252 papers
This study develops an optimal control framework for an extended SEIVBPR model to determine the most cost-effective strategy of combining routine vaccination, booster campaigns, and contact-reduction measures, demonstrating that such interventions can eradicate poliovirus transmission and reduce permanent paralysis by over 80% within 25 weeks.
Based on an empirical analysis of 35,852 papers from *Nature Communications* using propensity score matching, this study demonstrates that Open Peer Review significantly correlates with higher altmetric attention scores, suggesting that transparent review processes enhance the social impact and public dissemination of scientific research.
This paper demonstrates that label-free classifier evaluation is a singular statistical problem where classical model selection criteria fail due to degenerate Fisher information, and proposes using singular and widely applicable Bayesian information criteria to accurately recover latent structures and ensure trustworthy performance estimates without ground-truth labels.
This study proposes a hybrid decision-support framework that integrates the CRITIC method with a weighted k-means algorithm to optimize the strategic placement of humanitarian relief centers in park areas within Gaziantep, ensuring needs-based and objective planning for post-disaster scenarios.
This study demonstrates that k-nearest neighbours (KNN) imputation outperforms both median substitution and multiple imputation by chained equations (MICE) in preserving the distributional fidelity and multivariate structural integrity of real-world clinical datasets across varying levels of missingness.
This scoping review and case study of Syria reveal that conventional statistical methods systematically render people with disabilities invisible in humanitarian contexts by failing to capture complex, intersectional realities, thereby necessitating a paradigm shift toward more disaggregated and flexible data collection to ensure inclusive aid delivery.
This study demonstrates that machine-learning models trained on post-wildfire damage data, while accurate within specific events, fail to generalize across different fires because the protective or detrimental effects of home-hardening features (such as single-pane windows) are not stable and can even reverse depending on the fire context, thereby cautioning against using pooled observational data to derive universal hardening policies.
This Monte Carlo study demonstrates that in writing research pretest–posttest designs, varying the number of tasks and score reliability produces systematic trade-offs in performance and cost rather than a uniform ranking, thereby advocating for multi-criteria evaluation and mechanism-focused validation over reliance on single metrics.
This paper introduces a novel heteroscedastic regression framework based on the exponentiated generalized normal distribution with two shape parameters, validating its robustness through Monte Carlo simulations and demonstrating its practical application in modeling persimmon fruit coloration.
This study employs spatial econometric and Bayesian hierarchical models to analyze homicides in Brazil's Metropolitan Region of Campinas (2017–2022), revealing that socioeconomic factors like income and dependency ratios, alongside strong spatial clustering, significantly influence violence patterns and necessitate territory-sensitive public security policies.