Comparison of mass transfer efficiency in horizontal rotating packed beds and rotating biological contactors

In this study, the mass transfer efficiencies of a novel horizontal rotating packed (h‐RPB) bed and the conventional disc‐type rotating biological contactor (RBC) were studied at four speeds and seven submergences. Pall rings of two different sizes (25, 38 mm), superintalox saddles and a wiremesh spiral bundle were used as packings in the h‐RPB. Volumetric gas–liquid mass transfer coefficients were determined by unsteady state absorption of atmospheric oxygen in de‐aerated water. Power consumption per unit liquid volume has been found for all geometries tested. The oxygen transfer efficiency values for the h‐RPB were found to be 2–5 kg kWh^?1 and for the disc RBC were found to be 1–2 kg kWh^?1. The performance of the h‐RPB was also compared with other gas–liquid contactors such as surface aerators. The study proves that the h‐RPB is a energy efficient alternative to conventional contactors. Copyright © 2005 Society of Chemical Industry     

A microfluidic device for thermal particle detection

We demonstrate the use of heat to count microscopic particles. A thermal particle detector (TPD) was fabricated by combining a 500-nm-thick silicon nitride membrane containing a thin-film resistive temperature detector with a silicone elastomer microchannel. Particles with diameters of 90 and 200 μm created relative temperature changes of 0.11 and ?0.44 K, respectively, as they flowed by the sensor. A first-order lumped thermal model was developed to predict the temperature changes. Multiple particles were counted in series to demonstrate the utility of the TPD as a particle counter.     

A New Method for Getting Rational Approximation for Fractional Order Differintegrals

In this paper a new algorithm is presented to calculate the poles and zeros to approximate a fractional order (FO) differintegral (s^±α,α∈(0,1)) by a rational function on a finite frequency band ω∈(ω_l,ω_h). The constant phase property of the FO differintegral is the basis for development of the algorithm. Interlacing of real poles and zeros is used to achieve the constant phase. The calculations are done using the asymptotic Bode phase plot. A brief investigation is made to get a good approximation for the Bode phase plot. Two design parameters are introduced to keep the average phase close to the desired phase angle and to keep the error within the allowed bounds. A study is done to empirically understand the relationship between the error and the design parameters. The results thus obtained help in the further calculations. The algorithm is computationally simple and inexpensive, and gives a fairly good approximation of fractance frequency response on the specified frequency band.     

Mixed-Mode Stress Intensity Factor Determination of Riveted Lap Joints Used in Aircraft Fuselage Structures

Journal of Failure Analysis and Prevention - In the present work, mixed-mode stress intensity factor (SIF) of multiple cracks in a riveted lap joint has been determined, with and without the...     

A simulation study on the US rehabilitation service process for patients with modest-to-severe traumatic brain injury

Traumatic brain injury (TBI) is a devastating injury with severe consequences. In this paper, we conduct a simulation study on the commonly implemented care delivery process for TBI rehabilitation in the US, which covers three care categories: inpatient acute, outpatient sub-acute and general residential care. Our investigation is focused on assessing how coverage duration of publicly funded rehabilitation impacts two key system outcomes: sub-acute rehabilitation readmission and total rehabilitation spending. We develop prediction models on the above two outcomes for patients of different conditions. We introduce the notions of forceful transition and medical necessity adjustment, and embed the notions in a state-transition simulation model. Our simulation results suggest that to minimise the care spending, the duration of publicly insured outpatient sub-acute rehab be set smaller than what is currently implemented but not to the point where coverage should be completely removed. Our sensitivity analysis justifies the robustness of our results under variations on model parameters.     

Parameter identification in dynamic systems using the homotopy optimization approach

Identifying the parameters in a mathematical model governed by a system of ordinary differential equations is considered in this work. It is assumed that only partial state measurement is available from experiments, and that the parameters appear nonlinearly in the system equations. The problem of parameter identification is often posed as an optimization problem, and when deterministic methods are used for optimization, one often converges to a local minimum rather than the global minimum. To mitigate the problem of converging to local minima, a new approach is proposed for applying the homotopy technique to the problem of parameter identification. Several examples are used to demonstrate the effectiveness of the homotopy method for obtaining global minima, thereby successfully identifying the system parameters.     

ILP-assisted de novo drug design

The accurate and early detection of epileptic seizures in continuous electroencephalographic (EEG) data has a growing role in the management of patients with epilepsy. Early detection allows for therapy to be delivered at the start of seizures and for caregivers to be notified promptly about potentially debilitating events. The challenge to detecting epileptic seizures, however, is that seizure morphologies exhibit considerable inter-patient and intra-patient variability. While recent work has looked at addressing the issue of variations across different patients (inter-patient variability) and described patient-specific methodologies for seizure detection, there are no examples of systems that can simultaneously address the challenges of inter-patient and intra-patient variations in seizure morphology. In our study, we address this complete goal and describe a multi-task learning approach that trains a classifier to perform well across many kinds of seizures rather than potentially overfitting to the most common seizure types. Our approach increases the generalizability of seizure detection systems and improves the tradeoff between latency and sensitivity versus false positive rates. When compared against the standard approach on the CHB–MIT multi-channel scalp EEG data, our proposed method improved discrimination between seizure and non-seizure EEG for almost 83 % of the patients while reducing false positives on nearly 70 % of the patients studied.     

Quantum Complexity of Testing Group Commutativity

We consider the problem of testing the commutativity of a black-box group specified by its k generators. The complexity (in terms of k) of this problem was first considered by Pak, who gave a randomized algorithm involving O(k) group operations. We construct a quite optimal quantum algorithm for this problem whose complexity is in . The algorithm uses and highlights the power of the quantization method of Szegedy. For the lower bound of , we give a reduction from a special case of Element Distinctness to our problem. Along the way, we prove the optimality of the algorithm of Pak for the randomized model.     

Indexing,Elaboration and Refinement: Incremental Learning of Explanatory Cases

This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner's memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good lessons to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices. We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program.     

On the Hitting Times of Quantum Versus Random Walks

The hitting time of a classical random walk (Markov chain) is the time required to detect the presence of—or equivalently, to find—a marked state. The hitting time of a quantum walk is subtler to define; in particular, it is unknown whether the detection and finding problems have the same time complexity. In this paper we define new Monte Carlo type classical and quantum hitting times, and we prove several relationships among these and the already existing Las Vegas type definitions. In particular, we show that for some marked state the two types of hitting time are of the same order in both the classical and the quantum case. Then, we present new quantum algorithms for the detection and finding problems. The complexities of both algorithms are related to the new, potentially smaller, quantum hitting times. The detection algorithm is based on phase estimation and is particularly simple. The finding algorithm combines a similar phase estimation based procedure with ideas of Tulsi from his recent theorem (Tulsi A.: Phys. Rev. A 78:012310 2008) for the 2D grid. Extending his result, we show that we can find a unique marked element with constant probability and with the same complexity as detection for a large class of quantum walks—the quantum analogue of state-transitive reversible ergodic Markov chains. Further, we prove that for any reversible ergodic Markov chain P, the quantum hitting time of the quantum analogue of P has the same order as the square root of the classical hitting time of P. We also investigate the (im)possibility of achieving a gap greater than quadratic using an alternative quantum walk. In doing so, we define a notion of reversibility for a broad class of quantum walks and show how to derive from any such quantum walk a classical analogue. For the special case of quantum walks built on reflections, we show that the hitting time of the classical analogue is exactly the square of the quantum walk.