research on alternative care approaches. Consequently, there is an increasing need to perform economic evaluations in kidney disease to inform reimbursement decisions and optimise healthcare spending, thereby improving patient care within budget constraints. Cost-effectiveness assesses if the additional health gains are worth any additional costs by estimating differences in the quality and quantity of life, and the costs, from the point of intervention over observed but also longer (even lifetime) timelines, capturing the entire patient pathway through healthcare, e.g. from early-stage chronic kidney disease (CKD) through to dialysis or transplantation. Working with stakeholders to define the decision problem, merging evidence from a range of sources, including clinical trials complicated by limited follow-up and non-generalisable participants, surrogacy studies to estimate the intervention’s impact on longer-term kidney failure risk, quality of life data collected ideally using instruments sensitive to kidney disease progression and other real-world data are required to make extrapolations sufficiently far into the patient’s lifetime to capture kidney failure. Consideration of disadvantaged populations and how interventions may operate differently in certain groups may be indicated. Failure to capture competing risks of cardiovascular disease and death will bias estimates of kidney failure. Application of our tips, combined with an understanding of how decision-makers use cost-effectiveness results and information about factors like rarity and disease severity maximises the likelihood of new kidney treatments and care approaches being adopted
Drivers of cross-boundary land use and cover change in a megacity region: Evidence from the Guangdong–Hong Kong–Macao Greater Bay area
Get PDFMegacity regions mark a transformative phase of urbanisation, in which interconnected cities undergo land-use and land-cover change (LUCC) that extends beyond administrative boundaries. However, the drivers of cross-boundary LUCC remain insufficiently examined, particularly before the top-down regional integration. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) provides a clear empirical case, having experienced cross-boundary LUCC prior to its formal designation as a megacity region in 2018. This study builds a Landsat-derived LUCC and driver dataset for the GBA. Global and local spatial autocorrelation (Moran’s I and LISA) are used to characterise spatial structure and clustering, and geographically weighted regression identifies the socio-economic and environmental determinants of built-up expansion over 1980–2018, spanning the pre-reform decade and the post-1990 land-transfer era. Findings reveal that: (1) LUCC in the GBA already exhibited a cross-border, spatially networked expansion pattern before formal regional integration policies at the national level, with built-up area growth extending beyond core cities into decentralised urban nodes. Two prominent cross-border cores and one cross-administrative core emerged, suggesting that regional integration was co-led by market forces and local governments before an institutional framework was established. (2) Although the GBA showed a clear trend towards integrated development, urban expansion was highly uneven. Such spatial disparities were mainly driven by varying socioeconomic and natural factors, including gross domestic product, population growth, real estate investment, water resource proximity, and infrastructure development. These findings enhance understanding of megacity-region dynamics and offer insights from the GBA for cross-border urbanisation and sustainable spatial governance
Domain-adaptive diagnosis of Lewy Body disease with transferability aware transformer
No full textLewy Body Disease (LBD) is a common yet understudied form of dementia that imposes a significant burden on public health. It shares clinical similarities with Alzheimer’s disease (AD), as both progress through stages of normal cognition, mild cognitive impairment, and dementia. A major obstacle in LBD diagnosis is data scarcity, which limits the effectiveness of deep learning. In contrast, AD datasets are more abundant, offering potential for knowledge transfer. However, LBD and AD data are typically collected from different sites using different machines and protocols, resulting in a distinct domain shift. To effectively leverage AD data while mitigating domain shift, we propose a Transferability Aware Transformer (TAT) that adapts knowledge from AD to enhance LBD diagnosis. Our method utilizes structural connectivity (SC) derived from structural MRI as training data. Built on the attention mechanism, TAT adaptively assigns greater weights to disease-transferable features while suppressing domain-specific ones, thereby reducing domain shift and improving diagnostic accuracy with limited LBD data. The experimental results demonstrate the effectiveness of TAT. To the best of our knowledge, this is the first study to explore domain adaptation from AD to LBD under conditions of data scarcity and domain shift, providing a promising framework for domain-adaptive diagnosis of rare diseases
