The Impact of Stochastic Convenience Yield on Long-term Forestry Investment Decisions

This paper investigates whether convenience yield is an important factor in determining optimal decisions for a forestry investment. The Kalman filter method is used to estimate three different models of lumber prices: a mean reverting model, a simple geometric Brownian motion and the two-factor price model due to Schwartz (1997). In the latter model there are two correlated stochastic factors: spot price and convenience yield. The two-factor model is shown to provide a reasonable fit of the term structure of lumber futures prices. The impact of convenience yield on a forestry investment decision is examined using the Schwartz (1997) long-term model which transforms the two-factor price model into a single factor model with a composite price. Using the long-term model an optimal harvesting problem is analyzed, which requires the numerical solution of an impulse control problem formulated as a Hamilton-Jacobi-Bellman Variational Inequality. We compare the results for the long-term model to those from single-factor mean reverting and geometric Brownian motion models. The inclusion of convenience yield through the long-term model is found to have a significant impact on land value and optimal harvesting decisions.

Load Bearing Characteristics Of Implants For Osteochondral Defect Repair

Objective: To measure changes in joint contact mechanics, during simulated gait, in the presence of a medial femoral osteochondral defect and after filling the defect using two different polyvinyl alcohol implant configurations. Methods: Seven human cadaveric knees were tested under simulated gait, while the contact stresses on the tibial plateau were recorded using an electronic sensor. Each knee was tested using the following conditions: intact, defect, and after the defect has been filled with either 10% PVA, 20% PVA, 10% PVA + a porous titanium base, or 20% PVA + porous titanium base. Changes in contact area, total force, weight center of contact, and stress pattern differences were measured for each knee. Results: At 14% of the gait cycle, there were no changes in contact area observed between conditions. At 45% of the gait cycle, differences were seen in the meniscal-cartilage contact area with increases in contact area between the intact and 10% PVA as well as 20% PVA scaffolds. At 14% of gait, there was a significant increase in total force between intact and defect conditions and between defect and 20% PVA + pTi in the menical-cartilage region with forces of 179 ± 113 N, 278 ± 113 N, and 193 ± 96 N for the intact, defect, and 20% PVA + pTi respectively. At 45% of gait, there was a significant difference in total force between intact condition and the defect condition in the meniscal-cartilage contact area with average total force of 90 ± 73 N and 148 ± 75 N respectively. Differences were found in the cartilage-cartilage total force at 45% of gait between intact and all other conditions and between defect and 20% PVA + pTi. The total forces were 486 ± 134 N for the intact, 360 ± 158 N for the defect, and 431 ± 177 for the 20% PVA + pTi, and the remaining implants tested having total force values below 412 N. Conclusions: The presence of an osteochondral defect causes an increase in loading on the meniscus. Implants in the range of tissue engineered constructs can partially restore joint loading but cause alterations in contact stress patterns

Register automata with linear arithmetic

We propose a novel automata model over the alphabet of rational numbers, which we call register automata over the rationals (RA-Q). It reads a sequence of rational numbers and outputs another rational number. RA-Q is an extension of the well-known register automata (RA) over infinite alphabets, which are finite automata equipped with a finite number of registers/variables for storing values. Like in the standard RA, the RA-Q model allows both equality and ordering tests between values. It, moreover, allows to perform linear arithmetic between certain variables. The model is quite expressive: in addition to the standard RA, it also generalizes other well-known models such as affine programs and arithmetic circuits. The main feature of RA-Q is that despite the use of linear arithmetic, the so-called invariant problem---a generalization of the standard non-emptiness problem---is decidable. We also investigate other natural decision problems, namely, commutativity, equivalence, and reachability. For deterministic RA-Q, commutativity and equivalence are polynomial-time inter-reducible with the invariant problem

Optimal Charging of Electric Vehicles in Smart Grid: Characterization and Valley-Filling Algorithms

Electric vehicles (EVs) offer an attractive long-term solution to reduce the dependence on fossil fuel and greenhouse gas emission. However, a fleet of EVs with different EV battery charging rate constraints, that is distributed across a smart power grid network requires a coordinated charging schedule to minimize the power generation and EV charging costs. In this paper, we study a joint optimal power flow (OPF) and EV charging problem that augments the OPF problem with charging EVs over time. While the OPF problem is generally nonconvex and nonsmooth, it is shown recently that the OPF problem can be solved optimally for most practical power networks using its convex dual problem. Building on this zero duality gap result, we study a nested optimization approach to decompose the joint OPF and EV charging problem. We characterize the optimal offline EV charging schedule to be a valley-filling profile, which allows us to develop an optimal offline algorithm with computational complexity that is significantly lower than centralized interior point solvers. Furthermore, we propose a decentralized online algorithm that dynamically tracks the valley-filling profile. Our algorithms are evaluated on the IEEE 14 bus system, and the simulations show that the online algorithm performs almost near optimality ($<1$ relative difference from the offline optimal solution) under different settings.Comment: This paper is temporarily withdrawn in preparation for journal submissio

Compressive Channel Estimation and Multi-user Detection in C-RAN

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). Assuming that active users are sparse in the network, we solve CE and MUD problems with compressed sensing (CS) technology to greatly reduce the long identification pilot overhead. A mixed L{2,1}-regularization functional for extended sparse group-sparsity recovery is proposed to exploit the inherently sparse property existing both in user activities and remote radio heads (RRHs) that active users are attached to. Empirical and theoretical guidelines are provided to help choosing tuning parameters which have critical effect on the performance of the penalty functional. To speed up the processing procedure, based on alternating direction method of multipliers and variable splitting strategy, an efficient algorithm is formulated which is guaranteed to be convergent. Numerical results are provided to illustrate the effectiveness of the proposed functional and efficient algorithm.Comment: 6 pages, 3 figure