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Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery
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]
Burtscher A, García-Heveling L.
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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]
Tosh C +5 more
europepmc +1 more source
No-Signaling in Steepest Entropy Ascent: A Nonlinear, Non-Local, Non-Equilibrium Quantum Dynamics of Composite Systems Strongly Compatible with the Second Law. [PDF]
Ray RK, Beretta GP.
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Network-Based Epidemic Control Through Optimal Travel and Quarantine Management. [PDF]
Talaei M +4 more
europepmc +1 more source
The Glass Transition: A Topological Perspective. [PDF]
Vesperini A, Franzosi R, Pettini M.
europepmc +1 more source
A Toponogov globalisation result for Lorentzian length spaces. [PDF]
Beran T, Harvey J, Napper L, Rott F.
europepmc +1 more source
Hyperbolic P ( Φ ) 2 -model on the Plane. [PDF]
Oh T, Tolomeo L, Wang Y, Zheng G.
europepmc +1 more source
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Local Limit Theorems for Large Deviations
Theory of Probability & Its Applications, 1957Let $(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