Spatio-temporal data fusion framework based on large language model for enhanced prediction of electric vehicle charging demand in smart grid management
Get PDFAccurate prediction of electric vehicle (EV) charging demand is pivotal for effective smart grid management and renewable energy integration. However, predicting spatio-temporal EV charging patterns remains challenging due to complex data fusion requirements arising from heterogeneous temporal, spatial, and contextual features, as well as difficulties in effectively integrating multiple modeling approaches. This paper introduces EV-STLLM, a novel spatio-temporal data fusion framework based on Large Language Model explicitly designed for accurate short-term EV charging demand forecasting through innovative integration of data-level and model-level fusion techniques. At the data level, a multi-source embedding module is developed to seamlessly fuse temporal features (e.g., time slots, weekdays), spatial heterogeneity (e.g., geographical location), and contextual charging behaviors into a unified representation via embedding convolutional network. At the model level, a large language model (LLM) is employed to capture global spatiotemporal dependencies, enhanced with Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning, substantially reducing computational costs while maintaining prediction robustness. Using a comprehensive real-world dataset comprising over 830,000 EV charging records across 16 districts and 331 subdistricts in Beijing, we validate EV-STLLM across multiple forecasting scenarios (district and subdistrict levels, one-step and two-step ahead predictions). Extensive comparative evaluations demonstrate that EV-STLLM consistently outperforms classical, graph-based, and deep learning baselines. Specifically, in one-step ahead district-level forecasting, EV-STLLM achieves up to a 15.41% reduction in MAE and a 53.51% reduction in MAPE compared to the leading baseline, underscoring its potential to significantly enhance data-driven smart grid operations
Remote working and the new geography of local service spending
Get PDFRemote working has rapidly become the new norm in many sectors, at least some of the time. Remote working changes where workers spend much of their time and the geographical location of demand, particularly for local personal services (LPS). Our main contribution is to systematically quantify this change for England and Wales using a new nationally representative survey of nearly 35,000 working age adults, which captures (pre-pandemic) LPS spending while at work and permanent changes in remote working. On average, our work shows neighbourhoods where people commute 20% less often experience a decline in LPS spending of 5%. There is a clear geographic pattern (the ”donut” effect) to these spending changes but our granular analysis shows that they are uneven: large decreases in LPS demand are concentrated in a small number of city-centre neighbourhoods, while increases in LPS demand around the periphery are more dispersed. Further analysis of neighbourhoods by geographical and socio-demographic characteristics shows the least affluent are most likely to benefit the least from remote work, increasing inequalit
Multiple-input, multiple-output modal testing of a Hawk T1A aircraft: a new full-scale dataset for structural health monitoring
Get PDFThe use of measured vibration data from structures has a long history of enabling the development of methods for inference and monitoring. In particular, applications based on system identification and structural health monitoring have risen to prominence over recent decades and promise significant benefits when implemented in practice. However, significant challenges remain in the development of these methods. The introduction of realistic, full-scale datasets will be an important contribution to overcoming these challenges. This article presents a new benchmark dataset capturing the dynamic response of a decommissioned BAE Systems Hawk T1A. The dataset reflects the behaviour of a complex structure with a history of service that can still be tested in controlled laboratory conditions, using a variety of known loading and damage simulation conditions. As such, it provides a key stepping stone between simple laboratory test structures and in-service structures. In this article, the Hawk structure is described in detail, alongside a comprehensive summary of the experimental work undertaken. Following this, key descriptive highlights of the dataset are presented, before a discussion of the research challenges that the data present. Using the dataset, non-linearity in the structure is demonstrated, as well as the sensitivity of the structure to damage of different types. The dataset is highly applicable to many academic enquiries and additional analysis techniques which will enable further advancement of vibration-based engineering techniques
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