Results 211 to 220 of about 240,621 (277)

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

open access: yesMed Research, EarlyView.
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu   +3 more
wiley   +1 more source

Time Functions on Lorentzian Length Spaces. [PDF]

open access: yesAnn Henri Poincare
Burtscher A, García-Heveling L.
europepmc   +1 more source

Extending the hyper‐logistic model to the random setting: New theoretical results with real‐world applications

open access: yesMathematical Methods in the Applied Sciences, EarlyView.
We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés   +2 more
wiley   +1 more source

The piranha problem: Large effects swimming in a small pond. [PDF]

open access: yesNot Am Math Soc
Tosh C   +5 more
europepmc   +1 more source

Network-Based Epidemic Control Through Optimal Travel and Quarantine Management. [PDF]

open access: yesIEEE Trans Control Netw Syst
Talaei M   +4 more
europepmc   +1 more source

The Glass Transition: A Topological Perspective. [PDF]

open access: yesEntropy (Basel)
Vesperini A, Franzosi R, Pettini M.
europepmc   +1 more source

Hyperbolic P ( Φ ) 2 -model on the Plane. [PDF]

open access: yesCommun Math Phys
Oh T, Tolomeo L, Wang Y, Zheng G.
europepmc   +1 more source

Local Limit Theorems for Large Deviations

Theory of Probability & Its Applications, 1957
Let $(X_j ),j = 1,2, \cdots $, be a sequence of independent random variables with the distribution functions $V_j (x)$. We assume the existence of ${\bf D}X_j = \sigma _j^2 ,s_n^2 = \sum\nolimits_{j = 1}^n {\sigma _j^2 } ,{\bf E}X_j = 0,j = 1,2, \cdots $. We put \[ Z_n = \sum\limits_{j = 1}^n X_j /s_n .
W. Richter
semanticscholar   +3 more sources

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