The Effect of Moisture Content in Epoxy Film Adhesives on their Performance. II. T-Peel and 105°C Lap Shear Strength

The benefits of adhesively bonded structures are well known. However, the most significant factor limiting the extensive application of metal bonding in primary aerospace structures is the prevailing lack of confidence in its long term durability under hostile environmental conditions.^1,2     

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition‐primed decision model

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well‐known human decision‐making model called Recognition‐Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 827–845, 2007.     

Knowledge, data, and models

The object-oriented approach to integrating knowledge data, and models is examined. Under the object-oriented paradigm, everything is an object, which is itself an encapsulation of data and methods to manipulate and access the data within the object. Thus knowledge, data, and models can be defined as special kinds of objects. Active knowledge/data language (KDL), a functional object-oriented database system is considered, covering the active-KDL-type system and functions, and query-driven simulation     

Camcorders

The author explains from an engineering standpoint, how some of the camcorder components work. He discusses the solid-state image sensor, the photodetector, scanning, the shutter frame rate, optics, the recording mechanism, the viewfinder, special effects, and automatic features. Motion detection, discrimination, and compensation are also covered. Recommendations as to which features are most useful are made     

Assessing Human Impact of Organizational Crises: Reliability and Validity of the Triage Assessment Scale for Organizations (TAS:O)

The present study evaluated the reliability and validity of the Triage Assessment Survey: Organizations (TAS:O), a 27-item, 5-point, Likert summated rating scale. One hundred and seventeen participants responded to the TAS:O after reading mild, moderate, marked and severe organizational crisis scenarios. The overall Cronbach's alpha and split-half reliability were both .93. Confirmatory factor analysis revealed three factors, supporting the hypothesis that the TAS:O is comprised of three distinct factors (i.e., Affect, Behavior, and Cognition). An analysis of variance provided evidence that the TAS:O has the capacity to distinguish among mild, moderate, marked, and severe crises. Because this research is the first to evaluate the TAS:O, further studies are needed to strengthen confidence in the psychometric properties of this scale.     

Temperature and modulation characteristics of resonant-cavitylight-emitting diodes

Resonant-cavity light-emitting diodes (RCLED) are novel, high-efficiency light-emitting diodes which employ optical microcavities. These diodes have higher intensities and higher spectral purity as compared to conventional LEDs. Analytical formulas are derived for the enhancement of the spontaneous emission along the optical axis of the cavity. The design rules for high-efficiency operation of RCLEDs are established. The temperature dependence of the emission intensity is analyzed in the range 20-80° and it is described by an exponential dependence with a characteristic temperature of 112 K. The modulation characteristics of RCLEDs exhibit 3 dB frequencies of 580 MHz. Eye diagrams at transmission rates of 622 Mb/s are wide open indicating the suitability of RCLEDs for high-speed data transmission     

A 16-Kbit Θ-search associative memory

The design of a high-capacity Θ-search associative memory (Θ∈{<,>,⩽,⩾,=≠}) is presented. PSPICE simulation and layouts show that the proposed Θ-search associative memory chip consisting of 256 words, each 64-b long, can fit on a 13.5-mm×9.5-mm chip. It can perform maskable Θ-search operations over its contents in 110 ns     

Bilateral arachnoid cysts, seizures and severe encephalopathy: case report

A case is presented of a 3 1/2-year-old girl with a clinical picture of very severe psychomotor retardation, autistic behavior, and repetitive convulsive episodes starting in the neonatal period and resistant to any pharmacological treatment. Electroencephalography showed generalized abnormalities. Magnetic resonance imaging of the brain disclosed arachnoid cysts in both temporal fossae with marked hypoplasia of both temporal lobes. It is proposed that the severity of symptoms is related to the magnitude of underlying anatomical lesions as well as the associated intractable epilepsy.     

A Maximum Entropy Approach for Collaborative Filtering

Collaborative filtering (CF) involves predicting the preferences of a user for a set of items given partial knowledge of the user's preferences for other items, while leveraging a database of profiles for other users. CF has applications e.g. in predicting Web sites a person will visit and in recommending products. Fundamentally, CF is a pattern recognition task, but a formidable one, often involving a huge feature space, a large data set, and many missing features. Even more daunting is the fact that a CF inference engine must be capable of predicting any (user-selected) items, given any available set of partial knowledge on the user's other preferences. In other words, the model must be designed to solve any of a huge (combinatoric) set of possible inference tasks. CF techniques include memory-based, classification-based, and statistical modelling approaches. Among these, modelling approaches scale best with large data sets and are the most adept at handling missing features. The disadvantage of these methods lies in the statistical assumptions (e.g. feature independence), which may be unjustified. To address this shortcoming we propose a new model-based CF method, based on the maximum entropy principle. For the MS Web application, the new method is demonstrated to outperform a number of CF approaches, including naive Bayes and latent variable (cluster) models, support vector machines (SVMs), and the (Pearson) correlation method.