Developing competency-based methods for near-real-time air combat problem solving assessment

Air combat scenarios present unique problems where the solution is not obvious. An advanced networked simulation environment can be used to train high-level cognitive air combat skills such as problem solving. A problem in the development of principled and construct oriented assessment is related to determining the level of specificity of the assessment. We present a detailed discussion of the definition of critical competencies associated with combat mission problem solving performance, and we describe our approach to develop and implement an embedded performance assessment system that maps outcomes to these competencies. Finally, we discuss the implications for our approach for comprehensive assessment and discuss our goals for an evaluation of the competency-based approach to assessment.     

GaAs/AlGaAs grating surface-emitting diode lasers on Si substrates

A monolithic grating surface-emitting, GaAs/AlGaAs, separate-confinement-heterostructure, single-quantum-well diode laser has been fabricated on a Si substrate using a single-step metalorganic chemical vapour deposition process. An output power of 30 mW has been obtained under pulsed operation with a peak emission wavelength of 885 nm.<>     

Cognitive shifting as a predictor of progress in social understanding in high-functioning adolescents with autism: a prospective study

The pyrolytic behavior of inulin, a (2-->1)-linked fructofuranan, is described. Parallel investigations of the pyrolysis of glucose and of fructose were conducted to supplement the inulin results and to aid comparison with previous results from glucans. Effects of neutral and basic additives are emphasized. As with glucans, the addition of such additives (especially basic) increases the yields of the one-, two-, and three-carbon products (as well as of hexosaccharinolactones), while generally decreasing the yields of anhydro sugar and furan derivatives. The former products include glycolaldehyde, acetol, dihydroxy-acetone, acetic acid, formic acid, and lactic acid. Mechanistic speculations are made regarding the origins of these compounds, as well as of furan derivatives and saccharinic acid lactones. Parallels with alkaline degradation are considered.     

"Adolescent psychopathology: I. Prevalence and incidence of depression and other DSM-III—R disorders in high school students": Correction.

Reports an error in the original article by P. M. Lewinsohn et al ( Journal of Abnormal Psychology, 1993[Feb], Vol 102[1], 133–244). On page 140, in the Total Incidence columns in Table 4, the data for the Attention Deficit row should be switched with that for the Conduct row. (The following abstract of this article originally appeared in record 1993-25780-001.) Data were collected on the point and lifetime prevalences, 1-yr incidence, and comorbidity of depression with other Diagnostic and Statistical Manual of Mental Disorders-III-Revised (DSM-III-R) disorders in a randomly selected sample (n?=?1,710) of high school students at point of entry and at 1-yr follow-up (n?=?1,508). The Schedule for Affective Disorders and Schizophrenia for School-Age Children was used to collect diagnostic information; 9.6% met criteria for a current disorder, more than 33% had experienced a disorder over their lifetimes, and 31.7% of the latter had experienced a 2nd disorder. High relapse rates were found for all disorders, especially for unipolar depression (18.4%) and substance use (15.0%). Female Ss had significantly higher rates at all age levels for unipolar depression, anxiety disorders, eating disorders, and adjustment disorders; male Ss had higher rates of disruptive behavior disorders. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Machine learning in control of functional electrical stimulationsystems for locomotion

Two machine learning techniques were evaluated for automatic design of a rule-based control of functional electrical stimulation (FES) for locomotion of spinal cord injured humans. The task was to learn the invariant characteristics of the relationship between sensory information and the FES-control signal by using off-line supervised training. Sensory signals were recorded using pressure sensors installed in the insoles of a subject's shoes and goniometers attached across the joints of the affected leg. The FES-control consisted of pulses corresponding to time intervals when the subject pressed on the manual push-button to deliver the stimulation during FES-assisted ambulation. The machine learning techniques used were the adaptive logic network (ALN) and the inductive learning algorithm (IL). Results to date suggest that, given the same training data, the IL learned faster than the ALN while both performed the test rapidly. The generalization was estimated by measuring the test errors and it was better with an ALN, especially if past points were used to reflect the time dimension. Both techniques were able to predict future stimulation events. An advantage of the ALN over the IL was that ALN's can be retrained with new data without losing previously collected knowledge. The advantages of the IL over the ALN were that the IL produces small, explicit, comprehensible trees and that the relative importance of each sensory contribution can be quantified