Optimal capital investment under uncertainty: An extension

This paper develops a model for optimal capital investment in continuous time when both existing and new capital stocks are subject to uncertainty. The model is generalized to allow for large and infrequent changes in the dynamics of the capital stock, which may arise as a result of natural and man-made disasters.

Negative Differential Resistance of Oligo(Phenylene Ethynylene) Self-Assembled Monolayer Systems: The Electric-Field-Induced Conformational Change Mechanism

We investigate here a possible mechanism for the room temperature negative differential resistance (NDR) in the Au/AN-OPE/RS/Hg self-assembled monolayer (SAM) system, where AN-OPE = 2′-amino,5′-nitro-oligo(phenylene ethynylene) and RS is a C_(14) alkyl thiolate. Kiehl and co-workers showed that this molecular system leads to NDR with hysteresis and sweep-rate-dependent position and amplitude in the NDR peak. To investigate a molecular basis for this interesting behavior, we combine first-principles quantum mechanics (QM) and mesoscale lattice Monte Carlo methods to simulate the switching as a function of voltage and voltage rate, leading to results consistent with experimental observations. This simulation shows how the structural changes at the microscopic level lead to the NDR and sweep-rate-dependent macroscopic I−V curve observed experimentally, suggesting a microscopic model that might aid in designing improved NDR systems

On Expectations-Driven Business Cycles in Economies with Production Externalities: A Comment

Eusepi (2009, International Journal of Economic Theory 5, pp. 9-23) analytically finds that a one-sector real business cycle model may exhibit positive co-movement between consumption and investment when the equilibrium wage-hours locus is positively-sloped and steeper than the household's labor supply curve. However, we show that this condition does not imply expectations-driven business cycles will emerge in Eusepi's model. Specifically, a positive news shock about future productivity improvement leads to an aggregate recession whereby output, employment, consumption and investment all fall in the announcement period.Expectations-Driven Business Cycles; Production Externalities.

Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY

BACKGROUND: Chemical shift mapping is an important technique in NMR-based drug screening for identifying the atoms of a target protein that potentially bind to a drug molecule upon the molecule's introduction in increasing concentrations. The goal is to obtain a mapping of peaks with known residue assignment from the reference spectrum of the unbound protein to peaks with unknown assignment in the target spectrum of the bound protein. Although a series of perturbed spectra help to trace a path from reference peaks to target peaks, a one-to-one mapping generally is not possible, especially for large proteins, due to errors, such as noise peaks, missing peaks, missing but then reappearing, overlapped, and new peaks not associated with any peaks in the reference. Due to these difficulties, the mapping is typically done manually or semi-automatically, which is not efficient for high-throughput drug screening. RESULTS: We present PeakWalker, a novel peak walking algorithm for fast-exchange systems that models the errors explicitly and performs many-to-one mapping. On the proteins: hBcl(XL), UbcH5B, and histone H1, it achieves an average accuracy of over 95% with less than 1.5 residues predicted per target peak. Given these mappings as input, we present PeakAssigner, a novel combined structure-based backbone resonance and NOE assignment algorithm that uses just (15)N-NOESY, while avoiding TOCSY experiments and (13)C-labeling, to resolve the ambiguities for a one-to-one mapping. On the three proteins, it achieves an average accuracy of 94% or better. CONCLUSIONS: Our mathematical programming approach for modeling chemical shift mapping as a graph problem, while modeling the errors directly, is potentially a time- and cost-effective first step for high-throughput drug screening based on limited NMR data and homologous 3D structures

The roll back chip: hardware support for distributed simulation using time warp

Journal ArticleDistributed simulation offers an attractive means of meeting the high computational demands of discrete event simulation programs. The Time Warp mechanism has been proposed to ensure correct sequencing of events in distributed simulation programs without blocking processes unnecessarily. However, the overhead of state saving and rollback in Time Warp is one obstacle that may severely degrade performance. A special purpose hardware component, the rollback chip (RBC), is proposed to manage the state of a processor and provide an efficient rollback mechanism within a node of a parallel computer. The chip may be viewed as a special purpose memory management unit that lies on the data path between processor and memory. The algorithm implemented by the rollback chip is described, as well as extensions to the basic design. Implementation of the chip is briefly discussed. In addition to distributed simulation, the rollback chip may be used in other applications using the Time Warp mechanism, notably distributed database concurrency control

Design and evaluation of the rollback chip: special purpose hardware for time warp

technical reportThe Time Warp mechanism offers an elegant approach to attacking difficult clock synchronization problems that arise in applications such as parallel discrete event simulation. However, because Time Warp relies on a lookahead and rollback mechanism to achieve widespread exploitation of parallelism, the state of each process must periodically be saved. Existing approaches to implementing state saving and rollback are not appropriate for large Time Warp programs. We propose a component called the rollback chip (RBC) to efficiently implement these functions. Such a component could be used in a programmable, special purpose parallel discrete event simulation engine based on Time Warp. The algorithms implemented by the rollback chip are described, as well as mechanisms that allow efficient implementation. Results of simulation studies are presented that show that the rollback chip can virtually eliminate the state saving and rollback overheads that plague current software implementations of Time Warp. Index terms ? state saving, rollback, Time Warp, parallel discrete event simulation, VLSI component, special purpose computers

Estimation of local concentration from measurements of stochastic adsorption dynamics using carbon nanotube-based sensors

