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Quadrant categorization of spillover determinants of sovereign risk of BRICIT nations: a Bayesian approach
Full text linkThis study investigates the determinants that drive the volatility of the credit default swaps (CDS) of BRICIT (Brazil, Russia, India, China, Indonesia, and Turkey) nations as a proxy measure for sovereign risk. On the existence of cointegration, an unrestricted error correction model integrated with the autoregressive distributed lag (ARDL) model
is applied to measure the short-run and long-run dynamics empirically. The study utilizes the Bayesian global vector autoregression methodology for cross-border spillover estimation. The study also suggests a strategy for policymakers for quadrant categorization to mitigate risk arising from cross-border spillover. The result of ARDL indicates that the global macroeconomic variables affect the BRICIT CDS more than domestic macroeconomic determinants, with Indian CDS being the most sensitive to Fed tapering. Notably, China’s CDS is the most sensitive to shocks, with the CDS volatility primarily driven by China’s geopolitical risk. Russian CDS is more sensitive to real effective
exchange rates due to severe ruble depreciation than crude oil, despite Russia being a major oil exporter. The quadrant categorization indicates that the Indonesian stock market index is most interconnected with BRICIT CDS, while the Turkish long-term interest rates send the highest intensity spillover across BRICIT nation
Synthetic-polymer-assisted antisense oligonucleotide delivery: targeted approaches for precision disease treatment
Full text linkThis review explores the recent advancements in polymer-assisted delivery systems for antisense oligonucleotides (ASOs) and their
potential in precision disease treatment. Synthetic polymers have shown significant promise in enhancing the delivery, stability, and
therapeutic efficacy of ASOs by addressing key challenges such as cellular uptake, endosomal escape, and reducing cytotoxicity.
The review highlights key studies from the past decade demonstrating how these polymers improve gene silencing efficiencies, particularly in cancer and neurodegenerative disease models. Despite the progress achieved, barriers such as immunogenicity, delivery limitations, and scalability still need to be overcome for broader clinical application. Emerging strategies, including stimuli-responsive polymers and advanced nanoparticle systems, offer potential solutions to these challenges. The review underscores the transformative potential of polymer-enhanced ASO delivery in personalised medicine, emphasising the importance of continued innovation to optimise ASO-based therapeutics for more precise and effective disease treatment
Monitoring bank risk around the world using unsupervised learning
Full text linkThis paper provides a transparent and dynamic decision support tool that ranks clusters of listed banks worldwide by riskiness. It is designed to be flexible in updating and editing the values and quantities of banks, indicators, and clusters. For constructing this tool, a large set of stand-alone and systemic risk indicators are computed and reduced to fewer representative factors. These factors are set as features for an adjusted version of a nested k-means algorithm that handles missing data. This algorithm gathers banks per clusters of riskiness and ranks them. The results of the individual banks’ multidimensional clustering are also aggregable per country and region, enabling the identification of areas of fragility. Empirically, we rank five clusters of 256 listed banks and
compute 72 indicators, which are reduced to 12 components based on 10 main factors, over the 2004–2024 period. The findings emphasize the importance of giving special consideration to the ambiguous impact of banks’ size on systemic risk measures
Autism-Friendly Schools: Including the Voices of Autistic Students in Primary and Post-Primary Education in Ireland.
Full text linkAutism is a lifelong developmental disability or difference which relates to how a person communicates and interacts with others, and how they experience the world around them (AsIAm, 2025). Under Article 24 of the United Nations Convention on the Rights of Persons with Disabilities, autistic1 children have the right to be fully included in mainstream
educational settings, with individualised supports and accommodations in place to maximise their academic and social development at school (United Nations, 2006). Despite this, many autistic children continue to experience exclusion and barriers to inclusive education which
can result in lifelong difficulties. Notably, the views and experiences of autistic children and young people are not currently included in the development of educational policy and practices for them.
Inclusion in education refers to the process of identifying and removing barriers to the presence, participation and achievement of all students (Ainscow et al., 2006). This study aimed to undertake the first systematic investigation of the experiences of autistic students in primary and post-primary schools, in both mainstream and special school provision, in
Ireland to create new knowledge that would lead to clearly implementable supports for inclusive education policy and practice in Ireland. This study also aimed to examine the wider school community’s understanding of autism, and attitudes to inclusion, to provide an evidence base for educational policies and guide inclusive education practices
A Framework for an Autism-Friendly School Informed by Autistic Students.
