1,430 research outputs found
The Dynamic Effects of Subconscious Goal Pursuit on Resource Allocation, Task Performance, and Goal Abandonment
We test two potential boundary conditions for the effects of subconscious goals—the nature of the goal that is activated (achievement vs. underachievement) and conscious goal striving. Subconscious achievement goals increase the amount of time devoted to skill acquisition, and this increase in resource allocation leads to higher performance when conscious goals are neutral. However, specific conscious goals undermine the performance benefits of subconscious achievement goals. Subconscious underachievement goals cause individuals to abandon goal pursuit and this effect is mediated by task performance. Difficult conscious goals neutralize the detrimental effects of subconscious underachievement goals but only if implemented before performance is undermined. Overall, these results suggest that subconscious achievement goals facilitate task performance, subconscious underachievement goals trigger goal abandonment, and difficult conscious goals moderate these effects depending on the level of resource allocation and timing of goal implementation
A Multilevel Analysis of the Effect of Prompting Self-Regulation in Technology-Delivered Instruction
We used a within-subjects design and multilevel modeling in two studies to examine the effect of prompting self-regulation, an intervention designed to improve learning from technology-delivered instruction. The results of two studies indicate trainees who were prompted to self-regulate gradually improved their knowledge and performance over time, relative to the control condition. In addition, Study 2 demonstrated that trainees’ cognitive ability and self-efficacy moderated the effect of the prompts. Prompting self-regulation resulted in stronger learning gains over time for trainees with higher ability or higher self-efficacy. Overall, the two studies demonstrate that prompting self-regulation had a gradual, positive effect on learning, and the strength of the effect increased as trainees progressed through training. The results are consistent with theory suggesting self-regulation is a cyclical process that has a gradual effect on learning and highlight the importance of using a within-subjects design in self-regulation. research
Upgrading of fast pyrolysis oils by hot filtration
A hot filtration unit downstream of a 1kg/h fluidised bed fast pyrolysis reactor was designed and built. The filter unit operates at 450oC and consists of 1 exchangeable filter candle with reverse pulse cleaning system. Hot filtration experiments up to 7 hours were performed with beech wood as feedstock. It was possible to produce fast pyrolysis oils with a solid content below 0.01 wt%. The additional residence time of the pyrolysis vapours and secondary vapour cracking on the filter cake caused an increase of non-condensable gases at the expense of organic liquid yield. The oils produced with hot filtration showed superior quality properties regarding viscosity than standard pyrolysis oils. The oils were analysed by rotational viscosimetry and gel permeation chromatography before and after accelerated aging. During filtration the separated particulates accumulate on the candle surface and build up the filter cake. The filter cake leads to an increase in pressure drop between the raw gas and the clean gas side of the filter candle. At a certain pressure drop the filter cake has to be removed by reverse pulse cleaning to regenerate the pressure drop. The experiments showed that successful pressure drop recovery was possible during the initial filtration cycles, thereafter further cycles showed minor pressure drop recovery and therefore a steady increase in differential pressure. Filtration with pre-coating the candle to form an additional layer between the filter candle and cake resulted in total removal of the dust cake
DeepVoxels: Learning Persistent 3D Feature Embeddings
In this work, we address the lack of 3D understanding of generative neural
networks by introducing a persistent 3D feature embedding for view synthesis.
To this end, we propose DeepVoxels, a learned representation that encodes the
view-dependent appearance of a 3D scene without having to explicitly model its
geometry. At its core, our approach is based on a Cartesian 3D grid of
persistent embedded features that learn to make use of the underlying 3D scene
structure. Our approach combines insights from 3D geometric computer vision
with recent advances in learning image-to-image mappings based on adversarial
loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction
of the scene, using a 2D re-rendering loss and enforces perspective and
multi-view geometry in a principled manner. We apply our persistent 3D scene
representation to the problem of novel view synthesis demonstrating
high-quality results for a variety of challenging scenes.Comment: Video: https://www.youtube.com/watch?v=HM_WsZhoGXw Supplemental
material:
https://drive.google.com/file/d/1BnZRyNcVUty6-LxAstN83H79ktUq8Cjp/view?usp=sharing
Code: https://github.com/vsitzmann/deepvoxels Project page:
https://vsitzmann.github.io/deepvoxels
Personalización del aprendizaje de los alumnos de la educación media mediante el uso de las tecnologÃas de la información y de la comunicación (TIC)
Trabajo fin de máster en TecnologÃas de Información y Comunicación en Educación y Formació
Wind Phenomena
The natural phenomenon that this project deals with is wind. Wind is an invisible occurrence, so we decided to use sound to record the wind
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