"What is in a word? No versus Yes differentially engage the lateral orbitofrontal cortex": Correction.

Reports an error in "What is in a word? No versus Yes differentially engage the lateral orbitofrontal cortex" by Nelly Alia-Klein, Rita Z. Goldstein, Dardo Tomasi, Lei Zhang, Stephanie Fagin-Jones, Frank Telang, Gene-Jack Wang, Joanna S. Fowler and Nora D. Volkow (Emotion, 2007[Aug], Vol 7[3], 649-659). The supplemental materials link should appear as follows: http://dx.doi.org/10.1037/1528-3542.7.3.649.supp. (The following abstract of the original article appeared in record 2007-11660-018.) The words "No" and "Yes" are involved in conditioning to prohibit or encourage behavior, respectively. The authors, therefore, hypothesized that these words would be attributed to endogenous valence, activating neuronal circuits involved with valence and emotional control. Functional MRI (fMRI) at 4 Tesla was used to record regional brain activity while participants were exposed to emphatic vocalizations of the words. Results showed that No and Yes were associated with opposite brain-behavior responses; while No was negatively valenced, produced slower response times, and evoked a negative signal in the right lateral orbitofrontal cortex (OFC), Yes was positively valenced, produced faster response times, and evoked a positive signal in a contiguous region of the OFC. Attribution of negative valence to No and trait anger control were associated with increased responsivity of the OFC to No. Inasmuch as sensitivity to the prohibitive command No develops during childhood through interaction with primary caregivers as the first social objects, our findings may implicate the lateral OFC in the neurobiology of emotion regulation and subsequent social development. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Swoop: A Web Ontology Editing Browser

In this paper, we describe Swoop, a hypermedia inspired Ontology Browser and Editor based on OWL, the recently standardized Web-oriented ontology language. After discussing the design rationale and architecture of Swoop, we focus mainly on its features, using illustrative examples to highlight its use. We demonstrate that with its Web-metaphor, adherence to OWL recommendations and key unique features, such as Collaborative Annotation using Annotea, Swoop acts as a useful and efficient Web Ontology development tool. We conclude with a list of future plans for Swoop, that should further increase its overall appeal and accessibility.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

Investigation of long term drift of an intraocular pressure sensor

Microsystem Technologies - “Zero drift” behavior of an optical intraocular pressure sensor is studied using an analytical model based on the deflection of a circular membrane. Results...     

An efficient neural-network model for real-time fault detection in industrial machine

Neural Computing and Applications - Induction machines have extensive demand in industries as they are used for large-scale production and, therefore, vulnerable to both electrical and mechanical...     

Structure-property relations for a phase-pure,nanograined tetragonal zirconia ceramic stabilized with minimum CaO doping

Dense (~97%) CaO-stabilized ZrO₂ ceramic was stabilized with minimum (3 mol%) doping (reported to date) and processed via conventional sintering at a low temperature (~1200°C); compositional analysis via X-ray florescence confirmed the CaO doping accuracy. Phase-pure tetragonal structure (characterized via both X-ray diffraction and Raman spectroscopy) along with uniform nanograins (90 nm) of the ceramic ensured the evolution of no monoclinic phase even after vigorous low-temperature degradation experiments (both thermal and hydrothermal aging for 80-100 h). The sintered ceramic recorded a high hardness (~15 GPa); the indentation toughness value was also comparable to a 3 mol% yttria-stabilized zirconia system. The remarkable structure–property correlations in the 3 mol% CaO-stabilized ZrO₂ ceramic suggests that the same may be worth examining for suitable future applications (e.g., in dental ceramics).     

FRAPP: a framework for high-accuracy privacy-preserving mining

To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of individual data records have been proposed recently. In this paper, we present FRAPP, a generalized matrix-theoretic framework of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, FRAPP is used to demonstrate that (a) the prior techniques differ only in their choices for the perturbation matrix elements, and (b) a symmetric positive-definite perturbation matrix with minimal condition number can be identified, substantially enhancing the accuracy even under strict privacy requirements. We also propose a novel perturbation mechanism wherein the matrix elements are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at only a marginal reduction in accuracy. The quantitative utility of FRAPP, which is a general-purpose random-perturbation-based privacy-preserving mining technique, is evaluated specifically with regard to association and classification rule mining on a variety of real datasets. Our experimental results indicate that, for a given privacy requirement, either substantially lower modeling errors are incurred as compared to the prior techniques, or the errors are comparable to those of direct mining on the true database. A partial and preliminary version of this paper appeared in the Proc. of the 21st IEEE Intl. Conf. on Data Engineering (ICDE), Tokyo, Japan, 2005, pgs. 193–204.     

Highly Efficient Quasi 2D Blue Perovskite Electroluminescence Leveraging a Dual Ligand Composition

Perovskite light-emitting diodes (PeLEDs) are advancing because of their superior external quantum efficiencies (EQEs) and color purity. Still, additional work is needed for blue PeLEDs to achieve the same benchmarks as the other visible colors. This study demonstrates an extremely efficient blue PeLED with a 488 nm peak emission, a maximum luminance of 8600 cd m⁻², and a maximum EQE of 12.2% by incorporating the double-sided ethane-1,2-diammonium bromide (EDBr₂) ligand salt along with the long-chain ligand methylphenylammonium chloride (MeCl). The EDBr₂ successfully improves the interaction between 2D perovskite layers by reducing the weak van der Waals interaction and creating a Dion–Jacobson (DJ) structure. Whereas the pristine sample (without EDBr₂) is inhibited by small stacking number (n) 2D phases with nonradiative recombination regions that diminish the PeLED performance, adding EDBr₂ successfully enables better energy transfer from small n phases to larger n phases. As evidenced by photoluminescence (PL), scanning electron microscopy (SEM), and atomic force microscopy (AFM) characterization, EDBr₂ improves the morphology by reduction of pinholes and passivation of defects, subsequently improving the efficiencies and operational lifetimes of quasi-2D blue PeLEDs.     

A Formal Methodology for Detecting Managerial Vulnerabilities and Threats in an Enterprise Information System

From information security point of view, an enterprise is considered as a collection of assets and their interrelationships. These interrelationships may be built into the enterprise information infrastructure, as in the case of connection of hardware elements in network architecture, or in the installation of software or in the information assets. As a result, access to one element may enable access to another if they are connected. An enterprise may specify conditions on the access of certain assets in certain mode (read, write etc.) as policies. The interconnection of assets, along with specified policies, may lead to managerial vulnerabilities in the enterprise information system. These vulnerabilities, if exploited by threats, may cause disruption to the normal functioning of information systems. This paper presents a formal methodology for detection of managerial vulnerabilities of, and threats to, enterprise information systems in linear time.