Full text linkThis framework for an Autism-Friendly School is grounded in the findings of a comprehensive research study conducted over a 2-year period between 2023 & 2025. The study captured the missing voices of primary and post-primary school-aged autistic children about their
experiences of inclusion in education in the Republic of Ireland. Parents and school staff also informed the study. A summary report, including policy recommendations, and a number of peer reviewed publications (in progress) have emerged from this research, in addition to the
framework. The purpose of the framework is to serve as a practical resource for schools to help them to embed an autism-friendly culture in their school. Ten principles as set out below have been developed from the themes emerging from the study. These principles could be
adopted by any primary or post-primary school with minimal cost. Each of the principles are underpinned by a series of specific actions. These actions are examples of how the ten principles could be embedded within schools but they are not exhaustive, as each school will have their own specific contexts and can add further actions to the list provided
Innovations in diversity and equity in social health research in dementia - Recommendations from the JPND INTEREST Working Group March, 2025
Full text linkEXECUTIVE SUMMARY
Purpose
The societal challenge of dementia is the expected steep increase in the number of people with dementia in the coming years, coupled with a decrease in the workforce of professional caregivers. Despite progress in care and support, many health and social care needs remain unmet due to inequity, lack of individualized treatment, and a gap between research and practice implementation. The INTEREST Working Group aims to address these issues using a multi-level approach and an overarching framework of social health and intersectionality.
Methodology
The report employs a comprehensive literature review, public person involvement (PPI), and reviews of intervention guidelines, assistive technology, and policy. The methodology integrates findings from various sub-groups focusing on inequity, technology, and policy.
Findings
1. Inequity in Healthcare: Ethnic minorities and socioeconomically disadvantaged populations are underrepresented in dementia research and care, leading to unmet needs.
2. Limited Tailoring of Interventions: Innovations are often generic and not tailored to individual needs, requiring more culturally respectful and personalized approaches.
3. Implementation Gap: Innovations in support and care are not embedded in routine care, necessitating better connections between research, education, and practice. XXX
Recommendations
1. Strategic Policy and Best-Practice Guidelines: Develop policies and guidelines to address identified gaps.
2. Innovative Research Frameworks: Create frameworks to support transdisciplinary research for diverse groups.
3. Increased Research Capacity: Build capacity in research, including early career researchers and public participation.
Conclusion and Call to Action
The INTEREST Working Group proposes mechanisms for addressing gaps in dementia care through strategic policy, best-practice guidelines, innovative research frameworks, and increased research capacity. Future steps include implementing these recommendations to achieve equitable dementia research, care, and treatment. Policymakers, researchers, healthcare providers, and community organisations must collaborate to implement these recommendations. By doing so, we can create a more supportive and inclusive environment for people living with dementia and their carers, ultimately improving their quality of life and well-being
Improving Portfolio Management Using Clustering and Particle Swarm Optimisation
Full text linkPortfolio management, a critical application of financial market analysis, involves optimising asset allocation to maximise returns while minimising risk. This paper addresses the notable research gap in analysing historical financial data for portfolio optimisation purposes. Particularly, this research examines different approaches for handling missing values and volatility, while examining their effects on optimal portfolios. For this portfolio optimisation task, this study employs a metaheuristic approach through the Swarm Intelligence algorithm, particularly Particle Swarm Optimisation and its variants. Additionally, it aims to enhance portfolio diversity for risk minimisation by dynamically clustering and selecting appropriate assets using the proposed strategies. This entire investigation focuses on improving risk-adjusted return metrics, like Sharpe, Adjusted Sharpe, and Sortino ratios, for single-asset-class portfolios over two distinct classes of assets, cryptocurrencies and stocks. Considering relatively high market activity during pre, during and post-pandemic conditions, experiments utilise historical data spanning from 2015 to 2023. The results indicate that Sharpe ratios of portfolios across both asset classes are maximised by employing linear interpolation for missing value imputation and exponential moving average smoothing with a lower smoothing factor (α). Furthermore, incorporating assets from different clusters significantly improves risk-adjusted returns of portfolios compared to when portfolios are restricted to high market capitalisation assets
Financial perceptions and AI infringement risks
Full text linkArtificial intelligence (AI)-related intellectual property (IP) infringement involves the unauthorized use of copyrighted materials during model training and the creation of content that may violate copyright, trademark, or patent laws. This phenomenon presents critical financial risks for businesses, ranging from reputational harm and erosion of brand equity to potential litigation, regulatory scrutiny, and increased investor uncertainty. This study explores how to understand this emergent risk and the associated implications. To do so, we apply social capital theory to an analysis of 10,447 Chinese social media users’ reactions to China’s first AI-generated voice infringement lawsuit. Our findings suggest that out-tie social capital (exposure to diverse networks) tends to promote neutral or positive views, while in-tie social capital (strong, closeknit communities) initially encourages favourable attitudes but shifts toward ethical
and risk concerns when potential financial damages are perceived. Our study, thus, highlights the interplay between social perception and corporate financial considerations in an era where AI increasingly shapes economic opportunities and liabilities
Insurance Risk Premium Development with Model Risk
Full text linkAccurate risk premium prediction is critical for competitiveness and growth in general insurance business. Traditional approaches focus on clustering risks into well-defined groups to improve prediction accuracy, but practical challenges such as poorly defined risk classes and unexpected model risks complicate this process.
