Results 191 to 200 of about 268,397 (277)

A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks. [PDF]

BMC Bioinformatics, 2017
Röhl A, Bockmayr A.
europepmc   +1 more source

Intelligent Control Framework of District Heating Systems Considering Waste Heat Utilization in Data Centers

Energy Science &Engineering, EarlyView.
A deep reinforcement learning–based control architecture is proposed to coordinate heat pumps, thermal storage, renewable energy, and demand response in data center waste heat recovery systems. The agent learns optimal control actions from system states and reward feedback to achieve electrical–thermal co‐optimization under realistic operational ...
Rendong Shen, Ruifan Zheng, Shengyuan Zhong, Lei Gu, Dongfang Yang, Jun Zhao   +5 more
wiley   +1 more source

A freight integer linear programming model under fog computing and its application in the optimization of vehicle networking deployment.

PLoS One, 2020
Wang X, Qiu P.
europepmc   +1 more source

Local Polynomial Regression and Filtering for a Versatile Mesh‐Free PDE Solver

International Journal for Numerical Methods in Fluids, EarlyView.
A high‐order, mesh‐free finite difference method for solving differential equations is presented. Both derivative approximation and scheme stabilisation is carried out by parametric or non‐parametric local polynomial regression, making the resulting numerical method accurate, simple and versatile. Numerous numerical benchmark tests are investigated for
Alberto M. Gambaruto
wiley   +1 more source

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, EarlyView.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 more
wiley   +1 more source

Learning oncogenetic networks by reducing to mixed integer linear programming. [PDF]

PLoS One, 2013
Shahrabi Farahani H, Lagergren J.
europepmc   +1 more source

Motive and Opportunity: Order Choice in a Limit Order Book With Dispersed Information

International Journal of Finance &Economics, EarlyView.
ABSTRACT We test predictions of market microstructure theory relating to the determinants of order choice in a limit order book where information is dispersed among traders. Using an experimental limit order book, with a large state space, we find that informed traders exhibit patience, compatible with the ‘waiting game’ behaviour described in Foster ...
James Steeley, Charles Schnitzlein, Patricia Chelley‐Steeley   +2 more
wiley   +1 more source

Split diversity in constrained conservation prioritization using integer linear programming. [PDF]

Methods Ecol Evol, 2015
Chernomor O, Minh BQ, Forest F, Klaere S, Ingram T, Henzinger M, von Haeseler A.   +6 more
europepmc   +1 more source

Ultrafast in‐memory computing and highly efficient deep neural networks driven by phase‐change memory materials with partially amorphous state transitions

InfoScience, EarlyView.
This work addresses challenges including the nonlinear weight‐conductance update and the trade‐off between increasing melting uniformity and reducing solid‐to‐liquid transition time. It utilizes all four melting states to create an integrated framework for attaining in‐memory computing and deep neural network applications. The framework achieves a near‐
Kian‐Guan Lim, Shao‐Xiang Go, Qiang Wang, Dong‐Gwan Kim, Chan‐Hwi Baek, Qishen Wang, Tae Hoon Lee, Desmond K. Loke   +7 more
wiley   +1 more source