Results 81 to 90 of about 108,415 (292)

Charting the Path to Increased Oil Palm Output in Ghana Beyond Area Expansion: Technology or Managerial Capacity — Which Leads the Way?

Agribusiness, EarlyView.
ABSTRACT This study sets out to investigate the prospects for raising oil palm output in sub‐Saharan Africa, particularly Ghana, without further expansion of cropland. Given global concerns about oil palm's role in deforestation and land use change, the focus is on enhancing productivity on existing farmlands.
Jacob Asravor, Francis Tsiboe, Ayobami Adetoyinbo, Jacob Ankamah   +3 more
wiley   +1 more source

Nonzero-sum Stochastic Games [PDF]

This paper treats of stochastic games. We focus on nonzero-sum games and provide a detailed survey of selected recent results. In Section 1, we consider stochastic Markov games. A correlation of strategies of the players, involving ``public signals'', is
Nowak, Andrzej S., Szajowski, Krzysztof
core   +1 more source

Existence of Nash Equilibrium Points for Markovian Nonzero-sum Stochastic Differential Games with Unbounded Coefficients

, 2014
This paper is related to nonzero-sum stochastic differential games in the Markovian framework. We show existence of a Nash equilibrium point for the game when the drift is no longer bounded and only satisfies a linear growth condition.
Hamadène, Said, Mu, Rui
core   +1 more source

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

Evolutionary Dynamics of Stochastic Q Learning in Multi-Agent Systems

Axioms
Since high complexity and uncertainty is inherent in real-world environments that can influence the strategies choices of agents, we introduce a stochastic perturbation term to characterize the interference caused by uncertain factors on multi-agent ...
Luping Liu, Gang Sun
doaj   +1 more source

REGULARITY AND SENSITIVITY FOR MCKEAN-VLASOV TYPE SPDEs GENERATED BY STABLE-LIKE PROCESSES

Проблемы анализа, 2018
In this paper we study the sensitivity of nonlinear stochastic differential equations of McKean–Vlasov type generated by stable-like processes. By using the method of stochastic characteristics, we transfer these equations to non-stochastic equations ...
V. N. Kolokoltsov, M. S. Troeva
doaj   +1 more source

A theory of regular Markov perfect equilibria in dynamic stochastic games: genericity, stability, and purification [PDF]

This paper studies generic properties of Markov perfect equilibria in dynamic stochastic games. We show that almost all dynamic stochastic games have a finite number of locally isolated Markov perfect equilibria.
Doraszelski, Ulrich, Escobar, Juan
core   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Magnifying Lens Abstraction for Stochastic Games with Discounted and Long-run Average Objectives

, 2011
Turn-based stochastic games and its important subclass Markov decision processes (MDPs) provide models for systems with both probabilistic and nondeterministic behaviors. We consider turn-based stochastic games with two classical quantitative objectives:
Chatterjee, Krishnendu, de Alfaro, Luca, Roy, Pritam   +2 more
core  

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source