Classes of Split‐plot Response Surface Designs for Equivalent Estimation

When planning an experimental investigation, we are frequently faced with factors that are difficult or time consuming to manipulate, thereby making complete randomization impractical. A split‐plot structure differentiates between the experimental units associated with these hard‐to‐change factors and those that are relatively easy‐to‐change. Furthermore, it provides an efficient strategy that integrates the restrictions imposed by the experimental apparatus into the design structure. In this paper, several industrial and scientific examples are presented to highlight design considerations when a restriction on randomization is encountered. We propose classes of split‐plot response designs that provide an intuitive and natural extension from the completely randomized context. For these designs, the ordinary least‐squares estimates of the model are equivalent to the generalized least‐squares estimates. This property provides best linear unbiased estimators and simplifies model estimation. The design conditions that provide equivalent estimation are presented and lead to design construction strategies to transform completely randomized Box–Behnken, equiradial and small composite designs into a split‐plot structure. Published in 2006 by John Wiley & Sons, Ltd.     

Results demonstrating techniques for enhancing electrochemical reactions involving iron oxide in slags and C in liquid iron

Two techniques are described for the enhancement of the kinetics of reduction of iron oxide from slags by carbon in molten iron. Laboratory experiments have shown that the rate of iron oxide reduction by carbon-saturated iron can be increased by 5 to 10 times when the reaction is carried out under a reduced-pressure atmosphere. This effect is thought to be the result of the increased volumetric gas evolution through the slag layer and the associated increase in slag stirring. A model is presented, which relates the mass-transfer coefficient for ferrous ions in the slag to its stirring that is controlled by varying the ambient pressure. Additional laboratory experiments examined the electrochemical nature of iron oxide reduction from slag by carbon in liquid iron. Results indicate that the reduction of iron oxide from slag is increased in the presence of an applied electric field. The external circuit allows for the separation of the half-cell reactions associated with iron oxide reduction and decarburization and increases the reaction area available for the individual reactions. These results have significant implications for several important slag metal reactions, which occur during ironmaking and steelmaking operations.     

The Problem of Measurement Model Misspecification in Behavioral and Organizational Research and Some Recommended Solutions.

The purpose of this study was to review the distinction between formative- and reflective-indicator measurement models, articulate a set of criteria for deciding whether measures are formative or reflective, illustrate some commonly researched constructs that have formative indicators, empirically test the effects of measurement model misspecification using a Monte Carlo simulation, and recommend new scale development procedures for latent constructs with formative indicators. Results of the Monte Carlo simulation indicated that measurement model misspecification can inflate unstandardized structural parameter estimates by as much as 400% or deflate them by as much as 80% and lead to Type I or Type II errors of inference, depending on whether the exogenous or the endogenous latent construct is misspecified. Implications of this research are discussed. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

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.