13463 research outputs found
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Corruption and Default Risk: Global Evidence
Full text linkThe extant literature explores the consequences of corruption on firms’ growth and survival. However, its impact on default risk
remains unexplored. On the basis of a sample of 189,109 firm-years from 2004 to 2021 across 47 countries, our study reveals that a
one standard deviation increase in corruption is associated with an 11.3% increase in default risk. Our channel analysis identifies
information asymmetry and managerial risk-taking as key mechanisms through which corruption influences default risk. This
adverse effect is particularly pronounced in countries with opaque information environments, weak governance frameworks and
inadequate external monitoring of firms. We further highlight the detrimental impact of corruption on firms’ borrowing costs
and banks’ loan performance. Our study emphasizes the importance of enhancing information transparency and implementing
stringent control mechanisms as a basis of mitigating corruption’s detrimental effects across a range of different socio-political
contexts
Enhancing Small Object Detection in Resource-Constrained ARAS Using Image Cropping and Slicing Techniques
Full text linkPowered two-wheelers, such as motorcycles, e-bikes, and e-scooters, exhibit disproportionately high fatality rates in road traffic incidents worldwide. Advanced Rider Assistance Systems (ARAS) have the potential to enhance rider safety by providing real-time hazard alerts. However, implementing effective ARAS on the resource-constrained hardware typical of micromobility vehicles presents significant challenges, particularly in detecting small or distant objects using monocular cameras and lightweight convolutional neural networks (CNNs). This study evaluates two computationally efficient image preprocessing techniques aimed at improving small and distant object detection in ARAS applications: image center region-of-interest (ROI) cropping
and image slicing and re-slicing. Utilizing the YOLOv8-nano object detection model at relatively low in-put resolutions of 160×160, 320×320, and 640×640 pixels, we conducted experiments on the VisDrone and
KITTI datasets, which represent scenarios where small and distant objects are prevalent. Our results indicate that the image center ROI cropping technique improved the detection of small objects, particularly at a 320×320 resolution, achieving enhancements of 6.67× and 1.27× in mean Average Precision (mAP) on the VisDrone and KITTI datasets, respectively. However, excessive cropping negatively impacted the detection of medium and large objects due to the loss of peripheral contextual information and the exclusion of objects outside the cropped region. Image slicing and re-slicing demonstrated impressive improvements in detecting small objects, especially using the grid-based slicing strategy on the VisDrone dataset, with an mAP increase
of 2.24× over the baseline. Conversely, on the KITTI dataset, although a performance gain of 1.66× over the baseline was observed for small objects at a 320×320 resolution, image slicing adversely affected the detection of medium and large objects. The fragmentation of objects at image slice borders caused partial visibility, which reduced detection accuracy. These findings contribute to the development of more effective and efficient ARAS technologies, ultimately enhancing the safety of powered two-wheeler riders. Our evaluation code scripts are publicly accessible at: https://github.com/Luna-Scooters/SOD using image preprocessing
Richard Clark. 2025. Cooperative Complexity: The Next Level of Global Economic Governance
Full text linkReview of Cooperative Complexity: The Next Level of Global Economic
Governance
MSc Business Administration Research Digest
Full text linkAbstracts from DCU & PNU MScBA dissertations and applied research practicums
Gathering Autistic Children’s Views on Their Educational Experiences: A Systematic Review of Methods
Full text linkChildren should be heard on matters that impact their educational lives. However, meaningfully engaging autistic children can be difficult for researchers and policymakers. Therefore, this systematic review aimed to summarise methods used to gather the views of autistic children on their primary educational experi-ences. Database searches identified 34 peer-reviewed articles, which met the inclusion criteria. Analyses showed that semi-structured interviews were the most frequently used practice, and a variety of visual and technology-based activities were used as part of the interviews to support participants’ expression. A key consideration for future research and practice is that as researchers, it is our responsibility to co-produce methods with autistic children, to allow them to express their views, both in research and real life. The identity-first term ‘autistic’ is used throughout this study based on internationally reported preferences from autistic adults (e.g., Keating et al., 2023)
The power of coaching in the professional learning and development of school leaders: an ecological framework and critical insights from a systematic review