This thesis tackles diverse model risks in risk premium prediction using a Bayesian framework. Unlike classical actuarial methods that rely solely on data, Bayesian models incorporate parameter knowledge, offering flexibility in handling erroneous data issue. We leverage this advantage to link Bayesian parametric/ nonparametric frameworks with state-of-art strategies for managing incomplete data issues, such as Missingness at Random (MAR) and Non-Differential Berkson (NDB) mismeasurement.
Additionally, we address other key analytical challenges, including heterogeneity, convolution, and scalability.
The first part of this thesis focuses on Bayesian parametric frameworks, comparing Bayesian partial pooling with traditional error correction method such as Simulation Extrapolation (SIMEX). The second part extends to the Bayesian nonparametric (BNP) framework, investigating the efficiency of Bayesian parameter-free clustering while addressing incomplete data using techniques such as data augmentation
and Gustafson correction. We develop a hybrid Dirichlet Process Mixture
(DPM) model and compare it with Bayesian hierarchical models and other classical actuarial approaches. The originality of this thesis lies in leveraging existing state-of-the-art approaches and pushing the boundaries of their applicability to a broader analytical framework, encompassing challenges such as heterogeneity, convolution
error, scalability, missingness, and mismeasurement.
Based on the combined use of Bayesian parametric and nonparametric models trained on multiple insurance datasets, a critical insight from our study is that correction performance depends on the alignment between two conditional variances in the Gustafson framework—one conditioned on the true covariate and the other on the chosen covariate to approximate the true covariate. We introduce the concept of
a scaling factor for the first time to measure this alignment, applying it in calibrating the MCMC simulations. Overall, we believe that this thesis enhances the practical application of Bayesian tools for actuaries. Key innovations include:
1. Integrating data augmentation and Gustafson correction with Bayesian predictive modeling frameworks, leveraging unique prior knowledge of variance in the correction process.
2. Introducing log-normal and log-skewnormal convolution techniques for risk premium modeling, enhancing theoretical reliability.
3. Marking the first instance of integrating advanced Bayesian techniques with scalable methodologies tailored for risk premium prediction
Fabrication of Multifunctional Layer-by-Layer Assembly Nanocomposite Coatings for Bone Tissue-Engineered Scaffolds
No full textThe electrostatic Layer-by-Layer (LbL) assembly process holds promise in customising the surface and bulk properties of bone tissue-engineered scaffolds. This research aimed to create functionalised, mechanically robust multilayer nanocomposite coatings on highly porous 3D templates to enhance in vivo functionality. The nanocomposite LbL-coated scaffolds were fabricated using 1 wt.% solutions of poly-L-lysine (+), polyglutamic acid (-), polydiallydimethylammonium (+), and 0.5 wt.% montmorillonite-nanoclay (-). Optimised coating materials system and process parameters under dynamic LbL-coating conditions were employed. Deposition of the nanocomposite LbL-coating on polyureathane (PU) scaffolds maintained the highly interconnected structure of the PU, resulting in scaffolds with 79 ± 1.1% porosity. This process led to a 260-fold enhancement in mechanical properties, increasing from 0.082 to 21.64 MPa under fully optimised conditions with 100 quadlayers deposited. To enhance mechanical properties under hydration, chemical crosslinking was applied to the optimised LbL-coated scaffolds using a 1 wt.% aqueous tannic acid (TA) solution. The 1 wt.% TA crosslinking exhibited optimal behaviour, with hydrated TA crosslinked scaffolds retaining 75% of their mechanical performance post-hydration. SEM analysis confirmed that a uniform and robust multilayer nanocomposite coating was achieved using LbL process. Surface hydrophilicity analysis showed a decrease in the contact angle from 138.8 ± 2.85° for uncoated to 70.28 ± 3.10° for crosslinked scaffolds. In vitro studies where, the scaffolds seeded with pre-osteoblast cells (MC3T3-E1) showed minimal evidence of cytotoxicity, with cell viability > 100% in crosslinked scaffold extracts after 48 h and 97.31% ± 2.17% cell attachment to the scaffold after 24. Increased cell metabolic activity was observed over 14 days, indicating proliferation. SEM imaging confirmed uniform cell layers with cell filopodia interacting with the scaffold pore surface on Days 7 and 14.
The multifunctional crosslinked LbL-coated scaffolds demonstrated in this study represent a promising approach for enhancing the physiomechanical properties of open-cell structures with an initial positive impact on pre-osteoblast cellular responses for bone tissue engineering applications