Results 221 to 230 of about 15,837 (289)

Dispersal and delimitation: Phylogenomics of Connaraceae prompts revised generic delimitation in Cnestideae and reveals global biogeographic patterns

TAXON, Volume 75, Issue 2, April 2026.
Abstract The tempo and mode of assembly of the world's most diverse, tropical floras remain poorly known. Evolutionary relationships within pantropical plant clades such as Connaraceae (Oxalidales, ca. 220 species) offer an opportunity to address this issue.
Serafin J.R. Streiff, Jurriaan M. de Vos
wiley   +1 more source

Forecasting land-use and land-cover change for groundwater sustainability in the Muvattupuzha basin using CA-Markov (2033-2050). [PDF]

Sci Rep
K A, Gautam S, Prince Arulraj G, Joshi SK, Ho CH.   +4 more
europepmc   +1 more source

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

Journal of Economic Surveys, Volume 40, Issue 2, Page 915-953, April 2026.
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen, Tord Johan Espe, Kristoffer Grude, Morten Risstad, Rickard Sandberg   +4 more
wiley   +1 more source

Determinants of stunting among under-five children in Amhara and Oromia regions, Ethiopia: the linear quantile regression analysis. [PDF]

BMC Public Health
Wachifo DD, Debeko DD, Asfaw ZG.
europepmc   +1 more source

Myofibre Density Reveals a Critical Threshold Around Age 6 in Steroid‐Naïve Duchenne Muscular Dystrophy: A Retrospective Observational Study

Neuropathology and Applied Neurobiology, Volume 52, Issue 2, April 2026.
We analysed archival muscle biopsies of steroid‐naïve patients with Duchenne muscular dystrophy (DMD), proposing a novel, simple metric: myofibre density (MFD). MFD sharply declines until age 6, identifying a critical threshold around this age. MFD provides a sensitive, reproducible biomarker for early disease progression, highlighting a potential ...
Tetsuhiro Yamakado, Yuka Ishikawa, Koki Ise, Lei Wang, Yoshitaka Oda, Masumi Tsuda, Taichi Kimura, Masayoshi Nagao, Manabu Ito, Shinya Tanaka, Zen‐ichi Tanei   +10 more
wiley   +1 more source

A Systematic Review of Topic Modeling Techniques for Electronic Health Records. [PDF]

Healthcare (Basel)
Mehmood I, Zahra Z, Iqbal S, Qahmash A, Hussain I.   +4 more
europepmc   +1 more source

Stochastic Modelling of Daily Precipitation in Semi‐Arid Regions Using Markov Chains and Parametric Distributions

International Journal of Climatology, Volume 46, Issue 4, 30 March 2026.
This study applies stochastic rainfall models combining Markov Chains with gamma and mixed exponential distributions to a semi‐arid climate in Northeast Brazil. Model structures were evaluated using Bayesian Information Criterion (BIC), with maximum likelihood (MLM) for parameter estimation and cumulative distribution functions (CDFs) for validation ...
Gabriel Magno Cavalcante Calado, Mayke Feitosa Progênio, José Adalberto da Silva Filho, Ialy Rayane de Aguiar Costa, Severino Martins Neto, Artur Paiva Coutinho   +5 more
wiley   +1 more source

A Review of R Packages for Bayesian Model-based Clustering of High-dimensional Multivariate Environmental Exposures. [PDF]

Curr Environ Health Rep
Stephenson BJK, Fu Y.
europepmc   +1 more source

Utilising Benford's Law in the Validation of Precipitation Datasets

International Journal of Climatology, Volume 46, Issue 3, 15 March 2026.
This study demonstrates Benford's Law as a robust diagnostic tool for precipitation data integrity. Global monthly precipitation consistently conforms to the law, regardless of origin, while regional data requires appropriate temporal granularity for adherence.
Amee Gollop, Sarah Wilson Kemsley, Tim Osborn, Manoj Joshi, David Stevens, Ian Harris   +5 more
wiley   +1 more source

Disentangling Boltzmann Brains, the Time-Asymmetry of Memory, and the Second Law. [PDF]

Entropy (Basel)
Wolpert D, Rovelli C, Scharnhorst J.
europepmc   +1 more source