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Revenue composition and financial health of nonprofit humanitarian and emergency health services
Get PDFPurposeEmergency health and humanitarian nonprofits work under volatile circumstances that strain nonprofits' financial resources. This study investigates the impact of revenue composition on the financial health of these nonprofits and the impact of financial health on the likelihood of financial distress.Design/methodology/approachA sample of 11,335 emergency nonprofits from 2003 to 2020 was obtained through form 990 data and studied through a difference generalized method of moments (GMM) approach for the impact of revenue composition on financial health. The impact of financial health on financial distress was studied through panel logistics regression.FindingsRevenue diversification adversely affects the financial health of nonprofit emergency health and humanitarian organizations contrary to the implications of modern portfolio theory. The financial health of nonprofit emergency health and humanitarian organizations is persistent through the significant positive effect of lags in most cases.Originality/valueThe emergency health subsector of nonprofits was studied separately due to the unique nature of the sectors' operations and operating environment. The impact of revenue composition was investigated on key dimensions of financial health. Omitted variable bias, simultaneity and dynamic endogeneity were handled through difference GMM
The Future of Public Service Media in Northern Ireland
Get PDFA report of event proceedings from: The Future of Public Service Media in Northern Ireland: the Policy Implications of Research and Practice, 9 May 2025
Magnetic Field‐Assisted Conductive Nerve Guidance Conduit Enabling Peripheral Nerve Regeneration with Wireless Electrical Stimulation
Get PDFNerve guidance conduits capable of wireless stimulation represent a promising approach for addressing peripheral nerve defects. However, traditional electrical stimulation methods are not sufficiently convenient and may cause secondary damage. In this study, a conductive nerve guidance conduit combined with wireless electrical stimulation using alternating magnetic fields is presented. The conduit coated with nanographene and incorporated with Fe3O4 nanoparticles induces currents and creates a supportive microenvironment enhancing nerve regeneration. Finite element analysis confirms that the conduit generates electromotive force under an external alternating magnetic field. The conduit exhibits improved morphology, physicochemical properties, and conductivity by six orders of magnitude. In vitro experiments demonstrate that the conduit promotes Schwann cell proliferation, migration, and intercellular communication through microcurrents, as well as neuronal axon extension. TEM images confirm axon extension and myelin sheath thickness, indicating its high conductivity and efficiency in promoting nerve regeneration across defects. In vivo studies show that the conduit generated microcurrent using wireless electromagnetic stimulation, significantly enhancing myelin restoration, gastrocnemius muscle regeneration, motor function recovery, and nerve tissue growth, achieving results comparable to the gold-standard autograft method. Overall, this work highlights the effectiveness of electromagnetic induction in nerve repair and presents a new, non-invasive stimulation for peripheral nerve regeneration
Inclusion is not enough: a qualitative study of leadership, ethos, and policy in racially heterogeneous schools in NI
Get PDFIn Northern Ireland (NI), where the education system has historically been shaped by religious and cultural divisions, recent demographic shifts due to inward migration present both challenges and opportunities for educational leaders. While research reveals the crucial role of leaders in promoting inclusivity [Miller, P. 2019. “Race and Ethnicity in Educational Leadership.” In Principles of Educational Leadership and Management, edited by T. Bush, L. Bell, and D. Middlewood, 223–238. SAGE Publications.], little is known about how leaders in different school types, particularly those with limited prior exposure to racial heterogeneity, interpret these changes. Drawing on debates in multicultural education (MCE), we examine how leaders in a Catholic and a State-Controlled post-primary school, alongside policy workers, respond to racial diversity. Findings from 12 qualitative interviews show that while leaders are supportive of minority ethnic students (MES), responses are often intuitive rather than evidence-based, reflecting gaps in professional development and systemic constraints. The paper highlights the role of school ethos in shaping leadership responses. An ethos that makes a rhetorical commitment to inclusion without meaningful implementation embeds systemic inequities. By situating leadership of schools with MES within a complex web of ethos and policy, this study moves beyond prescriptive leadership models, advocating for a nuanced, critically engaged, and contextually responsive approach to educational leadership in NI
