Kingston University London is a public research university located within the Royal Borough of Kingston upon Thames, in South West London, England. Its roots go back to the Kingston Technical Institute, founded in 1899. It received university status in 1992, before which the institution was known as Kingston Polytechnic.Kingston has 16,820 students and a turnover of £192 million. It has four campuses situated in Kingston and Roehampton. The university specialises in the arts, design, fashion, science, engineering, and business and is organised into four faculties: Kingston School of Art, Faculty of Business and Social Sciences (which combines Kingston Business School and the School of Law, Social and Behavioural Sciences), Faculty of Health, Social Care and Education and Faculty of Science, Engineering and Computing. The Kingston Business School is CNAA MBA degree approved. In 2017, the university won The Guardian University Award for teaching excellence. Kingston is a member of the European University Association, the Association of Commonwealth Universities and University Alliance group..
P-cresol, indole and indole-3-acetic acid (IAA) are catabolites of amino acids, formed by the gut microbiome. Most of these aromatic hydrocarbon derivatives are excreted by the colon before reentering the body to form "exogenous" protein-bound uremic toxins (PBUTs), which aggravate chronic kidney disease (CKD). Removal efficiencies of these PBUT precursors from model phosphate-buffered saline solutions by three different surface-modified nanoporous carbon adsorbents (PCs) were studied. PCs were produced by physicochemical and/or acid base activation of carbonized rice husk waste. Removal rates achieved values of 32-96% within a 3 h contact time. High micro/mesoporosity and surface chemistry of the N- and P-doped biochars were established by N2 adsorption studies, SEM/EDS analysis, XPS and FT-IR-spectroscopy. The ammoxidized PC-N1 had the highest adsorption capacity (1.97 mmol/g for IAA, 2.43 mmol/g for p-cresol and 2.42 mmol/g for indole), followed by "urea-nitrified" PC-N2, whilst the phosphorylated PC-P demonstrated the lowest adsorption capacity for these solutes. These results do not correlate with the total pore volume values for PC-N2 (0.91 cm3/g) < PC-P (1.56 cm3/g) < PC-N1 (1.84 cm3/g), suggesting that other parameters such as the micropore volume (PC-N1 > PC-N2 > PC-P) and the interaction of surface chemical functional groups with the solutes play key roles in the adsorption mechanism. N-doped PC-N1 and PC-N2 have basic functional groups with higher affinity with acidic IAA and p-cresol. The ion-exchange mechanism of phenolic and indolic compound chemisorption by nanoporous carbon adsorbents, modified with surface N- and P-containing functional groups, has been proposed.
This paper uses a large individual-record-level dataset on sick leave to examine adult morbidity in the United Kingdom between 1850 and 1908. From 1859 onwards postal workers were eligible to receive a pension or gratuity when they retired or were forced to stop working due to ill health. Their pension application forms contain information on up to 10 years of sick leave taken prior to retirement by each retiree. These data have several advantages over previous sources of information on morbidity in this period. Sick leave was closely monitored by the Post Office's medical service, mitigating issues around the inflation of morbidity raised in the context of friendly society data; they cover the entirety of the United Kingdom; they include workers from the service sector as well as manufacturing occupations; and they include a greater proportion and number of women than previous sources. The paper discusses the nature of these data and the administrative systems that created them before examining the trends and modelling the determinants of sick leave. It concludes that postal workers became increasingly likely to take time off during this period but that the duration of sick leave remained stable. These changes were driven by changes to sick leave regulations; shifts in the composition of the workforce by age, occupation, gender, and location; and changes in the intensity of postal work.
This study presents the multidimensional style anchoring-dynamic feedback calibration (MSA-DFC) algorithm, designed to mitigate style drift and enhance consistency in generative artificial intelligence (AIGC) outputs across diverse generation modalities and long-sequence tasks. The algorithm constructs a text-visual multidimensional style primitive model that integrates semantic mood vectors, syntactic structure matrices, color distributions, and composition rules. These features are mapped into a unified cross-modal representation space through a modality adaptation network. A domain-adaptive style anchoring mechanism improves style adaptation accuracy, while a dynamic feedback calibration module suppresses style drift during long-sequence generation. To evaluate the algorithm, a hybrid dataset combining expanded public resources and self-constructed multi-scenario data was developed, along with a dual-dimensional evaluation system incorporating objective performance and subjective experience metrics. Experimental results show that MSA-DFC improves style similarity by 29.4% and reduces the style drift rate to as low as 3.2% compared to baseline methods. The algorithm also achieves a user satisfaction score of 88.7 and reduces task completion time by 27.4%, with all improvements being statistically significant (p<0.001). The proposed method outperforms mainstream models such as StyleGAN3 and ChatGLM-6B (fine-tuned) across multiple scenarios. This work addresses the core challenge of style control in AIGC and establishes a quantitative correlation between style consistency and user experience, providing both technical and theoretical support for the practical deployment of AIGC systems.
To address the limitations of traditional frame-based PnP algorithms—such as motion blur, limited dynamic range, and high computational complexity under high-speed motion and extreme illumination changes—this paper proposes the Event-PNP algorithm framework. This framework tightly couples asynchronous event streams with IMU data at the measurement model level, enabling high-precision real-time six-degree-of-freedom pose estimation. The algorithm establishes an event geometry model based on luminance constancy constraints, combining event triggering with pixel motion to avoid the temporal resolution loss inherent in traditional event frame accumulation. It employs IMU pre-integration techniques to jointly minimize event geometry errors and IMU residuals within a unified optimization framework. Through lightweight strategies such as incremental optimization and sparsification, the algorithm’s computational complexity is reduced to linear-time. Theoretically, an event-inertial fusion Fisher information matrix framework is established, deriving a closed-form lower bound expression for the Cramer-Rao inequality to provide a theoretical benchmark for performance evaluation. Experiments demonstrate that Event-PNP achieves rotational accuracy of 0.08° and translational accuracy of 1.2 cm, representing 35
Artificial neural networks (ANNs) are widely used to approximate nonlinear mappings, yet their ability to capture thermodynamic behaviour in dynamic physical systems remains insufficiently characterised. This study investigates how representational capacity influences surrogate modelling accuracy for a crank-angle-resolved internal combustion engine (ICE) simulation with a maximum dynamic state dimension of six. Two feedforward ANN configurations are evaluated: a low-capacity 5-5 architecture containing 84 trainable parameters and a high-capacity 25-25-25 architecture containing 1554 parameters (18.5 & times; larger). Both networks approximate the nonlinear mapping from five embedded operating parameters to four peak thermodynamic outputs (maximum pressure, pressure phasing, maximum temperature, and temperature phasing). Evaluation across 53,178 operating points demonstrates that the high-capacity configuration reduces root mean squared error by factors of 30-50 & times; relative to the low-capacity network, decreasing peak temperature error from 17.68 K to 0.36 K and peak pressure error from 0.116 MPa to 0.0025 MPa. Although both models achieve coefficients of determination exceeding 0.99, the low-capacity network exhibits heavy-tailed residual distributions and regime-dependent error amplification, whereas the high-capacity model reduces both central dispersion and extreme-case error. These results demonstrate that high correlation alone does not guarantee engineering reliability in nonlinear thermodynamic systems. Distribution-level analysis, including percentile and extreme-case characterisation, is required to evaluate engineering robustness. The findings provide a quantitative framework linking ANN capacity, nonlinear dynamic system representation, and predictive robustness.
