In this paper we propose and test a valuation methodology for improving the efficiency of contingent claims pricing using Artificial Neural Networks (ANN). Contingent claims is by now a standard method for pricing under uncertainty nonlinear (option embedded) contracts, for both financial options (standardized or customized) and real (investment) opportunities. In the presence of liquid option ...

This paper integrates competitive pricing and network design problems for the short life cycle products. The pricing problem determines selling prices of the products for different life cycle phases in a competitive market, as well as acquisition management of returned products. Besides, the selling and acquisition prices are related to the distance between distribution centers and customers. T...

We formulate optimization problems to study how data centers might modulate their power demands for cost-effective operation taking into account three key complex features exhibited by real-world electricity pricing schemes: (i) time-varying prices (e.g., time-of-day pricing, spot pricing, or higher energy prices during ”coincident” peaks) and (ii) separate charge for peak power consumption. Ou...

Recent research on pricing multiclass loss networks [19] has shown that the performance of optimal static pricing approaches that of optimal dynamic (congestion-dependent) pricing in the many small sources limit. In our own work with similar models, we have found it difficult to obtain large gains over static pricing in realistic settings, even when the many small sources assumption is violated...

We introduce a novel delay pricing and charging scheme in deadline-based networks to support real-time data delivery. We study the delay performance observed by individual users when different levels of deadline urgency are specified and take a market-based approach to determining a delay price. Our pricing and charging scheme can be used to prevent greedy users from gaining an advantage by spe...

an adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. another key feature of this structure is the fact that, it does not need model of the system. in this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  online training of neurolinearizer is compared to model predictive recurrent training...

The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion which in turn affects air pollution. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable to ameliorate and contr...

background: the aim of the current study was to assess the mrna levels of two mitochondria-related genes, including nuclear-encoded nrf1 (nuclear respiratory factor 1), mitochondrial transcription factor a (tfam), and mitochondrial-encoded cytochrome c oxidase subunit 1 (mt-co1) genes in various stages of the human oocyte maturation. methods: oocytes were obtained from nine infertile women with...