427 papers
This study introduces AttribRank, an open-source Python framework that quantifies how author-level research rankings shift based on evaluation choices like collaboration rules and indicator types, revealing that these variations are structured by disciplinary fields rather than random noise.
This paper introduces the Research Translation Analysis (RTA) framework, a novel method that integrates guideline mapping with qualitative citation-content analysis to move beyond simple citation counts and systematically evaluate how biomedical research is actually interpreted, utilized, or misrepresented in clinical practice and subsequent studies.
This paper presents a robust and straightforward algorithm that approximates a univariate Gaussian distribution with a mixture of smaller-variance homoscedastic Gaussians by minimizing the squared Euclidean norm, while also discussing its extension to non-Gaussian distributions.
This study develops and validates a comprehensive 25-item, six-dimensional Household Energy Insecurity (HEI) scale using data from 1,350 US respondents, providing a robust tool for identifying vulnerable households and guiding equitable energy policies.
This paper introduces a flexible trigonometric-generated family of distributions, specifically a parsimonious NewWeibull special case, which is mathematically validated through theoretical derivations and Monte Carlo simulations to effectively model heterogeneous outbreak data, outperforming existing models when applied to daily COVID-19 cases in Saudi Arabia's Aseer Region.
This study analyzes longitudinal data from China and Europe to reveal that while having one or two children generally protects cognitive function in older adults, the specific gender composition of offspring significantly impacts cognitive trajectories only in China due to cultural and social welfare differences, whereas no such gender effect is observed in Europe.
SunBURST is a deterministic, GPU-native algorithm that efficiently computes high-dimensional Bayesian evidence by combining ray-cast mode discovery, batched L-BFGS refinement, and Laplace integration to achieve sub-percent accuracy in seconds for near-quadratic targets, while explicitly defining its limitations regarding non-quadratic or heavy-tailed distributions.
This paper introduces a Bayesian bootstrap ensemble of low-rank models that overcomes the scalability limitations of existing uncertainty-aware causal discovery methods, enabling efficient and well-calibrated posterior edge probability estimation for high-dimensional genomic data (up to ) where traditional approaches fail.
This bibliometric study of 347 Chemical Engineering doctoral dissertations from five Andhra Pradesh universities reveals that supervisor productivity largely conforms to Lotka's Law, citation patterns have significantly evolved toward higher volume and online sources over time, self-citation rates remain low, and the INFLIBNET e-ShodhSindhu consortium provides extensive coverage of the field's core journals, thereby transforming the region's research information environment.
This paper proposes a hybrid Neutrosophic Repetitive Dependent-State Sampling Plan (NH-RDSSP) for truncated cycle-life testing of Lithium-ion batteries that integrates resampling and lot-history dependency while modeling batch-to-batch uncertainty via triangular neutrosophic numbers, validated through closed-form derivations and calibration against real-world data from four independent sources.