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Enhancing adhesive bonding and mechanical properties of composite sandwich panels through atmospheric plasma activation
No full textThis study investigates the effect of atmospheric plasma activation (APA) treatment on the thermomechanical performance of adhesively bonded skins of composite sandwich panels. The investigations show that APA treatment enhances surface wettability through the water contact angle measurements, and adhesion between the skin and honeycomb core of the panels under mechanical tests. Specifically, APA treatment results in an 11% increase in flatwise tensile strength and a 12% enhancement in the impact strength of the sandwich laminates. Flatwise tensile tests reveal superior tensile strength and toughness in APA-treated specimens, with fracture patterns suggesting more robust adhesion. Charpy impact test results confirm increased energy absorption, reflecting better mechanical resilience. Scanning electron microscopy (SEM) and dynamic mechanical analysis (DMA) techniques are utilized to validate these findings. Overall, APA treatment proves to be an effective technique for enhancing the performance of composite sandwich panels, offering improved adhesion, strength, and impact resistance. This approach holds significant promise for advancing high-performance composite materials in various engineering applications.
Highlights
APA treatment enhances wettability and adhesion in sandwich panels.
Improved adhesive distribution between skin-core after sandwich manufacturing.
11% increase in tensile strength and 12% in impact strength with APA.
Better toughness and energy absorption via SEM and DMA support in APA
Revisiting McFadden’s correction factor for sampling of alternatives in multinomial logit and mixed multinomial logit models
Get PDFWhen estimating multinomial logit (MNL) models where choices are made from a large set of available alternatives computational benefits can be achieved by estimating a quasi-likelihood function based on a sampled subset of alternatives in combination with ‘McFadden’s correction factor’. In this paper, we theoretically prove that McFadden’s correction factor minimises the expected information loss in the parameters of interest and thereby has convenient finite (and large sample) properties. That is, in the context of Bayesian estimation the use of sampling of alternatives in combination with McFadden’s correction factor provides the best approximation of the posterior distribution for the parameters of interest irrespective of sample size. As sample sizes become sufficiently large consistent point estimates for MNL can be obtained as per McFadden’s original proof. McFadden’s correction factor can therefore effectively be applied in the context of Bayesian MNL models. We extend these results to the context of mixed multinomial logit models (MMNL) by using the property of data augmentation in Bayesian estimation. McFadden’s correction factor minimises the expected information loss with respect to the augmented individual-level parameters, and in turn also for the population parameters characterising the shape and location of the mixing density in MMNL. Again, the results apply to finite and large samples and most importantly circumvent the need for additional correction factors previously identified for estimating MMNL models using maximum simulated likelihood. Monte Carlo simulations validate this result for sampling of alternatives in Bayesian MMNL models
Magnetic domain wall and skyrmion manipulation by static and dynamic strain profiles
Get PDFMagnetic domain walls and skyrmions in thin film micro- and nanostructures have been of interest to a growing number of researchers since the turn of the millennium, motivated by the rich interplay of materials, interface and spin physics as well as by the potential for applications in data storage, sensing and computing. This review focuses on the manipulation of magnetic domain walls and skyrmions by piezoelectric strain, which has received increasing attention recently. Static strain profiles generated, for example, by voltage applied to a piezoelectric-ferromagnetic heterostructure, and dynamic strain profiles produced by surface acoustic waves, are reviewed here. As demonstrated by the success of magnetic random access memory, thin magnetic films have been successfully incorporated into complementary metal-oxide-semiconductor back-end of line device fabrication. The purpose of this review is therefore not only to highlight promising piezoelectric and magnetic materials and their properties when combined, but also to galvanise interest in the spin textures in these heterostructures for a variety of spin- and straintronic devices
Copula-Based Risk Aggregation and the Significance of Reinsurance
Get PDFInsurance companies need to calculate solvency capital requirements in order to ensure that they can meet their future obligations to policyholders and beneficiaries. The solvency capital requirement is a risk management tool essential for addressing extreme catastrophic events that result in a high number of possibly interdependent claims. This paper studies the problem of aggregating the risks coming from several insurance business lines and analyses the effect of reinsurance on the level of risk. Our starting point is to use a hierarchical risk aggregation method which was initially based on two-dimensional elliptical copulas. We then propose the use of copulas from the Archimedean family and a mixture of different copulas. Our results show that a mixture of copulas can provide a better fit to the data than an individual copula and consequently avoid over- or underestimation of the capital requirement of an insurance company. We also investigate the significance of reinsurance in reducing the insurance company’s business risk and its effect on diversification. The results show that reinsurance does not always reduce the level of risk, but can also reduce the effect of diversification for insurance companies with multiple business lines
