Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

A technique is presented for the direct measurement of octanol-water partition coefficients by HPLC. The method involves running solutes in octanol-saturated water as the mobile phase against water-saturated octanol entrained on an inert support. Log P correlates linearly with log tc for a number of standards. The measurable range in log P (so far) is -0.3 to +3.7. A critical review of chromatographic methods in Hansch analysis is given.

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