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Do board meetings matter in the insurance industry?
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The data that has been used is confidential.This study examines the link between formally scheduled board meetings, profitability, and solvency in the United Kingdom’s (UK) property-casualty insurance industry. A panel data design using 83 UK insurers writing property casualty insurance for the period 2004/5 to 2013/4 is employed. The study finds that increasing the number of board meetings scheduled each year enhances overall attendance rates. However, outside directors with financial experience have relatively better attendance rates than their counterparts with less technical expertise. The study also finds that overall board meeting attendance and directorate turn-out at strategy and remuneration meetings improve profitability but not solvency. In addition, board meeting attendance by outside directors falls when prior period profitability is sound, suggesting a ’complacency-effect’ among non-executives. The lack of significance between solvency and both total and outside director attendance also hints at a ’dependency-effect’, whereby boards rely on professional managers (actuarial technocrats) to optimize financial strength and condition. Our results have implications for insurers and regulators in deciding on the suitability of candidates applying for board-level positions.None
The effect of storage conditions and computer model simplification on the thermal state of the RBMK-1500M2 cask
Get PDFInterim storage of spent nuclear fuel is a very important part of the overall nuclear power generation cycle. At Ignalina NPP, spent nuclear fuel is stored in interim storage facilities in specially designed casks before being transferred to a geological repository. The internal structure of spent nuclear fuel casks and the processes involved are quite complex. Therefore, simplifying and optimizing simulations for evaluating decay heat removal from the cask is worthwhile. In this paper, the effect of computer model simplifications on the thermal characteristics of the CONSTOR RBMK-1500/M2 cask stored in building-type and open-type storage facilities is presented. The modeling was carried out using the ANSYS Fluent code. The analysis showed a substantial impact of solar insolation. Also, in the case of the homogenization of the SNF load in the basket, higher temperatures are obtained compared with the case when detailed modeling of the internal basket structure is performed. Hence, it was demonstrated that the homogenization model can be used in safety assessment as a conservative approach for the modeling of decay heat removal from the cask
Optimisation of Surface Modification of Industrial Recycled Carbon Fibres for the Adsorption of Antibiotics from Aquaculture
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Data have been made available in Brunel University of London’s repository via Brunel Figshare database at https://doi.org/10.17633/rd.brunel.28344632.v1.This study investigates the use of recycled carbon fibers as precursors for activated adsorbents and applies nitric acid modification to enhance their selectivity toward two antibiotics: ciprofloxacin and oxytetracycline. A design of experiments was employed to optimize key modification parameters: acid concentration (1 – 6 M), hold time (16 – 24 h), and temperature (20 – 60°C), and their influence on adsorption. The modified adsorbents were characterized using N2 adsorption isotherms, Boehm titrations, and potentiometric titrations to determine porosity, acidity/basicity, and point of zero charge. Optimal conditions were identified as 1 M HNO3, 16 h, and 28°C. Under these conditions, modified adsorbents achieved adsorption capacities of 9.63 mg/g for ciprofloxacin and 7.72 mg/g for oxytetracycline. While ciprofloxacin removal decreased by 7% compared with unmodified counterparts, this reduction was not statistically significant. By contrast, oxytetracycline removal improved by 21%, a statistically significant increase. The superior uptake of oxytetracycline was attributed to its greater hydrophilicity and enhanced affinity for the oxidized, hydrophilic surface. Overall, acidic surface modification represents a promising strategy to improve the performance of recycled carbon fiber-derived adsorbents, particularly for tetracycline antibiotics. Further investigation is required to assess whether oxidative treatment methods can improve the removal of fluoroquinolones.This work has been funded by the UK’s Engineering and Physical Sciences Research Council (EPSRC), as part of the UKRI, via the EPSCR Doctoral Training Partnership (project reference EP/T518116/1)
Analytical Solution to Optimal Distributed Bipartite Consensus for Heterogeneous Multi-Agent Systems on Coopetition Networks: A Fast Convergent Algorithm