GAN-Based Data Augmentation for Fault Diagnosis and Prognosis of Rolling Bearings: A Literature Review
Get PDFIn 2014, Goodfellow et al. introduced Generative Adversarial Networks (GANs), an adversarial learning framework designed to generate synthetic data. In rolling bearing fault diagnosis and prognosis, specific GAN variants such as Conditional GANs (cGANs), Wasserstein GANs (WGANs), and their derivatives have been developed to address data scarcity and class imbalance challenges. In this work, we conduct a literature review to systematically examine GAN-based data augmentation techniques for fault diagnosis and prognosis of rolling bearings. Through a rigorous selection process, we identified 229 primary studies that employed GAN-based data augmentation, underscoring the widespread use of GANs to generate synthetic data in this field. Our review shows that GANs were first applied to rolling bearing fault diagnosis in 2018, and their use has grown significantly since then. Among GAN variants, Wasserstein GANs (WGANs) and Conditional GANs (cGANs) have proven highly effective in generating realistic synthetic data, particularly when integrated with Convolutional Neural Networks (CNNs). The review further reveals that CNN models have been widely used, achieving accuracy rates exceeding 95% in fault diagnosis and prognosis. We also report that 90% of studies employ accuracy as the primary evaluation metric, while 15% use F1-score, as detailed in our metric analysis for bearing fault diagnosis. For fault prognosis, RMSE and MAE are the most commonly used metrics, appearing in 11% and 9% of studies,respectively. Our analysis reveals standardized hyperparameter configurations with learning rate 0.0001, Adam optimizer, and batch size 32 being most effective. The review identifies critical challenges including data imbalance (19.7%), training instability (11.0%), and data scarcity (10.7%) as primary bottlenecks for industrial adoption. This review establishes a comprehensive foundation for understanding the current state and future directions of GAN-based approaches for rolling bearing fault diagnosis and prognosis, offering researchers and practitioners a valuable resource in industrial predictive maintenance.<br/
Women's experiences of living with chronic pain: A qualitative meta‐synthesis
Get PDFObjectives: The prevalence of chronic pain varies between males and females, and they also have distinct pain experiences. Improved understanding of these unique experiences is needed to improve support. Design: This meta‐synthesis aimed to develop a comprehensive understanding of women's lived experiences with chronic pain. Methods: Six electronic databases were searched in May and June 2022: PubMed Central, the Cumulative Index to Nursing and Allied Health Literature (CINAHL Plus), the Health Research Premium Collection, ScienceDirect, Web of Science and PsycINFO. Studies were included if they were full‐text journal articles, reported in English, presented qualitative findings obtained using qualitative research methods and focused on the experience of females over eighteen years old, living with chronic pain (not associated with cancer or conditions that are terminal). The search was updated in November 2024. Results: Analysis of the seventy studies retrieved identified four themes: Pain and Multiple Responsibilities; Countless Losses (and Their Psychological Effects); Lack of Understanding: Delegitimizing and Disempowering Encounters; and Solace and Self‐Empowerment. Confidence in all four themes was evaluated as high. Conclusions: These findings indicate that there are common themes that run through the lives of women living with chronic pain across a range of different age groups, locations and conditions. These domains present actionable opportunities to enhance pain management and well‐being for women living with chronic pain
A Smooth-Delayed Phase-Type Mixture Model for Human-Driven Process Duration Modeling
Get PDFActivities in business processes primarily depend on human behavior for completion. Due to human agency, the behavior underlying individual activities may occur in multiple phases and can vary in execution. As a result, the execution duration and nature of such activities may exhibit complex multimodal characteristics. Phase-type distributions are useful for analyzing the underlying behavioral structure, which may consist of multiple sub-activities. The phenomenon of delayed start is also common in such activities, possibly due to the minimum task completion time or prerequisite tasks. As a result, the distribution of durations or certain components does not start at zero but has a minimum value, and the probability below this value is zero. When using phase-type models to fit such