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

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring a large integration step to impute over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multitype branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for hematopoiesis and transposable element evolution.Comment: 18 pages, 4 figures, 2 table

Towards Automatic Learning of Procedures from Web Instructional Videos

The potential for agents, whether embodied or software, to learn by observing other agents performing procedures involving objects and actions is rich. Current research on automatic procedure learning heavily relies on action labels or video subtitles, even during the evaluation phase, which makes them infeasible in real-world scenarios. This leads to our question: can the human-consensus structure of a procedure be learned from a large set of long, unconstrained videos (e.g., instructional videos from YouTube) with only visual evidence? To answer this question, we introduce the problem of procedure segmentation--to segment a video procedure into category-independent procedure segments. Given that no large-scale dataset is available for this problem, we collect a large-scale procedure segmentation dataset with procedure segments temporally localized and described; we use cooking videos and name the dataset YouCook2. We propose a segment-level recurrent network for generating procedure segments by modeling the dependencies across segments. The generated segments can be used as pre-processing for other tasks, such as dense video captioning and event parsing. We show in our experiments that the proposed model outperforms competitive baselines in procedure segmentation.Comment: AAAI 2018 Camera-ready version. See http://youcook2.eecs.umich.edu for YouCook2 datase

Mechanisms of neuroprotection against ischemic insult by stress-inducible phosphoprotein-1

Stress-inducible phosphoprotein-1 (STI1) levels are increased in the brain following ischemia. STI1 is a co-chaperone for Hsp70/Hsp90 modulating protein folding. STI1 can also be secreted by a number of cells and function to activate extracellular signalling by the prion protein (PrPC) and type-I bone morphogenetic protein receptor ALK2. However, the mechanisms by which STI1 can protect neurons against ischemia are currently unknown. A caspase-3 reporter mouse line was used to evaluate the consequences of increased extracellular STI1 levels. Neurons were treated with recombinant STI1 and specific agonists/antagonists for PrPC, α7nAChR, and ALK2 prior to oxygen-glucose deprivation (OGD). STI1 treatment significantly decreased apoptosis and cell death in neurons submitted to OGD in a manner dependent on PrPC, α7nAChR, and ALK2. Activation of both the α7nAChR and ALK2 receptor were effective at decreasing neuronal death in response to ischemia, suggesting that α7nAChR and ALK2 receptor may be novel targets for preventing death of neurons after ischemia