Construction and validation of the Disability Rights Attitude Scale: Assessing attitudes toward the Americans with Disabilities Act (ADA).

The authors constructed and validated an instrument that assesses attitudes toward the Americans With Disabilities Act (ADA), a law that protects the civil rights of individuals with disabilities. The Disability Rights Attitude Scale (DRAS) demonstrated acceptable reliability and validity with 2 samples of university students (N?=?421). Reliability analysis resulted in Cronbach's alphas of .91 (Sample 1) and .90 (Sample 2). Principal-components factor analysis indicated that the DRAS essentially consists of 1 factor that accounted for 27% (Sample 1) and 31% (Sample 2) of the total variance. Construct validity analysis resulted in predicted, significant positive correlations with other relevant measures. A stepwise multiple regression analysis revealed that sex, ethnicity, and prior contact with people with disabilities were significant predictors of attitudes toward the law. The DRAS provides a psychometrically sound means of assessing attitudes toward disability rights that may encourage or impede implementation of the ADA. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Second-order sliding mode observer for multiple kinetic rates estimation in bioprocesses

Specific kinetic rates are key variables regarding metabolic activity in bioprocesses. They are non-linear functions of concentrations and operating conditions and therefore of difficult access for process control. In this paper, a multiple kinetic rates observer based on second-order sliding mode ideas is proposed. The main difference with other proposals is that smooth estimates are achieved in finite-time without adding additional dynamics. The resulting estimator is robust against uncertainty in the model of the estimated variables. Experimental results from continuous fermentation of S. cerevisiae are presented, where microbial specific growth rate and net ethanol production rate are estimated.     

Robot coordination using task-priority and sliding-mode techniques

In this work, an approach based on task-priority redundancy resolution and sliding mode ideas is proposed for robot coordination. In particular, equality and inequality constraints representing the coordination of the multi-robot system are considered as mandatory (for instance, rigid-body manipulation constraints to distance between the end-effectors of several robot arms, or other inequality constraints guaranteeing safe operation of a robotic swarm or confining the robot's workspace to avoid collision and joint limits). Besides the mandatory constraints, other constraints with lower priority are considered for the tracking of the workspace reference and to achieve secondary goals. Thus, lower-priority constraints are satisfied only in the null space of the higher-priority ones. The fulfillment of the constraints is achieved using geometric invariance and sliding mode control theory. The validity and effectiveness of the proposed approach are substantiated by 2D and 3D simulation results using two 3R planar robots and two 6R PUMA-762 robots, respectively.     

Robot measuring form errors

This work presents an automated system for the measurement of form errors of mechanical components using an industrial robot. A three-probe error separation technique was employed to allow decoupling between the measured form error and errors introduced by the robotic system. A mathematical model of the measuring system was developed to provide inspection results by means of the solution of a system of linear equations. A new self-calibration procedure, which employs redundant data from several runs, minimizes the influence of probes zero-adjustment on the final result. Experimental tests applied to the measurement of straightness errors of mechanical components were accomplished and demonstrated the effectiveness of the employed methodology.     

Aircraft interior failure pattern recognition utilizing text mining and neural networks

Being more competitive is routine in the aeronautical sector. Airline competitiveness is affected by such factors as time, price, reliability, availability, safety, technology, quality, and information management. To remain competitive, airlines must promptly identify and correct failures found in their fleet. This study aims at reducing the time spent on identifying and correcting such failures logged. Utilizing Text Mining techniques during the pre-processing phase, our study processes an extensive database of events from commercial regional jets. The result is a unique list of keywords that describes each reported failure. Later, an Artificial Neural Network (ANN) identifies and classifies failure patterns, yielding a respective disposition for a given failure pattern. Approximately five years of historical data was used to build and validate the present model. Results obtained were promising.     

Optimal reconfiguration of water networks based on properties

This paper presents a mathematical programming model for the reconfiguration of existing water networks based on the stream properties that impact the performance of the process units and the environment. To develop an improved configuration, the model simultaneously evaluates the repiping of the existing network through the placement/reassignment of the existing treatment units, and the addition of new treatment units while addressing environmental constraints. The model also accounts for the options of process modification and increased capacity of the plant. The objective function of the optimization model seeks to minimize the total annualized cost of the system which incorporates the capital investment associated with process retrofitting and the operating cost which includes the cost of fresh resources. The applicability of the proposed model is illustrated through several case studies.