A micro particle shadow velocimetry (μPSV) technique to measure flows in microchannels

A micro particle shadow velocimetry (μPSV) system based on back-lit illumination and forward scatter observation of light from non-fluorescent particles has been developed. Relatively high luminous efficiencies and particle image contrasts were achieved by using the condenser stage of a standard transmitted light microscope and a continuous incoherent collimated light emitting diode (LED). This paper includes a critical review of the operating principles, benefits and practical problems associated with the predominant epifluorescent micro particle image velocimetry (μPIV) technique, and the less common light scatteringμPIV methods of whichμPSV is a development. ThisμPSV system was then successfully used to measure axial velocity profiles in a 280-μm-diameter circular channel up to a Reynolds number of 50 which corresponds to peak velocities of around 0.4 m/s. These velocity profiles were then integrated to provide instantaneous flow rates on the order of 100μl/min to an accuracy of±5% relative to average flow rates determined using a digital balance. Due to the incoherent nature of the LED light source, the back-lit forward scatter observation mode and the applied refractive index matching system, the location of the test section walls and thus the local velocity fields were also accurately obtained. As a result of this,μPSV provides a low cost and safe way to investigate microfluidics, especially in lab-on-a-chip applications where the necessary optical access through transparent test sections is often availabl

SIGIR 2021 E-Commerce Workshop Data Challenge

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions for two open problems: a recommendation task (where a model is shown some events at the start of a session, and it is asked to predict future product interactions); an intent prediction task, where a model is shown a session containing an add-to-cart event, and it is asked to predict whether the item will be bought before the end of the session.Comment: SIGIR eCOM 2021 Data Challeng