Coaxial electrospray is an electrohydrodynamic process that produces multilayer microparticles and nanoparticles by introducing coaxial electrified jets. In comparison with other microencapsulation/nanoencapsulation processes, coaxial electrospray has several potential advantages such as high encapsulation efficiency, effective protection of bioactivity and uniform size distribution. However, process control in coaxial electrospray is challenged by the multiphysical nature of the process and the complex interplay of multiple design, process and material parameters. This paper reviews the previous works and the recent advances in design, modeling and control of a coaxial electrospray process. The review intends to provide general guidance for coaxial electrospray and stimulate further research and development interests in this promising microencapsulation/nanoencapsulation process.

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Abstract. We developed a novel dual-modal contrast agent for the structural and functional imaging of cancer. The contrast agent was fabricated by encapsulating indocyanine green ͑ICG͒ in poly͑lactic-co-glycolic acid͒ ͑PLGA͒ microbubbles using a modified doubleemulsion method. More stabilized absorption and fluorescence emission characteristics were observed for aqueous and plasma suspensions of ICG-encapsulated microbubbles. The technical feasibility of concurrent structural and functional imaging was demonstrated through a series of benchtop tests in which the aqueous suspension of ICG-encapsulated microbubbles was injected into a transparent tube embedded in an Intralipid phantom at different flow rates and concentrations. Concurrent fluorescence imaging and B-mode ultrasound imaging successfully captured the changes of microbubble flow rate and concentration with high linearity and accuracy. One potential application of the proposed ICG-encapsulated PLGA microbubbles is for the identification and characterization of peritumoral neovasculature for enhanced coregistration between tumor structural and functional boundaries in ultrasound-guided nearinfrared diffuse optical tomography. © 2009 Society of Photo-Optical Instrumentation Engineers.

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SummaryWith the popularity of Internet of Things (IoT), Point‐of‐Interest (POI) recommendation has become an important application for location‐based services (LBS). Meanwhile, there is an increasing requirement from IoT devices on the privacy of user sensitive data via wireless communications. In order to provide preferable POI recommendations while protecting user privacy of data communication in a distributed collaborative environment, this paper proposes a federated learning (FL) approach of geographical POI recommendation. The POI recommendation is formulated by an optimization problem of matrix factorization, and singular value decomposition (SVD) technique is applied for matrix decomposition. After proving the nonconvex property of the optimization problem, we further introduce stochastic gradient descent (SGD) into SVD and design an FL framework for solving the POI recommendation problem in a parallel manner. In our FL scheme, only calculated gradient information is uploaded from users to the FL server while all the users manage their rating and geographic preference data on their own devices for privacy protection during communications. Finally, real‐world dataset from large‐scale LBS enterprise is adopted for conducting extensive experiments, whose experimental results validate the efficacy of our approach.