Get PDFThis note makes the first attempt to investigate the optimal bipartite consensus problem in the general case for heterogeneous multi-agent systems with cooperative-competitive interactions, specifically those containing a spanning tree. Unlike traditional control protocols based on the gradient descent method, our proposed optimal bipartite algorithm demonstrates a fast superlinear convergence speed. The key innovation of this method lies in the design of a fully distributed optimal controller, which is based on a distributed observer utilizing local neighbor information. The analytical solution for the optimal controller is derived with the help of the Riccati equation, aligning with classical optimal control theory. This approach unifies the design method for both the bipartite consensus problem and the well-studied consensus problem. Additionally, the proposed optimal algorithm can be directly extended to homogeneous systems. A numerical example is provided to demonstrate the effectiveness and rapid convergence of our control algorithm.This work was supported by the Original Exploratory Program Project of National Natural Science Foundation of China (62450004), the National Nat-ural Science Foundation of China (62103240), the Joint Funds of the National Natural Science Foundation of China (U23A20325), the Major Basic Research of Natural Science Foundation of Shandong Province (ZR2021ZD14), the Youth Foundation of Natural Science Foundation of Shandong Province (ZR2021QF147) and the High-level Talent Team Project of Qingdao West Coast New Area (RCTD-JC-2019-05)
Democracy by algorithm? Public attitudes towards AI in parliamentary decision-making in the UK and Japan
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Full replication data and code are available from the Harvard Dataverse, at: https://doi.org/10.7910/DVN/D7OETJ .Supplementary data are available online at: https://academic.oup.com/pa/advance-article/doi/10.1093/pa/gsaf050/8300196?login=false#supplementary-data .Parliaments are beginning to experiment with artificial intelligence (AI), but public acceptance remains uncertain. We examine attitudes to AI in two parliamentary democracies: the UK (n = 990) and Japan (n = 2117). We look at two key issues: AI helping Members of Parliament (MPs) make better decisions and AI or robots making decisions instead of MPs. Using original surveys, we test the roles of demographics, institutional trust, ideology, and attitudes toward AI. In both countries, respondents are broadly cautious: support is higher for AI that assists representatives than for delegating decisions, with especially strong resistance to delegation in the UK. Trust in government (and general social trust in Japan) increases acceptance; women and older respondents are more sceptical. In the UK, right-leaning respondents are more supportive, whereas ideology is weak or negative in Japan. Perceptions of AI dominate: seeing AI as beneficial and feeling able to use it raises support, while fear lowers it. We find that legitimacy for parliamentary AI hinges not only on safeguards but on alignment with expectations of representation and accountability.This research was funded by the UKRI/ESRC (grant number ES/W011913/1) and the JSPS (grant number JPJSJRP 20211704)
Windows of fantasy
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe research set out in this thesis explored science-fiction and fantasy posters, specifically those related to films and television shows, from the perspective of their owners, examining their potential as sources of cultural significance and meaning. The research explored these in terms of the components (e.g., content) of the poster, the room they are displayed in, their placement, the media texts (films or television shows) they reference, morals, behaviour, identity, sense of self, well-being (through emotional responses), self-expression, other opinions and levels of investment. Given that science fiction and fantasy are otherworldly and imaginative genres, exploring their posters offers a unique lens, akin to exploring the fantastical worlds they depict. Data were collected through an online survey and semi-structured interviews with adult science-fiction and fantasy film and television show poster owners. The significance and meaning of these posters were framed by two conceptual models: ‘The Three Significances’, aesthetics, functionality, and significance (both spatial and personal), and ‘The Big Three’, content, design, and colour. Among these, content held the greatest significance for owners. Posters served as tools for self-expression, reflecting their owners' identities, affinities, and convictions, while also reinforcing their connection to films and TV shows they reference. Rather than shaping moral beliefs, posters were more likely to reinforce their sense of self and fan identity and evoke emotional responses. The space in which posters are displayed shapes their meaning and significance, just as posters influence the atmosphere and function of that space. Official posters tend to be accurate depictions of the media text, while fan art posters offer creative reinterpretations of already reimagined worlds. Additionally, the type of poster, physical or digital, plays a crucial role in shaping ‘The Three Significances’, influencing its availability (both in terms of content and accessibility) and its presentation/display (how it is showcased and experienced)
