A simulation-based algorithm for optimal pricing policy under demand uncertainty

We propose a simulation-based algorithm for computing the optimal pricing policy for a product under uncertain demand dynamics. We consider a parameterized stochastic differential equation (SDE) model for the uncertain demand dynamics of the product over the planning horizon. In particular, we consider a dynamic model that is an extension of the Bass model. The performance of our algorithm is compared to that of a myopic pricing policy and is shown to give better results. Two significant advantages with our algorithm are as follows: (a) it does not require information on the system model parameters if the SDE system state is known via either a simulation device or real data, and (b) as it works efficiently even for high-dimensional parameters, it uses the efficient smoothed functional gradient estimator.

Interference-Constrained Optimal Power-Adaptive Amplify-and-Forward Relaying and Selection for Underlay Cognitive Radios

In an underlay cognitive radio (CR) system, a secondary user can transmit when the primary is transmitting but is subject to tight constraints on the interference it causes to the primary receiver. Amplify-and-forward (AF) relaying is an effective technique that significantly improves the performance of a CR by providing an alternate path for the secondary transmitter's signal to reach the secondary receiver. We present and analyze a novel optimal relay gain adaptation policy (ORGAP) in which the relay is interference aware and optimally adapts both its gain and transmit power as a function of its local channel gains. ORGAP minimizes the symbol error probability at the secondary receiver subject to constraints on the average relay transmit power and on the average interference caused to the primary. It is different from ad hoc AF relaying policies and serves as a new and fundamental theoretical benchmark for relaying in an underlay CR. We also develop a near-optimal and simpler relay gain adaptation policy that is easy to implement. An extension to a multirelay scenario with selection is also developed. Our extensive numerical results for single and multiple relay systems quantify the power savings achieved over several ad hoc policies for both MPSK and MQAM constellations.

Interferon-gamma induced cell death: Regulation and contributions of nitric oxide, cJun N-terminal kinase, reactive oxygen species and peroxynitrite

Interferon-gamma (Ifn gamma), a known immunomodulatory cytokine, regulates cell proliferation and survival. In this study, the mechanisms leading to the selective susceptibility of some tumor cells to Ifn gamma were deciphered. Seven different mouse tumor cell lines tested demonstrated upregulation of MHC class I to variable extents with Ifn gamma; however, only the cell lines, H6 hepatoma and L929 fibrosarcoma, that produce higher amounts of nitric oxide (NO) and reactive oxygen species (ROS) are sensitive to Ifn gamma-induced cell death. NO inhibitors greatly reduce Ifn gamma-induced ROS; however, ROS inhibitors did not affect the levels of Ifn gamma-induced NO, demonstrating that NO regulates ROS. Consequently, NO inhibitors are more effective, compared to ROS inhibitors, in reducing Ifn gamma-induced cell death. Further analysis revealed that Ifn gamma induces peroxynitrite and 3-nitrotyrosine amounts and a peroxynitrite scavenger, FeTPPS, reduces cell death. Ifn gamma treatment induces the phosphorylation of c-jun N-terminal kinase (Jnk) in H6 and L929 but not CT26, a colon carcinoma cell line, which is resistant to Ifn gamma-mediated death. Jnk activation downstream to NO leads to induction of ROS, peroxynitrite and cell death in response to Ifn gamma. Importantly, three cell lines tested, i.e. CT26, EL4 and Neuro2a, that are resistant to cell death with Ifn gamma alone become sensitive to the combination of Ifn gamma and NO donor or ROS inducer in a peroxynitrite-dependent manner. Overall, this study delineates the key roles of NO as the initiator and Jnk, ROS, and peroxynitrite as the effectors during Ifn gamma-mediated cell death. The implications of these findings in the Ifn gamma-mediated treatment of malignancies are discussed. (C) 2014 Elsevier B.V. All rights reserved.

Interferon-gamma induced cell death: Regulation and contributions of nitric oxide, cJun N-terminal kinase, reactive oxygen species and peroxynitrite

Interferon-gamma (Ifn gamma), a known immunomodulatory cytokine, regulates cell proliferation and survival. In this study, the mechanisms leading to the selective susceptibility of some tumor cells to Ifn gamma were deciphered. Seven different mouse tumor cell lines tested demonstrated upregulation of MHC class I to variable extents with Ifn gamma; however, only the cell lines, H6 hepatoma and L929 fibrosarcoma, that produce higher amounts of nitric oxide (NO) and reactive oxygen species (ROS) are sensitive to Ifn gamma-induced cell death. NO inhibitors greatly reduce Ifn gamma-induced ROS; however, ROS inhibitors did not affect the levels of Ifn gamma-induced NO, demonstrating that NO regulates ROS. Consequently, NO inhibitors are more effective, compared to ROS inhibitors, in reducing Ifn gamma-induced cell death. Further analysis revealed that Ifn gamma induces peroxynitrite and 3-nitrotyrosine amounts and a peroxynitrite scavenger, FeTPPS, reduces cell death. Ifn gamma treatment induces the phosphorylation of c-jun N-terminal kinase (Jnk) in H6 and L929 but not CT26, a colon carcinoma cell line, which is resistant to Ifn gamma-mediated death. Jnk activation downstream to NO leads to induction of ROS, peroxynitrite and cell death in response to Ifn gamma. Importantly, three cell lines tested, i.e. CT26, EL4 and Neuro2a, that are resistant to cell death with Ifn gamma alone become sensitive to the combination of Ifn gamma and NO donor or ROS inducer in a peroxynitrite-dependent manner. Overall, this study delineates the key roles of NO as the initiator and Jnk, ROS, and peroxynitrite as the effectors during Ifn gamma-mediated cell death. The implications of these findings in the Ifn gamma-mediated treatment of malignancies are discussed. (C) 2014 Elsevier B.V. All rights reserved.

