This page shows all publications that appeared in the IASI annual research reports.
Authors currently affiliated with the Institute are always listed with the full name.
You can browse through them using either the links of the following line or those associated with
author names.
Show all publications of the year
2016, with author Palumbo P., in the category ALL
(or show them all): (Items found: 18)
2016 [top]
- Borri A, Francesco Carravetta, Pasquale Palumbo: Cubification of Nonlinear Stochastic Differential Equations and Approximate Moments Calculation of the Langevin Equation, 55th IEEE Conference on Decision and Control (CDC16), 2016
- Borri A, Pasquale Palumbo, Singh A: The impact of negative feedback in metabolic noise propagation, IET Systems Biology 10, 179-186, 2016
- Borri A, Pasquale Palumbo, Singh A: Noise reduction for enzymatic reactions: a case study for stochastic product clearance, 55th IEEE Conference on Decision and Control (CDC16), 2016
- Borri A, Simona Panunzi, Pasquale Palumbo, Manes C., De Gaetano A.: Glucose control with incomplete information, IEEE Conference on Systems, Man and Cybernetics, 2016
- Borri A., Francesco Carravetta, Gabriella Mavelli, Pasquale Palumbo: Block-tridiagonal state-space realization of Chemical Master Equations: a tool to compute explicit solutions, Journal of computational methods in applied mathematics 296, 410-426, 2016
- Alessandro Borri, Francesco Carravetta, Gabriella Mavelli, Pasquale Palumbo: Block-tridiagonal state-space realization of Chemical Master Equations: A tool to compute explicit solutions, Journal of Computational and Applied Mathematics vol. 296, pp. 410–426, 2016
- Alessandro Borri, Francesco Carravetta, Pasquale Palumbo: A Cubification Approach for the Approximate Moments Computation in Stochastic Differential Equations: Application to the Chemical Langevin Equation, IASI-CNR, R. 16-01, 4/2016
- Alessandro Borri, Francesco Carravetta, Pasquale Palumbo: Stochastic Differential Equations and Approximate Moments Calculation of the Langevin Equation, Proceedings of the 55th IEEE Conference on Decision and Control (CDC 2016), Las Vegas, USA, pp. 4540-4545, 2016
- Alessandro Borri, Francesco Carravetta, Pasquale Palumbo: A Cubification Approach for the Approximate Moments Computation in Stochastic Differential Equations: Application to the Chemical Langevin Equation, IASI-CNR, R. 16-01, 2016
- Alessandro Borri, Pasquale Palumbo, Singh A: Noise reduction for enzymatic reactions: a case study for stochastic product clearance, Proceedings of the 55th IEEE Conference on Decision and Control (CDC 2016), Las Vegas, USA, pp. 5851-5856, 2016
- Alessandro Borri, Pasquale Palumbo, Singh A: Impact of negative feedback in metabolic noise propagation, IET Systems Biology 10 (5), 179-186, 2016
- Alessandro Borri, Simona Panunzi, Pasquale Palumbo, Manes C., De Gaetano A.: Preliminary results on glucose control with sampled information, IASI-CNR, R. 16-02, 4/2016
- Alessandro Borri, Simona Panunzi, Pasquale Palumbo, Manes C., De Gaetano A.: Glucose control with incomplete information, Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), Budapest, Ungheria, pp. 1780-1784, 2016
- Alessandro Borri, Simona Panunzi, Pasquale Palumbo, Manes C., De Gaetano A.: Preliminary results on glucose control with sampled information, IASI-CNR, R. 16-02, 2016
- Cacace F., Valerio Cusimano, Germani A., Pasquale Palumbo: A state predictor for continuous-time stochastic systems, Systems & Control Letters 98, 37-43, 2016
- Cacace F., Valerio Cusimano, Germani A., Pasquale Palumbo: Carleman discretization of impulsive systems: application to the optimal control problem of anti-angiogenic tumor therapies, 55th IEEE Conference on Decision and Control (CDC16), 2016
- Valerio Cusimano, Pasquale Palumbo, Federico Papa: Closed-loop control of tumor growth by means of anti-angiogenic administration, presented at SIMAI 2016, 2016
- Pasquale Palumbo, Vanoni M., Valerio Cusimano, Busti S, Marano M., Manes C., Alberghina L.: Whi5 phosphorylation embedded in the G1/S network dynamically controls critical cell size and cell fate, Nature Communications, 2016
|