distributions, a large number of phases are often required, which exceed the actual number of sub-activities. This reduces the interpretability of the parameters and may also lead to optimization difficulties due to overparameterization. In this paper, we propose a smooth-delayed phase-type mixture model that introduces delay parameters to address the difficulty of fitting this kind of distribution. Since durations shorter than the delay should have zero probability, such hard truncation renders the parameter not estimable under the Expectation–Maximization (EM) framework. To overcome this, we design a soft-truncation mechanism to improve model convergence. We further develop an inference framework that combines the EM algorithm, Bayesian inference, and Sequential Least Squares Programming for comprehensive and efficient parameter estimation. The method is validated on a synthetic dataset and two real-world datasets. Results demonstrate that the proposed approach maintains a suitable performance comparable to purely data-driven methods while providing good interpretability to reveal the potential underlying structure behind human-driven activities
Improved Performance and Stability of Perovskite Solar Cells by Incorporating Silicon Quantum Dots within the FAPbI3 Active Layer
Get PDFEmbedding inorganic quantum dots (iQDs) within the perovskite absorber offers a promising route to improve both efficiency and stability in perovskite solar cells (PSCs). Due to the defect-tolerant nature of lead halide perovskites, iQDs can be incorporated within crystal grains without degrading performance, while contributing their unique optoelectronic properties. In this study, silicon quantum dots (SiQDs) are embedded into perovskite films to form high-quality hybrid thin films. Prior to forming the hybrid film, a femtosecond laser-based surface engineering (SE) technique is used to fragment SiQDs into highly dispersed, stable, ultrasmall particles (≈2 nm). Incorporation of SE-treated SiQDs (SE-SiQDs) into the perovskite layer reduces the density of shallow traps and improves carrier transport. A substantial decrease in residual lead iodide (PbI2) is observed at the film surface, and modulation of the Fermi level is achieved through SiQD incorporation. PSCs incorporating SE-SiQDs exhibit a fill factor exceeding 80% and a power conversion efficiency above 20% (active area: 0.23 cm2), along with enhanced long-term operational stability
Polyether ether ketone (PEEK) and polyetherimide (PEI) for fused filament fabrication (FFF) in medical applications
Get PDFRecently, high-performance polymers (HPP) have been exploited in the world of additive manufacturing (AM) as a result of improved techniques and the ability to process these materials which require higher processing temperatures. These materials present enhanced mechanical properties, chemical resistance, and thermal stability, increasing AM potentials beyond prototyping applications. Polyether ether ketone (PEEK) has been recognised for its mechanical properties due to its semi crystalline nature and established itself as a biomaterial, possessing biocompatibility and chemical resistance. Polyetherimide (PEI) is renowned for its thermal stability and has been utilised in high temperature-dependent applications. PEEK and PEI are one of the few miscible blends of HPPs, characterized through the presence of a single glass transition temperature. Both materials individually present properties which make them ideal for biomedical applications and through the blending of these materials the biomedical industry could benefit from the synergistic outcome. This review paper will look at PEEK, PEI, and their blends, focusing on the printing parameters, crystallinity and reinforcements. It will also take a look at some of the areas which PEEK and PEI are currently being used, including, implants, prosthetics, and Tissue Engineering
Orientation Prediction in Robotics: A Study of Trigonometric Decomposition Methods Across Synthetic and Real-World Datasets
Get PDFOrientation prediction is a critical task for robotics as it enables robots to understand and interact with their environment more effectively. By accurately determining an object's position and orientation, robots can perform a range of complex tasks. This in turn will advance smart manufacturing facilities to achieve higher levels of automation, increase efficiency, and enable more flexible production systems. Hence, we present a comparative study of shallow regression models, integration strategies, and trigonometric encoding schemes for planar orientation prediction in robotics, using synthetic and real-world datasets. Results demonstrate that XGBoost 1.7, combined with vector integration and quadrant encoding, achieves the best balance of accuracy, robustness to angular boundary discontinuities, and computational efficiency, significantly outperforming alternative approaches in real-world scenarios