Enhancing graph convolutional networks with an efficient k-hop neighborhood approach
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Data will be made available on request.Graph Convolutional Network (GCN) has emerged as a powerful model for network data analysis and representation since it effectively utilizes neighborhood information via message propagation. However, existing GCNs cannot efficiently utilize the deeply propagated k-hop neighborhood in-formation when k becomes large due to over-smoothing issues, which significantly constrains their capacity for representation learning to the deep structure of the graph. Motivated by this critical issue, this paper proposes an efficient k-hop Neighborhood Enhanced Graph Convolutional Network (NE-GCN) model is proposed in this paper, which is developed based on two-fold main ideas: a) deriving an efficient -hop neighborhood enhancement scheme from the perspective of path-aware embedding, which enables each ego node to preserve the -hop neighborhood information in polynomial form; and b) building a nearest-neighborhood constraint into the objective function for stressing one-hop neighborhood information, thus relieving the impacts by redundant nodes or noisy links. Empirical results from four benchmark datasets against 11 state-of-the-art models clearly illustrate the superior performance of the proposed kNE-GCN in missing link estimation in undirected weighted graphs.This research is supported in part by the National Natural Science Foundation of China under grants 62372385, and 62272078, and in part by the Chongqing Natural Science Foundation, China under grant CSTB2023NSCQ-LZX0069
H<inf>∞</inf> PID Control for Singularly Perturbed Systems With Randomly Switching Nonlinearities Under Dynamic Event-Triggered Mechanism
No full textIn this article, the observer-based H∞ proportional-integral-derivative (PID) control problem is studied for a class of singular perturbed systems with randomly switching nonlinearities. During the modeling process, a set of binary random sequences is introduced to describe the random behaviors of the nonlinear switching encountered in engineering practice. Considering the singular perturbation parameter (SPP), an observer-based H∞ PID controller is constructed for the concerned singularly perturbed systems (SPSs), where the state information is estimated. To improve communication efficiency and reduce resource waste, a dynamic event-triggered mechanism is employed during the measurement transmission. Meanwhile, an SPP-dependent Lyapunov-Krasovskii function is utilized to derive conditions under which the closed-loop system is guaranteed to be stochastically stable with the desired H∞ performance under a specified upper bound on the SPP. All the desired observer and controller gains are determined by solving a set of matrix inequalities. Finally, the effectiveness and superiority of the suggested PID control method are verified through a simulation example.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
An Ensemble Learning for Automatic Stroke Lesion Segmentation Using Compressive Sensing and Multi-Resolution U-Net
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The dataset used in this study is publicly available in this address: https://www.isles-challenge.org (accessed on 29 July 2025).A stroke is a critical medical condition and one of the leading causes of death among humans. Segmentation of the lesions of the brain in which the blood flow is impeded because of blood coagulation plays a vital role in drug prescription and medical diagnosis. Computed tomography (CT) scans play a crucial role in detecting abnormal tissue. There are several methods for segmenting medical images that utilize the main images without considering the patient’s privacy information. In this paper, a deep network is proposed that utilizes compressive sensing and ensemble learning to protect patient privacy and segment the dataset efficiently. The compressed version of the input CT images from the ISLES challenge 2018 dataset is applied to the ensemble part of the proposed network, which consists of two multi-resolution modified U-shaped networks. The evaluation metrics of accuracy, specificity, and dice coefficient are 92.43%, 91.3%, and 91.83%, respectively. The comparison to the state-of-the-art methods confirms the efficiency of the proposed compressive sensing-based ensemble net (CS-Ensemble Net). The compressive sensing part provides information privacy, and the parallel ensemble learning produces better results.This research received no external funding
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