This paper proposes a maximum likelihood estimation (MLE) method for estimating time varying local concentration of the target molecule proximate to the sensor from the time profile of monomolecular adsorption and desorption on the surface of the sensor at nanoscale. Recently, several carbon nanotube sensors have been developed that can selectively detect target molecules at a trace concentration level. These sensors use light intensity changes mediated by adsorption or desorption phenomena on their surfaces. The molecular events occurring at trace concentration levels are inherently stochastic, posing a challenge for optimal estimation. The stochastic behavior is modeled by the chemical master equation (CME), composed of a set of ordinary differential equations describing the time evolution of probabilities for the possible adsorption states. Given the significant stochastic nature of the underlying phenomena, rigorous stochastic estimation based on the CME should lead to an improved accuracy over than deterministic estimation formulated based on the continuum model. Motivated by this expectation, we formulate the MLE based on an analytical solution of the relevant CME, both for the constant and the time-varying local concentrations, with the objective of estimating the analyte concentration field in real time from the adsorption readings of the sensor array. The performances of the MLE and the deterministic least squares are compared using data generated by kinetic Monte Carlo (KMC) simulations of the stochastic process. Some future challenges are described for estimating and controlling the concentration field in a distributed domain using the sensor technology.Korea (South). Ministry of Science, ICT and Future Planning (Advanced Biomass R&D Center (ABC) of Global Frontier Project

Fast and Robust Mathematical Modeling of NMR Assignment Problems

NMR spectroscopy is not only for protein structure determination, but also for drug screening and studies of dynamics and interactions. In both cases, one of the main bottleneck steps is backbone assignment. When a homologous structure is available, it can accelerate assignment. Such structure-based methods are the focus of this thesis. This thesis aims for fast and robust methods for NMR assignment problems; in particular, structure-based backbone assignment and chemical shift mapping. For speed, we identified situations where the number of 15N-labeled experiments for structure-based assignment can be reduced; in particular, when a homologous assignment or chemical shift mapping information is available. For robustness, we modeled and directly addressed the errors. Binary integer linear programming, a well-studied method in operations research, was used to model the problems and provide practically efficient solutions with optimality guarantees. Our approach improved on the most robust method for structure-based backbone assignment on 15N-labeled data by improving the accuracy by 10% on average on 9 proteins, and then by handling typing errors, which had previously been ignored. We show that such errors can have a large impact on the accuracy; decreasing the accuracy from 95% or greater to between 40% and 75%. On automatically picked peaks, which is much noisier than manually picked peaks, we achieved an accuracy of 97% on ubiquitin. In chemical shift mapping, the peak tracking is often done manually because the problem is inherently visual. We developed a computer vision approach for tracking the peak movements with average accuracy of over 95% on three proteins with less than 1.5 residues predicted per peak. One of the proteins tested is larger than any tested by existing automated methods, and it has more titration peak lists. We then combined peak tracking with backbone assignment to take into account contact information, which resulted in an average accuracy of 94% on one-to-one assignments for these three proteins. Finally, we applied peak tracking and backbone assignment to protein-ligand docking to illustrate the potential for fast 3D complex determination

Physician-prescribed Asthma Treatment Regimen does not differ Between Smoking and Non-smoking Patients With Asthma in Seoul and Gyunggi province of Korea

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ACKNOWLEDGMENTS The authors thank Lauren Weisenfluh and Melissa Stauffer, PhD, in collaboration with SCRIBCO, for medical writing assistance. Funding for this research was provided by Merck & Co., Inc. The authors also wish to thank Eric Maiese and Sharlette Everett for their contributions to the design and implementation of the study and the analytic plan. The authors would also like to thank the study investigators who contributed to patient enrollment and data collection: Drs. Young Il Hwang (Hallym University Sacred Heart Hospital), Young Min Ye (Ajou University Medical Center), Joo Hee Kim (Ajou University Medical Center), Heung Woo Park (Seoul National University Hospital), Tae Wan Kim (Seoul National University Hospital), Jae Jeong Shim (Korea University Guro Hospital), Gyu Young Hur (Korea University Guro Hospital), Soo Taek Uh (SoonChunHyang University Hospital), Sang Ha Kim (Wonju Christian Hospital), Myoung Kyu Lee (Wonju Christian Hospital), Soo Keol Lee (Dong-A Medical Center), Jin Hong Chung (Yeungnam University Medical Center), Kyu Jin Kim (Yeungnam University Medical Center), Young Koo Jee (Dankook University Hospital), Kyung Mook Kim (Dankook University Hospital), Young Il Koh (Chonnam National University Hospital), Cheol Woo Kim (Inha university Hospital), You Sook Cho (Seoul Asan Medical Center), Tae Bum Kim (Seoul Asan Medical Center), Jae Myung Lee (Myeong Internal Medicine), Young Mok Lee (Good Friends Internal Medicine), Bong Chun Lee (Namsan Hospital), So Yoen Park (A&A Clinic).Peer reviewedPublisher PD

The Effects of Safety Information on Aeronautical Decision Making

The importance of aeronautical decision making (ADM) has been considered one of the most critical issues of flight education for future professional pilots. Researchers have suggested that a safety information system based on information from incidents and near misses is an important tool to improve the intelligence and readiness of pilots. This paper describes a study that examines the effect of safety information on aeronautical decision making for students in a collegiate flight program. Data was collected from study participants who were exposed to periodic information about local aircraft malfunctions. Participants were then evaluated using a flight simulator profile and a pen and pencil test of situational judgment. Findings suggest that regular access to the described safety information program significantly improves decision making of student pilots