Overview of a Modal-Based Condition Assessment Procedure

Condition assessment is a term that is used to describe the process of characterizing the physical condition of constructed systems. This paper summarizes a condition assessment (CA) procedure based on a complete system of field-testing, finite element (FE) modeling, and load rating. Experimental techniques, including both modal testing and truckload testing, are used to collect measurements of the constructed systems. The basic mechanism and procedure of the FE modeling and calibration are presented. Different physical parameters of FE models are adjusted during the calibration process using both static and dynamic responses as criteria to achieve convergence between experimental measurements and analytical results using carefully developed objective functions. Finally, a bridge load rating is completed on the basis of the calibrated model. These developments are described and illustrated using a representative bridge as an example.     

The Mediating Role of Self-Efficacy in the Development of Entrepreneurial Intentions.

The purpose of this study was to investigate the mediating role of self-efficacy in the development of students' intentions to become entrepreneurs. The authors used structural equation modeling with a sample of 265 master of business administration students across 5 universities to test their hypotheses. The results showed that the effects of perceived learning from entrepreneurship-related courses, previous entrepreneurial experience, and risk propensity on entrepreneurial intentions were fully mediated by entrepreneurial self-efficacy. Contrary to expectations, gender was not mediated by self-efficacy but had a direct effect such that women reported lower entrepreneurial career intentions. The authors discuss practical implications and directions for future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Serotonergic Agonists Quipazine, CGS-12066A, and α-Methylserotonin Alter Motor Activity and Induce Hindlimb Stepping in the Intact and Spinal Rat Fetus.

The effects of serotonergic agonists were examined in intact and spinal fetuses, using an in vivo fetal rat preparation. On Gestational Day 20, fetuses were prepared with a midthoracic or sham spinal transection. Dose-response curves were obtained for quipazine (nonselective 5-hydroxytryptamine [5-HT] agonist; 1.0-10.0 mg/kg), CGS-12066A (5-HT1B agonist; 1.0-30.0 mg/kg), and α-methylserotonin (α-Me-5-HT; 5-HT? agonist; 0.2-15.0 mg/kg). During a 10-min test, each of the agonists (delivered via intraperitoneal injection) influenced fetal behavior: They increased the occurrence of head movements, mouthing, and hindlimb stepping. Quipazine and α-Me-5-HT also promoted hindlimb activity in spinal fetuses. Thus, stimulation of the fetal 5-HT system modulates motor activity at multiple levels of the developing central nervous system. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Physical, Mental, and Social Catastrophic Cognitions as Prognostic Factors in Cognitive-Behavioral and Pharmacological Treatments for Panic Disorder.

The authors explored the prognostic value of 3 different types of catastrophic cognitions in the treatment of panic disorder with and without mild-to-moderate agoraphobia using a sample of 143 participants who received either cognitive-behavioral therapy (CBT) or imipramine in a randomized controlled trial. Stronger fears of social catastrophes both prior to and following treatment with CBT or imipramine were associated with a poorer outcome. In contrast, cognitions involving physical or mental catastrophes were unrelated to outcome, regardless of whether these thoughts were measured prior to or following treatment. These findings are consistent with the notion that although the intensity of physical catastrophe cognitions may best discriminate between panic disorder and other anxiety disorders, it is the intensity of social catastrophe cognitions that is most closely tied to success in treating this disorder. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Learning from examples with unspecified attribute values

A challenging problem within machine learning is how to make good inferences from data sets in which pieces of information are missing. While it is valuable to have algorithms that perform well for specific domains, to gain a fundamental understanding of the problem, one needs a “theory” about how to learn with incomplete data. The important contribution of such a theory is not so much the specific algorithmic results, but rather that it provides good ways of thinking about the problem formally. In this paper we introduce the unspecified attribute value (UAV) learning model as a first step towards a theoretical framework for studying the problem of learning from incomplete data in the exact learning framework.In the UAV learning model, an example x is classified positive (resp., negative) if all possible assignments for the unspecified attributes result in a positive (resp., negative) classification. Otherwise the classification given to x is “?” (for unknown). Given an example x in which some attributes are unspecified, the oracle UAV-MQ responds with the classification of x. Given a hypothesis h, the oracle UAV-EQ returns an example x (that could have unspecified attributes) for which h(x) is incorrect.We show that any class of functions learnable in Angluin’s exact model using the MQ and EQ oracles is also learnable in the UAV model using the MQ and UAV-EQ oracles as long as the counterexamples provided by the UAV-EQ oracle have a logarithmic number of unspecified attributes. We also show that any class learnable in the exact model using the MQ and EQ oracles is also learnable in the UAV model using the UAV-MQ and UAV-EQ oracles as well as an oracle to evaluate a given boolean formula on an example with unspecified attributes. (For some hypothesis classes such as decision trees and unate formulas the evaluation can be done in polynomial time without an oracle.) We also study the learnability of a universal class of decision trees under the UAV model and of DNF formulas under a representation-dependent variation of the UAV model.     

