Deposition of particle pollution in turbulent forced-air cooling

Rotating fans are the prevalent forced cooling method for heat generating equipment and buildings. As the concentration of atmospheric pollutants has increased, the accumulation of microscale and nanoscale particles on surfaces due to advection-diffusion has led to adverse mechanical, chemical and electrical effects that increase cooling demands and reduce the reliability of electronic equipment. Here, we uncover the mechanisms leading to enhanced deposition of particle matter (PM$_{10}$ and PM$_{2.5}$) on surfaces due to turbulent axial fan flows operating at Reynolds numbers, $Re \sim 10^5$. Qualitative observations of long-term particle deposition from the field were combined with \textit{in situ} particle image velocimetry on a telecommunications base station, revealing the dominant role of impingement velocity and angle. Near-wall momentum transport for $10 < y^+ < 50$ were explored using a quadrant analysis to uncover the contributions of turbulent events that promote particle deposition through turbulent diffusion and eddy impaction. By decomposing these events, the local transport behaviour of fine particles from the bulk flow to the surface has been categorised. The transition from deposition to clean surfaces was accompanied by a decrease in shear velocity, turbulent stresses, and particle sweep motions with lower flux in the wall-normal direction. Finally, using these insights, selective filtering of coarse particles was found to promote the conditions that enhance the deposition of fine particle matter

SmartMirror: A Glance into the Future

In todays society, information is available to us at a glance through our phones, our laptops, our desktops, and more. But an extra level of interaction is required in order to access the information. As technology grows, technology should grow further and further away from the traditional style of interaction with devices. In the past, information was relayed through paper, then through computers, and in todays day and age, through our phones and multiple other mediums. Technology should become more integrated into our lives - more seamless and more invisible. We hope to push the envelope further, into the future. We propose a new simple way of connecting with your morning newspaper. We present our idea, the SmartMirror, information at a glance. Our system aims to deliver your information quickly and comfortably, with a new modern aesthetic. While modern appliances require input through modules such as keyboards or touch screen, we hope to follow a model that can function purely on voice and gesture. We seek to deliver your information during your morning routine and throughout the day, when taking out your phone is not always possible. This will cater to a larger audience base, as the average consumer nowadays hopes to accomplish tasks with minimal active interaction with their adopted technology. This idea has many future applications, such as integration with new virtual or augmented reality devices, or simplifying consumer personal media sources

Reading Wikipedia to Answer Open-Domain Questions

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA datasets indicate that (1) both modules are highly competitive with respect to existing counterparts and (2) multitask learning using distant supervision on their combination is an effective complete system on this challenging task.Comment: ACL2017, 10 page

Energetics of Protein-DNA Interactions

Protein-DNA interactions are vital for many processes in living cells, especially transcriptional regulation and DNA modification. To further our understanding of these important processes on the microscopic level, it is necessary that theoretical models describe the macromolecular interaction energetics accurately. While several methods have been proposed, there has not been a careful comparison of how well the different methods are able to predict biologically important quantities such as the correct DNA binding sequence, total binding free energy, and free energy changes caused by DNA mutation. In addition to carrying out the comparison, we present two important theoretical models developed initially in protein folding that have not yet been tried on protein-DNA interactions. In the process, we find that the results of these knowledge-based potentials show a strong dependence on the interaction distance and the derivation method. Finally, we present a knowledge-based potential that gives comparable or superior results to the best of the other methods, including the molecular mechanics force field AMBER99