BELIEF PROPAGATION FOR OPTIMAL EDGE COVER IN THE RANDOM COMPLETE GRAPH

We apply the objective method of Aldous to the problem of finding the minimum-cost edge cover of the complete graph with random independent and identically distributed edge costs. The limit, as the number of vertices goes to infinity, of the expected minimum cost for this problem is known via a combinatorial approach of Hessler and Wastlund. We provide a proof of this result using the machinery of the objective method and local weak convergence, which was used to prove the (2) limit of the random assignment problem. A proof via the objective method is useful because it provides us with more information on the nature of the edge's incident on a typical root in the minimum-cost edge cover. We further show that a belief propagation algorithm converges asymptotically to the optimal solution. This can be applied in a computational linguistics problem of semantic projection. The belief propagation algorithm yields a near optimal solution with lesser complexity than the known best algorithms designed for optimality in worst-case settings.

Regulation of Off-Network Pricing in a Nonneutral Network

Representatives of several Internet service providers (ISPs) have expressed their wish to see a substantial change in the pricing policies of the Internet. In particular, they would like to see content providers (CPs) pay for use of the network, given the large amount of resources they use. This would be in clear violation of the ``network neutrality'' principle that had characterized the development of the wireline Internet. Our first goal in this article is to propose and study possible ways of implementing such payments and of regulating their amount. We introduce a model that includes the users' behavior, the utilities of the ISP and of the CPs, and, the monetary flow that involves the content users, the ISP and CP, and, in pUrticular, the CP's revenues from advertisements. We consider various game models and study the resulting equilibria; they are all combinations of a noncooperative game (in which the ISPs and CPs determine how much they will charge the users) with a ``cooperative'' one on how the CP and the ISP share the payments. We include in our model a possible asymmetric weighting parameter (that varies between zero to one). We also study equilibria that arise when one of the CPs colludes with the TSP. We also study two dynamic game models as well as the convergence of prices to the equilibrium values.

Optimal Binary Power Control for Underlay CR With Different Interference Constraints and Impact of Channel Estimation Errors

Adapting the power of secondary users (SUs) while adhering to constraints on the interference caused to primary receivers (PRxs) is a critical issue in underlay cognitive radio (CR). This adaptation is driven by the interference and transmit power constraints imposed on the secondary transmitter (STx). Its performance also depends on the quality of channel state information (CSI) available at the STx of the links from the STx to the secondary receiver and to the PRxs. For a system in which an STx is subject to an average interference constraint or an interference outage probability constraint at each of the PRxs, we derive novel symbol error probability (SEP)-optimal, practically motivated binary transmit power control policies. As a reference, we also present the corresponding SEP-optimal continuous transmit power control policies for one PRx. We then analyze the robustness of the optimal policies when the STx knows noisy channel estimates of the links between the SU and the PRxs. Altogether, our work develops a holistic understanding of the critical role played by different transmit and interference constraints in driving power control in underlay CR and the impact of CSI on its performance.

Optimal error estimate using mesh equidistribution technique for singularly perturbed system of reaction-diffusion boundary-value problems

In this article, we study the problem of determining an appropriate grading of meshes for a system of coupled singularly perturbed reaction-diffusion problems having diffusion parameters with different magnitudes. The central difference scheme is used to discretize the problem on adaptively generated mesh where the mesh equation is derived using an equidistribution principle. An a priori monitor function is obtained from the error estimate. A suitable a posteriori analogue of this monitor function is also derived for the mesh construction which will lead to an optimal second-order parameter uniform convergence. We present the results of numerical experiments for linear and semilinear reaction-diffusion systems to support the effectiveness of our preferred monitor function obtained from theoretical analysis. (C) 2014 Elsevier Inc. All rights reserved.

An interior penalty method for distributed optimal control problems governed by the biharmonic operator

In this paper, a C-0 interior penalty method has been proposed and analyzed for distributed optimal control problems governed by the biharmonic operator. The state and adjoint variables are discretized using continuous piecewise quadratic finite elements while the control variable is discretized using piecewise constant approximations. A priori and a posteriori error estimates are derived for the state, adjoint and control variables under minimal regularity assumptions. Numerical results justify the theoretical results obtained. The a posteriori error estimators are useful in adaptive finite element approximation and the numerical results indicate that the sharp error estimators work efficiently in guiding the mesh refinement. (C) 2014 Elsevier Ltd. All rights reserved.

Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a `new paradigm' under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants. (C) 2014 Elsevier Ltd. All rights reserved.

Optimal control analysis of the dynamic growth behavior of microorganisms

Understanding the growth behavior of microorganisms using modeling and optimization techniques is an active area of research in the fields of biochemical engineering and systems biology. In this paper, we propose a general modeling framework, based on Monad model, to model the growth of microorganisms. Utilizing the general framework, we formulate an optimal control problem with the objective of maximizing a long-term cellular goal and solve it analytically under various constraints for the growth of microorganisms in a two substrate batch environment. We investigate the relation between long term and short term cellular goals and show that the objective of maximizing cellular concentration at a fixed final time is equivalent to maximization of instantaneous growth rate. We then establish the mathematical connection between the generalized framework and optimal and cybernetic modeling frameworks and derive generalized governing dynamic equations for optimal and cybernetic models. We finally illustrate the influence of various constraints in the cybernetic modeling framework on the optimal growth behavior of microorganisms by solving several dynamic optimization problems using genetic algorithms. (C) 2014 Published by Elsevier Inc.

Optimal values of bipartite entanglement in a tripartite system

For a general tripartite system in some pure state, an observer possessing any two parts will see them in a mixed state. By the consequence of Hughston-Jozsa-Wootters theorem, each basis set of local measurement on the third part will correspond to a particular decomposition of the bipartite mixed state into a weighted sum of pure states. It is possible to associate an average bipartite entanglement ((S) over bar) with each of these decompositions. The maximum value of (S) over bar is called the entanglement of assistance (E-A) while the minimum value is called the entanglement of formation (E-F). An appropriate choice of the basis set of local measurement will correspond to an optimal value of (S) over bar; we find here a generic optimality condition for the choice of the basis set. In the present context, we analyze the tripartite states W and GHZ and show how they are fundamentally different. (C) 2014 Elsevier B.V. All rights reserved.

Genome wide chromatin occupancy of mrhl RNA and its role in gene regulation in mouse spermatogonial cells

Mrhl RNA is a nuclear lncRNA encoded in the mouse genome and negatively regulates Wnt signaling in spermatogonial cells through p68/Ddx5 RNA helicase. Mrhl RNA is present in the chromatin fraction of mouse spermatogonial Gc1-Spg cells and genome wide chromatin occupancy of mrhl RNA by ChOP (Chromatin oligo affinity precipitation) technique identified 1370 statistically significant genomic loci. Among these, genes at 37 genomic loci also showed altered expression pattern upon mrhl RNA down regulation which are referred to as GRPAM (Genes Regulated by Physical Association of Mrhl RNA). p68 interacted with mrhl RNA in chromatin at these GRPAM loci. p68 silencing drastically reduced mrhl RNA occupancy at 27 GRPAM loci and also perturbed the expression of GRPAM suggesting a role for p68 mediated mrhl RNA occupancy in regulating GRPAM expression. Wnt3a ligand treatment of Gc1-Spg cells down regulated mrhl RNA expression and also perturbed expression of these 27 GRPAM genes that included genes regulating Wnt signaling pathway and spermatogenesis, one of them being Sox8, a developmentally important transcription factor. We also identified interacting proteins of mrhl RNA associated chromatin fraction which included Pc4, a chromatin organizer protein and hnRNP A/B and hnRNP A2/B1 which have been shown to be associated with lincRNA-Cox2 function in gene regulation. Our findings in the Gc1-Spg cell line also correlate with the results from analysis of mouse testicular tissue which further highlights the in vivo physiological significance of mrhl RNA in the context of gene regulation during mammalian spermatogenesis.

Optimal Pulse Width Modulation of Voltage-Source Inverter fed Motor Drives with Relaxation of Quarter Wave Symmetry Condition

Optimal switching angles for minimization of total harmonic distortion of line current (I-THD) in a voltage source inverter are determined traditionally by imposing half-wave symmetry (HWS) and quarter-wave symmetry (QWS) conditions on the pulse width modulated waveform. This paper investigates optimal switching angles with QWS relaxed. Relaxing QWS expands the solution space and presents the possibility of improved solutions. The optimal solutions without QWS are shown here to outperform the optimal solutions with QWS over a range of modulation index (M) between 0.82 and 0.94 for a switching frequency to fundamental frequency ratio of 5. Theoretical and experimental results are presented on a 2.3kW induction motor drive.

Architectural plan of transcriptional regulation in Mycobacterium tuberculosis

Transcriptional regulation enables adaptation in bacteria. Typically, only a few transcriptional events are well understood, leaving many others unidentified. The recent genome-wide identification of transcription factor binding sites in Mycobacterium tuberculosis has changed this by deciphering a molecular road-map of transcriptional control, indicating active events and their immediate downstream effects.