Specifying and enforcing application-level Web security policies

Application-level Web security refers to vulnerabilities inherent in the code of a Web-application itself (irrespective of the technologies in which it is implemented or the security of the Web-server/back-end database on which it is built). In the last few months, application-level vulnerabilities have been exploited with serious consequences: Hackers have tricked e-commerce sites into shipping goods for no charge, usernames and passwords have been harvested, and confidential information (such as addresses and credit-card numbers) has been leaked. We investigate new tools and techniques which address the problem of application-level Web security. We 1) describe a scalable structuring mechanism facilitating the abstraction of security policies from large Web-applications developed in heterogeneous multiplatform environments; 2) present a set of tools which assist programmers in developing secure applications which are resilient to a wide range of common attacks; and 3) report results and experience arising from our implementation of these techniques.     

Automatic Design of Application Specific Instruction Set Extensions Through Dataflow Graph Exploration

General-purpose processors are often incapable of achieving the challenging cost, performance, and power demands of high-performance applications. To meet these demands, most systems employ a number of hardware accelerators to off-load the computationally demanding portions of the application. As an alternative to this strategy, we examine customizing the computation capabilities of a processor for a particular application. The processor is extended with hardware in the form of a set of custom function units and instruction set extensions. To effectively identify opportunities for creating custom hardware, a dataflow graph design space exploration engine heuristically identifies candidate computation subgraphs without artificially constraining their size or shape. The engine combines estimates of performance gain, cost, and inherent limitations of the processor to grow candidate graphs in profitable directions while pruning unprofitable paths. This paper describes the dataflow graph exploration engine and evaluates its effectiveness across a set of embedded applications.     

Quantum Aspects of Resolving Discrete Charges in ‘Atomistic’ Device Simulations

In ‘atomistic’ device simulation the resolving of discrete charges onto a fine grained simulation mesh can lead to problems. The sharply resolved coulomb potential can cause simulation artefacts to appear in classical simulation environments using Boltzmann or Fermi-Dirac statistics. Various methods have been proposed in an effort to reduce or eliminate artefacts such as the trapping of mobile carriers in sharply resolved Coulomb wells, however they have met with limited success. In this paper we show an alternative approach for handling discrete charges in drift diffusion ‘atomistic’ simulations by properly introducing the related quantum mechanical effects using the Density Gradient formalism. This produces the desired effect of eliminating the trapping of mobile charge in heavily doped regions of the device.     

An evaluation of the development of IT in a college of nursing and midwifery

This paper presents the findings of a case study of the development of IT in a College of Nursing and Midwifery. Data were collected by means of postal questionnaires, interviews, focus groups, direct observations and documentary evidence. The findings suggest that the majority of staff used some form of IT on a regular basis, but only 50% of students used college-based IT facilities. The main use of IT was for administrative and productive purposes and there was little evidence of IT in teaching/learning methods. Training offered to staff was poorly attended and training offered to students did not meet what they perceived as their needs. Although most staff used IT there was still evidence of resistance and stress related to IT use. The use of IT in the study college was driven by tactical considerations rather than as a part of an explicit strategy.     

Coping with Uncertainty in Map Learning

In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refer to such a representation as a map, and the process of constructing a map from a set of measurements as map learning. In this paper, we develop a framework for describing map-learning problems in which the measurements taken by the robot are subject to known errors. We investigate approaches to learning maps under such conditions based on Valiant's probably approximately correct learning model. We focus on the problem of coping with accumulated error in combining local measurements to make global inferences. In one approach, the effects of accumulated error are eliminated by the use of local sensing methods that never mislead but occasionally fail to produce an answer. In another approach, the effects of accumulated error are reduced to acceptable levels by repeated exploration of the area to be learned. We also suggest some insights into why certain existing techniques for map learning perform as well as they do. The learning problems explored in this paper are quite different from most of the classification and boolean-function learning problems appearing in the literature. The methods described, while specific to map learning, suggest directions to take in tackling other learning problems.