Full text linkSchool leadership has been shown to have a profound influence on students’ experiences and outcomes. Following the success of leadership coaching in industry, coaching has started to feature as a mechanism in the professional learning and development of school leaders. However, to date, evidence of how the various elements of coaching are embedded in the professional learning and development of school leaders is limited. To fill the lacuna of research in this area, this study aimed to conduct a systematic literature review of coaching as a form of the professional learning and development of school leaders, based on papers published in peer-reviewed journals between July 2014 and July 2024. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used, and the work was framed within Bronfenbrenner’s ecological systems theory. An overview of the concept of coaching and its development in the context of the professional learning and development of school leaders was provided. The methodology used in the study was then described, before the research evidence on coaching in the professional learning and development of school leaders was reported and discussed across five thematic findings, illuminating the factors that may advance the success of coaching as well as those that may impede it. Gaps in the literature were identified that may inform further research on this important topic
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 afect 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 efective
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
Mechanistic Modelling of Coupled UV Energy Penetration and Resin Flow Dynamics in Digital Light Processing (DLP)-Based Microfluidic Chip Printing
Full text linkDigital light processing (DLP) technology has emerged as a promising approach for fabricating high-precision microfluidic chips due to its exceptional resolution and rapid prototyping capabilities. However, UV energy penetration and resin flow dynamics during layer-by-layer printing introduce significant challenges for microchannel printing, particularly in controlling microchannel over-curing. In this study, a novel 3D DLP over-curing interaction model (DLP-OCIM) was developed to investigate the coupled effects of UV energy penetration and directional resin flow on the over-cured structure formation of microchannels. COMSOL Multiphysics 6.1 simulations incorporating UV light propagation, photopolymerization kinetics, and resin flow dynamics revealed that microchannel over-curing is a result of both energy infiltration through previously cured layers and
periodic resin flow induced by the peeling process. Experimental validation using linear and annular microfluidic chips demonstrated that increasing layer thickness induces progressive over-curing, leading to inclined cross-sectional structures. Additionally, the microchannel geometry and size significantly influence resin flow patterns, with shorter
transverse microchannels producing flatter over-cured profiles compared to their longitudinal counterparts. This study provides the first comprehensive analysis of the dynamic interplay between UV energy penetration and resin flow during DLP-based microchannel
fabrication, offering valuable process insights and optimization strategies for enhancing shape fidelity and printing accuracy in high-resolution microfluidic chips
Synthetic Fluency: Hallucinations, Confabulations, and the Creation of Irish Words in LLM-Generated Translations
Full text linkThis study examines hallucinations in Large Language Model (LLM) translations into Irish, specifically focusing on instances where the models generate novel, non-existent words. We classify these hallucinations within verb and noun categories, identifying six distinct patterns among the latter. Additionally, we analyse whether these hallucinations adhere to Irish morphological rules and what linguistic tendencies they exhibit. Our findings show that while both GPT-4.o and GPT-4.o Mini produce similar types of hallucinations, the Mini model generates them at a significantly higher frequency.
Beyond classification, the discussion raises speculative questions about the implications of these hallucinations for the Irish language. Rather than seeking definitive answers, we offer food for thought regarding the increasing use of LLMs and their potential role in shaping Irish vocabulary and linguistic evolution. We aim to prompt discussion on how such technologies might influence language over time, particularly in the context of low-resource, morphologically rich languages
Appraising Model Complexity in Option Pricing
Full text linkThe research question we consider is whether incremental complexity in option pricing models is justified by incremental model performance. We apply the model confidence set as a formal model comparison approach in appraising stochastic volatility jump‐diffusion option pricing models, spanning affine and nonaffine specifications. Jumps in price with stochastic (constant) arrival intensity produce superior (inferior) outcomes. A parsimonious negative exponential price jump distribution
outperforms the popular normal distribution. Jumps in volatility synchronized or not) worsen model performance. A parsimonious nonlinear hyperbolic drift extension of the Heston model performs particularly well. Nonlinear CEV models generally
do not produce appreciable model performance