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Physics-Informed Neural Networks (PINNs) and Neural Ordinary Differential Equations (NODEs) represent two distinct machine learning frameworks for modeling nonlinear neuronal dynamics. This study systematically evaluates their performance on the two-dimensional Morris-Lecar model across three canonical bifurcation regimes: Hopf, Saddle-Node on Limit Cycle, and homoclinic orbit. Synthetic time-series data are generated via numerical integration under controlled conditions, and training is performed using collocation points for PINNs and adaptive solvers for NODEs (Dormand-Prince method). PINNs incorporate the governing differential equations into the loss function using automatic differentiation, which enforces physical consistency during training. In contrast, NODEs learn the system's vector field directly from data, without prior structural assumptions or inductive bias toward physical laws. Model performance is assessed using standard regression metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination. Results indicate that PINNs tend to achieve higher accuracy and robustness in scenarios involving stiffness or sensitive bifurcations, owing to their embedded physical structure. NODEs, while more expressive and flexible, operate as black-box approximators without structural constraints, which can lead to reduced interpretability and stability in these regimes. Although advanced variants of NODEs (e.g., ANODEs, latent NODEs) aim to mitigate such limitations, their performance under stiff dynamics remains an open question. These findings emphasize the trade-offs between physics-informed models, which embed structure and interpretability, and purely data-driven approaches, which prioritize flexibility at the cost of physical consistency.
Education systems worldwide face a growing pressure to align with Sustainable Development Goal 4.7 by embedding Education for Sustainable Development (ESD) into school life. This study examines how primary school headteachers in Cyprus interpret and enact sustainable leadership to advance ESD within a small, highly centralised system. Drawing on sustainable and distributed leadership theories and a whole-school lens, the study employed semi-structured interviews with ten headteachers from diverse regions (urban, rural, and semi-rural). Reflective thematic analysis identified four patterns: (1) leaders sought a strategic integration of ESD into planning and culture; (2) empowerment and participation were pursued through teacher working groups, student eco-councils, and community partnerships; (3) systemic constraints, a rigid curriculum, limited autonomy, and scarce professional development produced a policy–practice gap; and (4) leaders relied on adaptive, collaborative micro-practices to sustain momentum. The findings suggest that, in Cyprus, sustainable leadership operates as a values-based stewardship enacted through ‘quiet activism’. The study highlights implications for leadership development, such as reflexivity, systems thinking, and ethical reasoning, as well as policy design, such as time, autonomy, and structured support for whole-school ESD, in small-state contexts.
Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forecasts without uncertainty estimates, a critical gap for high-stakes decisions during extreme weather events. This paper proposes Neural Tangent Kernel-based uncertainty quantification (NTK-UQ) using last-layer empirical features. Theoretical analysis predicts that UQ quality is architecture-dependent through two mechanisms. First, a variance collapse mechanism explains when UQ fails: when the eigenvalue truncation rank approaches the effective rank of the feature space, the GP correction term consumes nearly all prior variance, destroying discrimination between tropical cyclones and routine conditions; architectures with concentrated spectra (spectral operators) require aggressive truncation (k <= 10), while attention-based models tolerate full-rank computation. Second, decomposition performance depends on the non-Gaussian, heavy-tailed structure of extreme weather: Independent Component Analysis exploits higher-order statistics (kurtosis, negentropy) to isolate heavy-tailed extreme-event features, achieving higher discrimination than singular value decomposition, which captures only second-order variance. A data-driven selection rule chooses ICA or SVD from the feature eigenspectrum concentration ratio, correctly prescribing the superior decomposition for all four evaluated architectures. Compared to split conformal prediction (the natural post-hoc baseline), NTK-UQ achieves 31-37% sharper prediction intervals at 90% coverage, and uniquely produces adaptive intervals that scale with extreme event severity, which conformal prediction cannot achieve by construction. The framework requires no retraining; inference-time uncertainty requires only a single matrix-vector product per sample.
School congestion, where student enrollment exceeds school capacity, is a major challenge in low- and middle-income countries. It highly impacts learning outcomes and deepens inequities in education. While subsidy programs that transfer students from public to private schools offer a mechanism to alleviate congestion without capital-intensive construction, they often underperform due to fragmented data systems that hinder effective implementation. The Philippine Educational Service Contracting program, one of the world's largest educational subsidy programs, exemplifies these challenges, falling short of its goal to decongest public schools. This prevents the science-based and data-driven analyses needed to understand what shapes student enrollment flows, particularly how families respond to economic incentives and spatial constraints. We introduce a computational framework for modeling student flow patterns and simulating policy scenarios. By synthesizing heterogeneous government data across nearly 3,000 institutions, we employ a stochastic gravity model estimated via negative binomial regression to derive behavioral elasticities for distance, net tuition cost, and socioeconomic determinants. These elasticities inform a doubly constrained spatial allocation mechanism that simulates student redistribution under varying subsidy amounts while respecting both origin candidate pools and destination slot capacities. We find that geographic proximity constrains school choice four times more strongly than tuition cost and that slot capacity, not subsidy amounts, is the binding constraint. Our work demonstrates that subsidy programs alone cannot resolve systemic overcrowding, and computational modeling can empower education policymakers to make equitable, data-driven decisions by revealing the structural constraints that shape effective resource allocation, even when resources are limited.
BackgroundDengue and chikungunya, both transmitted by Aedes mosquitoes, continue to pose significant public health concerns in Thailand, particularly during the rainy season. Despite ongoing vector control efforts, the incidence of infection remains high, with an increasing trend observed in chikungunya. This underscores the need for additional control measures, including vaccination, to reduce disease burden and morbidity. This study aims to assess the seroprevalence of dengue and chikungunya infections among children aged 10-15 years in Bangkok and to evaluate the knowledge, attitudes, and practices (KAP) related to mosquito-borne disease prevention.MethodologyA cross-sectional descriptive study was conducted across 12 schools in Bangkok. Children aged 10-15 years were included. Seroprevalence was determined using rapid diagnostic tests (Abbott DENGUE IgG/IgM and Citest Chikungunya IgG/IgM) based on the immunochromatography technique, using fingertip blood samples. Parents completed KAP questionnaires, including factors influencing vaccination decisions.Principal findingsFrom June to August 2024, 937 participants were enrolled, with a mean (SD) age of 11 (1.6) years; 67% were aged 10-12 years, and 33% were aged 13-15 years. The seroprevalence of dengue was 28.1% (95% CI 25.2-31.0), while that of chikungunya was 6.3% (95% CI 4.7-7.9). KAP assessments revealed a high level of awareness regarding symptoms and transmission; however, notable deficiencies in preventive behaviors were identified. Only 14.8% of respondents reported consistent use of mosquito repellent, and 17.5% routinely inspected and removed mosquito larvae from their homes.ConclusionThe substantial seroprevalence of dengue and the emerging trend of chikungunya among children in Bangkok highlights the urgent need to enhance community education and strengthen vector control interventions. Expanding dengue vaccination coverage and raising awareness about chikungunya prevention, including consideration for future vaccine implementation, are essential to mitigating future outbreaks and reducing the disease burden.Trial registrationThai Clinical Trials under the number TCTR20240404002 (https://www.thaiclinicaltrials.org/show/TCTR20240404002).
