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.     

Breakfasts high in monoglyceride or triglyceride: no differential effect on appetite or energy intake

OBJECTIVE: To test whether isoenergetic isoenergetically-dense doses of dietary 1-monoglyceride or triglyceride differentially influence appetite and meal-to-meal energy intake in man. DESIGN: Six men and six women were each studied twice in a 2 d protocol. On day 1 (maintenance day) they were fed a medium fat (MF) maintenance diet (MF: 40% fat, 47% carbohydrate and 13% protein by energy) calculated at 1.6 x resting metabolic rate (RMR). On day 2 (manipulation day) at 08.30 h subjects consumed a high-fat breakfast designed to contain 80-85% of RMR, composition 10% protein, 56% fat and 34% carbohydrate by energy, with 65% of energy for fat derived as either 1-monoglyceride or triglyceride. Food and energy intake were monitored at lunch (given at 12.30pm) and throughout the remainder of the day. During this time subjects had ad libitum access to isoenergetic, isoenergetically dense MF (40:47:13) foods (550kJ/100g), until 22.30pm). Subjective hunger and satiety were tracked hourly, during waking hours. RESULTS: There was no significant effect of fat type on food or energy intake at lunch or during the ad libitum period. There was no diet effect on subjective hunger (F(1,10)0.00; P= 0.975) in the inter-meal periods of morning or afternoon, nor during the whole day. Subjects found both diets to be similarly pleasant (F(1,61)0.84;P= 0.364). Men and women responded similarly, except that men ate more on all occasions than women. CONCLUSIONS: This study suggests that when a large dose of l-monoglyceride or triglyceride is incorporated into a breakfast meal, it behaves in a manner that is very similar to triglyceride in terms of the effects on hunger, appetite or feeding behaviour.     

Note on the movement of insecticide droplets through and out of a crop canopy after emission below canopy level

Coarse aerosol sprays with number median diameters in the range 30–40 μm were emitted at 0.25 m above the ground within the canopy of a cotton crop planted at 0.93 m row separation and standing 1.0 m tall. Movement of spray was monitored by vertical stepped paper tower samplers with narrowest section of width 0.13 cm, giving collection by inertial impaction down to 10 μm in a 1 m/sec wind. Horizontal paper targets were used to sample droplets depositing by sedimentation. Droplet number counts indicated that under conditions of moderate instability and associated convective turbulence, droplets were drawn rapidly out of the crop to a height of 2 m (the upper level sampled). The peak concentration of the spray cloud was centred at a height of between 1.0 and 2.0 m beyond 1.0 m downwind of the emission point in the first test, using droplet spectrum with volume median diameter of 41 μm, and beyond 2.5 m downwind of the emission point in the second test with spectrum of 57 μm volume median diameter. Inertial impaction at the 0.25 and 0.5 m levels was slight, except on the samplers within 0.5 m of the sprayer, and sedimentation was only significant at and below 0.5 m in the first two rows sampled. The moderate wind speed of 2.5 m/sec at canopy level produced marked inertial impaction over the first 4.5 m downwind of the sprayer at the 1.0 m level.     

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.

     

Factors influencing the decision to crowdsource: A systematic literature review

Crowdsourcing is currently attracting much attention from organisations for its competitive advantages over traditional work structures regarding how to utilise skills and labour and especially to harvest expertise and innovation. Prior research suggests that the decision to crowdsource cannot simply be based on perceived advantages; rather multiple factors should be considered. However, a structured account and integration of the most important decision factors is still lacking. This research fills the gap by providing a systematic literature review of the decision to crowdsource. Our results identify nine factors and sixteen sub-factors influencing this decision. These factors are structured into a decision framework concerning task, people, management, and environmental factors. Based on this framework, we give several recommendations for managers making the crowdsourcing decision.