Why is science difficult to learn? Things are seldom what they seem

Abstract The difficulties of learning science are related to the nature of science itself and to the methods by which science is customarily taught without regard to what is known about children's learning. An information processing model is proposed to guide thinking and research in this area.     

Effect of thermal treatment on ZnO substrate for epitaxial growth

ZnO is a highly efficient photon emitter, and has optical and piezoelectric properties that are attractive for a variety of applications in sensors and potentially optoelectronic devices such as emitters. Due to its identical stacking order and close lattice match to GaN, it is also being developed as a substrate material for GaN epitaxy. However, the surface finish of the ZnO is such that much of the damage induced by sawing and follow up mechanical polishing remains. We developed a thermal treatment method to eliminate surface damage on the 0 face of ZnO (0 0 0 1) to prepare it for epitaxial growth. Atomic force microscopy images of ZnO (0 0 0 1) annealed at 1050 °C for 3 h etc. show that residual scratches from mechanical polishing are removed and atomically flat, terrace-like surfaces are attained. In addition, low-temperature photoluminescence and high-resolution X-ray diffraction measurements have been employed to investigate the effect of annealing on ZnO substrates.     

Photovoltaic-integrated building facades

Photovoltaic (PV) cells, when integrated within a building facade, offer the possibility of generating electric power and heat for local use or export. This paper reports on a project to investigate the practical operational efficiencies that might be delivered from such facades. The results from laboratory experiments and computer simulations are presented: the former were used to develop an empirical relationship between cell temperature and power output; the latter were undertaken to assess operational efficiencies under a range of climate conditions representative of the UK.     

Single-channel EEG measurement of engagement in virtual rehabilitation: a validation study

Virtual Reality - Stroke rehabilitation suffers from low levels of patient engagement, impeding recovery. Virtual rehabilitation (VR) approaches can improve patient outcomes; however, there is...     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Telehealth Brain Injury Training for Rural Behavioral Health Generalists: Supporting and Enhancing Rural Service Delivery Networks.

Rural psychologists are frontline primary behavioral health care providers for nearly 60 million Americans, but they face limited access to peer consultation and continuing education. This article describes a program that matched 70 rehabilitation inpatients who had new brain injury with rural clinicians from patients' home communities. Neuropsychologists provided one-on-one training for clinicians through telehealth video teleconferencing. Clinicians showed gains in brain injury knowledge, and clients rated trained providers higher than untrained providers. Families seeking brain injury services can connect with these trained rural providers through a Web site, which receives more than 800 hits per month. Telehealth offers potential for rural clinicians to receive support, reduce professional isolation, gain working knowledge of specialty conditions, and deliver high-quality services for their rural clients. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reduction of defects and improvement in the performance of blue phosphor for thick‐dielectric electroluminescent displays using Color‐by‐Blue

Abstract— Defect‐free large‐area inorganic thick‐dielectric EL (TDEL) displays using Color by Blue (CBB) technology have been successfully developed. We have achieved the world's highest blue‐phosphor luminance of 900 cd/m² for a single‐pixel device by using CBB and by optimizing the e‐beam gun configuration and the flow rate of H₂S in the vacuum chamber. By analyzing the defects on panels with triple‐pattern phosphors and CBB panels, we also found that the number of defects on CBB panels can be drastically reduced compared with those on triple‐pattern panels. The defect‐free 17‐in. VGA CBB panels show better characteristics, a high peak luminance of 600 cd/m² and a high contrast ratio of 1000:1, compared with those of triple‐pattern panels.     

Characterization of HgCdTe Diodes on Si Substrates Using Temperature-Dependent Current-Voltage Measurements and Deep Level Transient Spectroscopy

Reverse current in diodes can be dominated by generation processes, depending exponentially on temperature according to the rate-limiting step in the generation process. In this report, the current-voltage-temperature (IVT) relationship is analyzed for several midwave infrared and long-wave infrared (MWIR x = 0.295, LWIR x = 0.233) Hg_1−x Cd _x Te (MCT) diodes. The energy varied from diode to diode. At high reverse biases, the energy tends toward the band gap energy. Close to zero bias, the energy ranged from 0.06 to 0.1 eV. Deep level transient spectroscopy (DLTS) showed a broad peak centered at 55–80 K for the MWIR MCT. Comparison of the DLTS spectrum to a simulation based on the energy and capture cross section from a rate window analysis shows that the peak is a band of traps. The capacitance transient amplitude increased as the filling pulse increased from 1 μs to 0.1 s, consistent with capture at a dislocation. A shift to lower temperatures for the peak was also observed when the diodes are cooled under forward bias. The shift is reversible, indicating that the traps consist at least partially of a bistable defect.