Book Review: Refashioning Race: How Global Cosmetic Surgery Crafts New Beauty Standards By Alka Vaid Menon
No full textHow to build resilient community energy systems? Lessons from Malawi and Ethiopia
Get PDFThis paper defines the notion of realising resilient community energy systems (R-CESs) through community capital to withstand unforeseen natural hazards, climate change induced risks and socio-political disruptions. It evaluates the interrelationship between different stages of CES project implementation with the development of the community's resilience in the form of social, human, economic, physical and natural capital. This study employs empirical research by carrying out case study analysis of CES projects deployed in risk-prone regions of Malawi and Ethiopia. Three CES projects, two in Malawi and one in Ethiopia, have been examined through qualitative analysis of data collected through semi-structured interviews with CES project stakeholders. Case studies analysed the role of different stakeholders in planning, installation, and operation of projects and the evolution of the community's resilience during phases of project implementation. In-depth critical analysis of cases demonstrates how a community's evolved resilience in different forms of community capital enables it to cope with unforeseen shocks/disruptions encountered over the period of CES operation. Comparative analysis of cases proposed the novel R-CES framework defining seven key components of community capital to realise a R-CES in practice. The proposed framework provides recommendations and best practices to CES project developers, managers and community representatives to implement CES projects in a way that strengthens community capital to thus realise a resilient community and sustainable infrastructure
Fracture risk assessment in metabolic syndrome in terms of secondary osteoporosis potential. A narrative review
Get PDFOsteoporosis is a major global public health problem with the associated bone fractures contributing significantly to both morbidity and mortality. In many countries, osteoporotic fractures will affect one in three women and one in five men over the age of 50. Similarly, diabetes, obesity, and metabolic syndrome (MetS) are among the leading public health problems due to their worldwide prevalence and burden on health budgets. Although seemingly disparate, metabolic disorders are known to affect bone health, and the interaction between fat and bone tissue is increasingly well understood. For example, it is now well established that diabetes mellitus (both type 1 and 2) is associated with fracture risk. In this narrative review, we focus on the potential link between MetS and bone health as expressed by bone mineral density and fracture risk. This narrative review demonstrates the association of MetS and its components with increased fracture risk, and also highlights the need for fracture risk assessment in patients with obesity and MetS
DYNAPARC: AI-Driven Predictive Path Failure Management for Industrial IoT-Fog Networks
Get PDFThe increasing adoption of IoT-Fog networks in industrial environments demands resilient systems to meet stringent Quality-of-Service (QoS) requirements. Network failures disrupt critical processes and degrade QoS, necessitating innovative predictive failure management. This paper presents the Dynamic Resilient Path Recovery (DYNAPARC) system, an AI-centric solution leveraging Software-Defined Networking (SDN) to predict and mitigate failures in industrial IoT-Fog networks (IIoT). DYNAPARC integrates AI-based reliability prediction model with SDN's programmable architecture and routing protocols to enhance resilience. A hybrid approach combines proactive and reactive methods: secondary paths are pre-installed (proactively) for immediate failover during primary link failures, while new alternative paths are dynamically calculated in real-time (reactively) for multiple failures, ensuring adaptive routing. To quantify the system’s performance, a novel Network Performance Score (N) measures QoS under failure conditions. Simulations show that DYNAPARC maintains an N score above 0.975135 before and after failures, outperforming traditional reactive and proactive methods. Integrating machine learning in the SDN controller significantly reduces packet loss by selecting the most reliable paths. These results highlight the potential of AI-driven prediction and SDN to achieve predictive reliability, ensuring superior resilience, fast recovery, and efficient traffic management in fog-based IIoT environments
Planetary Biostyles: Community-Making and Futures Design in the Age of Extremes
Get PDFThis innovative book addresses what ‘life’ is in scholarship and public culture, explores how it has been valued in the Anthropocene since the birth of critical theory, and designs a new approach to understanding biographical styles of life, or ‘biostyles’
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