SCHEMORA combines LLM-based metadata enrichment with hybrid vector and lexical retrieval to achieve new state-of-the-art schema matching accuracy on MIMIC-OMOP, improving HitRate@5 by 7.49 percentage points over prior best.
Time-Transient Wireless RF Sensor with Differentiative Detecting Capability for Target Ionic Solution of Water and Dielectric Objects Introduced into Water
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
A novel wireless microstrip-based RF sensor designed for detecting changes in ionic content of water and the addition of solid contaminant objects is proposed and demonstrated. The sensor can be installed on the exterior wall of dielectric containers and customized according to the material of the container to enable wireless sensing. Its operation within the lower microwave frequency range (670-730 MHz) serves to minimize signal attenuation in water and streamlines circuitry design. The most significant feature of this sensor is its unique design, rendering it impervious to its surrounding environment. This not only shields it from environmental noise but also maximizes its sensitivity by efficiently utilizing incoming power for sensing purposes. The sensor exhibits remarkable sensitivity, capable of detecting solute concentrations as low as 0.003125 M in water. It can also detect the insertion of foreign solid objects into the container from the exterior wirelessly and distinguish them from liquids being added. As a proof-of-concept demonstration, the sensor in this study was optimized for a porcelain wall of 10-12 mm thickness. The sensor's small size and the materials used for its fabrication make it adaptable to a wide range of applications in industries such as food, pharmaceuticals, and bathroom fixtures. The aforementioned properties position the sensor as an ideal choice for various smart bathroom applications, where accurate and reliable water use monitoring is essential for efficient water conservation.
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Schemora: schema matching via multi-stage recommendation and metadata enrichment using off-the-shelf llms
SCHEMORA combines LLM-based metadata enrichment with hybrid vector and lexical retrieval to achieve new state-of-the-art schema matching accuracy on MIMIC-OMOP, improving HitRate@5 by 7.49 percentage points over prior best.