MARKOV DECISION PROCESSES WITH UNCERTAIN TRANSITION RATES: SENSITIVITY AND MAX HYPHEN MIN CONTROL

Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain transition rates represented as compact sets. We first consider the problem of sensitivity analysis where the aim is to quantify the range of uncertainty of the average per‐unit‐time reward given the range of uncertainty of the transition rates. We then develop solution techniques for the problem of obtaining the max‐min optimal policy, which maximizes the worst‐case average per‐unit‐time reward. In each of these problems, we distinguish between systems that can have their transition rates chosen independently and those where the transition rates depend on each other. Our solution techniques are applicable to Markov decision processes with fixed but unknown transition rates and to those with time‐varying transition rates.     

A note on productivity gains in flexible robotic cells

Flexible robotic cells combine the capabilities of robotic flow shops with those of flexible manufacturing systems. In an m-machine flexible cell, each part visits each machine in the same order. However, the m operations can be performed in any order, and each machine can be configured to perform any operation. We derive the maximum percentage increase in throughput that can be achieved by changing the assignment of operations to machines and then keeping that assignment constant throughout a lot's processing. We find that no increase can be gained in two-machine cells, and that the gain in three- and four-machine cells each is at most 14 %.     

Skeletal reduction of boundary value problems

Boundary value problems posed over thin solids are amenable to a dimensional reduction in that one or more spatial variables may be eliminated from the governing equation, resulting in significant computational gains with minimal loss in accuracy. Extant dimensional reduction techniques rely on representing the solid as a hypothetical mid‐surface plus a possibly varying thickness. Such techniques are however hard to automate since the mid‐surface is often ill‐defined and ambiguous. We propose here a skeletal representation based dimensional reduction of boundary value problems. The proposed technique has the computational advantages of mid‐surface reduction, but can be easily automated. A systematic methodology is presented for reducing boundary value problems to lower‐dimensional problems over the skeleton of a solid. The theoretical properties of the proposed method are derived, and supported by representative numerical experiments. Copyright © 2005 John Wiley & Sons, Ltd.     

Studies on dechlorination of DDT (1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethane) using magnesium/palladium bimetallic system

The aim of our investigation was to compare the rates of dechlorination of DDT using Mg0/Pd4+ system in two different reaction phases, namely, water-acetone and 0.05% biosurfactant in water. Since palladium is expensive and its toxicity effects are not well known we also examined the reuse efficiency of Pd0 immobilized on alumina for dechlorinating DDT. Studies on the dechlorination of DDT in water-acetone (1:1, v/v) and 0.05% biosurfactant phases revealed that the reaction followed second order kinetics and rate of reaction is dependent upon both initial concentrations of the target compound and Mg0/Pd4+. The presence of acid enhanced the rate of reaction by providing protons and preventing passivation of metal that occurs due to deposition of magnesium hydroxide. GC-MS analyses revealed the formation of completely dechlorinated hydrocarbon skeleton of DDT namely, diphenylethane (DPE), as the end product in both reaction phases (water-acetone and 0.05% biosurfactant in water) thereby implying the removal of all five chlorine atoms (three alkyl and two aryl) of DDT. The optimum ratio of water and acetone to facilitate successful dechlorination reaction was found to be 9:1. Results suggested that salt form (K2PdCl6) of palladium had higher potential to dechlorinate DDT as compared to pellet (Pd0-alumina) form (efficiencies of 95 and 36%, respectively, for 100 ppm initial concentration of DDT). We noted that Pd0-alumina pellets could be reused at least four times for successful dechlorination of DDT provided Mg0 granules are present in sufficient quantity. Technical grade DDT (50 ppm) containing significant amounts of DDD was dechlorinated almost completely by the Mg0/Pd4+ (10mg/0.2mg/ml) within 1h in water-biosurfactant phase. Our studies reveal that Mg/Pd system is a promising option due to its high reactivity and its ability to achieve complete dechlorination of DDT. This bimetallic system may be useful for designing indigenous permeable barriers or reactors for the treatment of DDT contaminated water.     

Frequency tunable circularly polarized antenna with branch line coupler feed network for wireless applications

In this article, frequency tuning and circularly polarized concentric circular microstrip antenna is investigated. The proposed antenna consist of varactor diode for frequency tuning and branch line coupler (BLC) feed network to achieve the circular polarization (RHCP/LHCP). By changing the varactor diode capacitance between 12.33 pF (0 V) to 1.30 pF (15 V) attain the frequency tuning (2.34‐2.68 GHz). The right hand circular polarization (RHCP) and left hand circular polarizations (LHCP) are realized in the antenna through BLC feed network output ports. The impedance bandwidth (2.05‐3.13 GHz) of BLC feed network is well‐matched with the circular microstrip antenna frequency tunable bandwidth. The proposed antenna is fabricated, and simulated results are verified using the mathematical modeling and experimental verification.     

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence,Severity, and Treatment Response

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.     

Recursive Technique For Computing System Reliability

This paper presents a simple recursive formula for calculating source-to-sink congestion, and hence reliability, in a network. This congestion is derived from individual blocking probabilities of each link. The method has an advantage of not requiring cut-sets. Such a knowledge is a prerequisite in most other methods of reliability analysis. We have programmed the algorithm using Pascal as it allows recursive procedure calls. An example illustrates the method.     

Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems

‘Meta-cognitive Radial Basis Function Network’ (McRBFN) and its ‘Projection Based Learning’ (PBL) algorithm for classification problems in sequential framework is proposed in this paper and is referred to as PBL-McRBFN. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, namely the cognitive component and the meta-cognitive component. The cognitive component is a single hidden layer radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions are considered for proper initialization of new hidden neurons, thus minimizes the misclassification. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from UCI machine learning repository and two practical problems, viz., the acoustic emission signal classification and the mammogram for cancer classification. The statistical performance evaluation on these problems has proven the superior performance of PBL-McRBFN classifier over results reported in the literature.     

Stable imposition of stiff constraints in explicit dynamics for embedded finite element methods

We investigate various strategies to enforce the kinematics at an embedded interface for transient problems within the extended finite element method. In particular, we focus on explicit time integration of the semi‐discrete equations of motion and extend both dual and primal variational frameworks for constraint enforcement to a transient regime. We reiterate the incompatibility of the dual formulation with purely explicit time integration and the severe restrictions placed by the Courant–Friedrichs–Levy condition on primal formulations. We propose an alternate, consistent formulation for the primal method and derive an estimate for the stabilization parameter, which is more amenable in an explicit dynamics framework. Importantly, the use of the new estimate circumvents the need for any tolerances as an interface approaches an element boundary. We also show that with interfacial constraints, existing mass lumping schemes can lead to prohibitively small critical time steps. Accordingly, we propose a mass lumping procedure, which provides a more favorable estimate. These techniques are then demonstrated on several benchmark numerical examples, where we compare and contrast the accuracy of the primal methods against the dual methods in enforcing the constraints. Copyright © 2012 John Wiley & Sons, Ltd.