Education reform: Challenges for psychology and psychologists.

National, state, and local education reform agendas have been on a fast track for more than a decade. Although some psychologists have been active in promoting psychology and psychological services in education reform initiatives, knowledge about their efforts is fragmented. In order to educate psychologists about education reform, this article highlights the impetus for the recent education reform movement, briefly reviews the latest waves of education reform, and discusses the development of the National Education Goals. Then, an argument is made for the integration of education, health, and social reform initiatives; state-level education reform principles and initiatives are highlighted, illustrated by the systemic education reform mandate in Kentucky. Next, progress toward the development and implementation of an APA-sponsored response to the National Education Goals is discussed. Finally, objectives and roles for psychologists in advancing education reform are presented, and guidelines for the education and training of future psychologists are introduced. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Threshold voltage models of short,narrow and small geometry MOSFET's: A review

As MOS devices are shrunk to near and submicrometer dimensions, short channel, narrow width and small geometry effects cause variations in the threshold voltage. It is critical for circuit and device designers to be able to predict these variations. This paper reviews and compares the various modeling techniques developed to determine the threshold voltage as a function of device geometry. It is hoped this review will provide insights for the development of new models for today's small devices.     

Electrodeposited amorphous Co-P alloys with variable magnetic anisotropy

A method is indicated for obtaining electrodeposited amorphous Co-P alloys with differing anisotropies, employing an alternating current density of very low frequency. The method allows samples of a high degree of permeability to be obtained with the result that, in the case of a current density at 5 Hz with amplitude between +0.6 and -0.2 A/cm², the magnetic properties are similar to those found by Dietz and coworkers, in samples deposited on rotating cylindrical substrates. In the case of samples without perpendicular anisotropy, there can be observed a distribution of the magnetization in the plane, with 180° domains.     

Ascertaining the importance of neurons to develop better brain-machine interfaces

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.     

Integration of knowledge-based metabolic predictions with liquid chromatography data-dependent tandem mass spectrometry for drug metabolism studies: application to studies on the biotransformation of indinavir

Despite recent advances in the application of data-dependent liquid chromatography/tandem mass spectrometry (LC/MS/MS) to the identification of drug metabolites in complex biological matrixes, a prior knowledge of the likely routes of biotransformation of the therapeutic agent of interest greatly facilitates the detection and structural characterization of its metabolites. Thus, prediction of the [M + H]+ m/z values of expected metabolites allows for the construction of user-defined MS(n) protocols that frequently reveal the presence of minor drug metabolites, even in the presence of a vast excess of coeluting endogenous constituents. However, this approach suffers from inherent user bias, as a result of which additional "survey scans" (e.g., precursor ion and constant neutral loss scans) are required to ensure detection of as many drug-related components in the sample as possible. In the present study, a novel approach to this problem has been evaluated, in which knowledge-based predictions of metabolic pathways are first derived from a commercial database, the output from which is used to formulate a list-dependent LC/MS(n) data acquisition protocol. Using indinavir as a model drug, a substructure similarity search on the MDL metabolism database with a similarity index of 60% yielded 188 "hits", pointing to the possible operation of two hydrolytic, two N-dealkylation, three N-glucuronidation, one N-methylation, and several aromatic and aliphatic oxidation pathways. Integration of this information with data-dependent LC/MS(n) analysis using an ion trap mass spectrometer led to the identification of 18 metabolites of indinavir following incubation of the drug with human hepatic postmitochondrial preparations. This result was accomplished with only a single LC/MS(n) run, representing significant savings in instrument use and operator time, and afforded an accurate view of the complex in vitro metabolic profile of this drug.     

Low bias mean estimator for symmetric and asymmetric unimodal distributions

An adaptive trimmed mean estimator for symmetric and asymmetric distributions has been developed. The simulation sample size as small as n = 10, 25 and 50 are chosen, where the influence of the outliers increases in small sample data. The comparison of the proposed estimator and the Princeton Robust Study are made. The results show that the proposed mean estimator has lower standard deviation for symmetric distributions and has a better location estimation for asymmetric distributions.     

Planar photonic crystal structure with inherently single-mode waveguides

A planar photonic crystal that allows inherently gap-guided single-mode waveguides is proposed and discussed. This novel structure consists of a two-dimensional lattice of silicon rods embedded on a thin silica slab sandwiched between two silica claddings whose refractive indices are slightly lower than the index of the silica core. The physical parameters of the structure, i.e., rod radius and core thickness, are optimized to maximize the bandgap width for odd modes. Lossless guided modes inside the bandgap and below the claddings' light cone are obtained by reducing the radius of a row of rods. The waveguide bandwidth can be increased by inserting a thin silicon dielectric waveguide instead of the row of rods. The proposed approach may overcome many of the common drawbacks in conventional holes-on-dielectric planar photonic crystal waveguides.     

Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks

This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.