Gender-related differences in susceptibility of A/J mouse to benzo[a]pyrene-induced pulmonary and forestomach tumorigenesis

A commercial patient dose verification system utilizing non-invasive metal oxide semiconductor field effect transistor (MOSFET) dosimeters originally designed for radiotherapy applications has been evaluated for use at diagnostic energy levels. The system features multiple dosimeters that may be used to monitor entrance or exit skin dose and intracavity doses in phantoms in real time. We have characterized both the standard MOSFET dosimeter designed for radiotherapy dose verification and a newly developed "high sensitivity" MOSFET dosimeter designed for lower dose measurements. The sensitivity, linearity, angular response, post-exposure response, and physical characteristics were evaluated. The average sensitivity (free in air, including backscatter) of the radiotherapy MOSFET dosimeters ranged from 3.55 x 10(4) mV per C kg(-1) (9.2 mV R(-1)) to 4.87 x 10(4) mV per C kg(-1) (12.6 mV R(-1)) depending on the energy of the x-ray field. The sensitivity of the "high sensitivity" MOSFET dosimeters ranged from 1.15 x 10(5) mV per C kg(-1) (29.7 mV R(-1)) to 1.38 x 10(5) mV per C kg(-1) (35.7 mV R(-1)) depending on the energy of the x-ray field. The high sensitivity dosimeters demonstrated excellent linearity at high energies (90 and 120 kVp) and acceptable linearity at lower energies (60 kVp). The angular response was significant for free-in-air exposures, as illustrated by the sensitivity differences between the two sides of the dosimeter, but was excellent for measurements within a tissue equivalent cylinder. The post-exposure drift response is a complicated but reproducible function of time. Real-time monitoring requires little if any corrections for the post-exposure drift response. The MOSFET dosimeter system brings some unique capabilities to diagnostic radiology dosimetry including small size, real-time capabilities, nondestructive measurement, good linearity, and a predictable angular response.     

PVC-Based 2,2,2-Cryptand Sensor for Zinc Ions

A PVC-based membrane of 2,2,2-cryptand exhibits a very good response for Zn(2+) in a wide concentration range (from 2.06 ppm to 6.54 × 10(3) ppm) with a slope of 22.0 mV/decade of Zn(2+) concentration. The response time of the sensor is <10 s, and the membrane can be used for more than 3 months without any observed divergence in potentials. The proposed sensor exhibits very good selectivity for Zn(2+) over other cations and can be used in a wide pH range (2.8-7.0). It has also been possible to use this assembly as an indicator electrode in potentiometric titrations involving zinc ions.     

Design of interval networks based on neural network and Choquet integral

Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control.     

Anonymizing bipartite graph data using safe groupings

Private data often come in the form of associations between entities, such as customers and products bought from a pharmacy, which are naturally represented in the form of a large, sparse bipartite graph. As with tabular data, it is desirable to be able to publish anonymized versions of such data, to allow others to perform ad hoc analysis of aggregate graph properties. However, existing tabular anonymization techniques do not give useful or meaningful results when applied to graphs: small changes or masking of the edge structure can radically change aggregate graph properties. We introduce a new family of anonymizations for bipartite graph data, called (k, ℓ)-groupings. These groupings preserve the underlying graph structure perfectly, and instead anonymize the mapping from entities to nodes of the graph. We identify a class of “safe” (k, ℓ)-groupings that have provable guarantees to resist a variety of attacks, and show how to find such safe groupings. We perform experiments on real bipartite graph data to study the utility of the anonymized version, and the impact of publishing alternate groupings of the same graph data. Our experiments demonstrate that (k, ℓ)-groupings offer strong tradeoffs between privacy and utility.     

Layered Approach Using Conditional Random Fields for Intrusion Detection

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.     

Diffusion of fluid confined to nanotube with rectangular cross section

A dynamical model is proposed to study self-diffusion coefficient by confining the fluid in rectangular nanotube. The theoretical model is based on the consideration that the confinement affects the movement at atomic level. The model predicts that the diffusion parallel to walls of channel is different from that of diffusion perpendicular to the walls. Near the walls the dynamics of fluid has been found to slow down to an extent that below a certain value of ratio of width to the diameter of particle, the molecules behave as if these belong to solid. The results are contrasted with the result obtained from the model based on similar considerations for a fluid confined only in one direction. It is found that tendency of freezing near the wall increases due to confinement from second direction. Empirical relation which governs the behavior of diffusion coefficient as function of distance from the confining walls has also been proposed. The effect of confinement is more pronounced for denser fluids than for dilute fluid.     

Closed-loop identification using direct approach and high order ARX/GOBF-ARX models

Model accuracy plays a key role in the performance of advanced, model predictive control algorithms. Model fidelity is usually affected by routine operating condition changes, which necessitate reidentification. From several theoretical and practical considerations, it is recommended that such re-identification be performed under closed-loop conditions. The direct approach for closed-loop identification, owing to its simplicity, is better suited for MPC. In order to yield unbiased and consistent parameter estimates, however, this approach requires the noise model to be sufficiently parameterized. Towards this objective, high order ARX models are the most suitable candidates from the viewpoint of ease of parameter estimation. For multivariable systems, however, the identification of high order ARX models would require longer experiments to be performed. This being undesirable from a practical viewpoint, there is a need for a parsimonious parameterization that would retain the benefits of high order ARX models. In this work, we propose to use generalized orthonormal basis filters (GOBFs) to achieve this parsimonous parameterization. Further, we propose an approach to obtain reduced order models by emphasizing important frequencies so as to suitably shape the bias. We also show that the choice of the GOBF parameterization has another important merit, viz. their ability to perform well even with minimal perturbation data or short experiment times. The efficacy of the proposed approach is demonstrated via simulations on the benchmark Shell Control Problem and a laboratory quadruple tank setup.     

Skyline and mapping aware join query evaluation

Growing interests in multi-criteria decision support applications have resulted in a flurry of efficient skyline algorithms. In practice, real-world decision support applications require to access data from disparate sources. Existing techniques define the skyline operation to work on a single set, and therefore, treat skylines as an “add-on” on top of a traditional Select-Project-Join query plan. In many real-world applications, the skyline dimensions can be anti-correlated such as the attribute pair {price, mileage} for cars and {price, distance} for hotels. Anti-correlated data are particularly challenging for skyline evaluation and therefore have commonly been ignored by existing techniques. In this work, we propose a robust execution framework called SKIN to evaluate skyline over joins. The salient features of SKIN are: (a) effective in reducing the two primary costs, namely the cost of generating the join results and the cost of dominance comparisons to compute the final skyline of join results, (b) shown to be robust for both skyline-friendly (independent and correlated) as well as skyline-unfriendly (anti-correlated) data distributions. SKIN is effective in exploiting the skyline knowledge in both local within individual data sources and across disparate sources—to significantly reduce the above-mentioned costs incurred during the evaluation of skyline over join. Our experimental study demonstrates the superiority of our proposed approach over state-of-the-art techniques to handle a wide variety of data distributions.     

Quintic nonpolynomial spline method for the solution of a second-order boundary-value problem with engineering applications

Nonpolynomial quintic spline functions are used to develop a numerical algorithm for computing an approximation to the solution of a system of second order boundary value problems associated with heat transfer. We show that the approximate solutions obtained by our algorithm are better than those produced by other spline and domain decomposition methods. A comparison of our algorithm with nonpolynomial quadratic spline method is discussed with the help of two numerical examples.