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Droplet microfluidics marks a significant advancement from the traditional continuous flow systems by utilizing two non-mixing liquid phases to generate droplets for further applications. This innovative technology encases samples within droplets, creating isolated compartments that enhance analytical sensitivity. It achieves this enhancement by concentrating substances of low abundance and accelerating the attainment of reaction equilibrium and detection thresholds. Notably, the technology allows for the adjustment of droplet size to approximate that of cells, marrying the benefits of minimal reagent use with the efficiency of high-throughput microfluidic experimentation. Beyond controlling droplet flow in microscopic channels, it consistently produces droplets of uniform size, ensuring reproducibility. Such precision finds widespread use across the food, cosmetics, and pharmaceutical sectors, laying the groundwork for novel biological and medical research avenues. This review presents an in-depth examination of recent advancements in droplet microfluidics technology. It specifically addresses the strategic methodologies for droplet analysis, offering a comprehensive summary of prevalent techniques for droplet signal amplification, alongside both optical and non-optical detection methods. Furthermore, it critically evaluates the existing limitations within droplet microfluidics technology and anticipates prospective directions for its future evolution.

This paper proposes a distributed model predictive control (DMPC) for a class of discrete-time stochastic multi-agent systems subject to partially coupled temporal logic tasks. For each agent, the given tasks are formulated as local and coupled probabilistic signal temporal logic (PrSTL) constraints in DMPC, and the control objective is to satisfy the PrSTL constraints against stochastic uncertainties and the coupled spatio-temporal relationship between agents. Considering that control under STL is historically dependent, a shrinking horizon DMPC framework is adopted and a probabilistic-tightening method is proposed to transform the complex form of PrSTL into deterministic constraints. Then, combining with the asynchronous update strategy, the satisfaction verification of coupled PrSTL tasks is achieved. Since large uncertainties may result in optimization infeasibility and affect the completion of the temporal logic tasks, a distributed PrSTL task softening method is further proposed, which can guarantee the softened tasks converge to the original ones and reduce the conservatism of the controller design. The recursive feasibility of the proposed PrSTL-DMPC strategy is proved and the efficiency of the algorithm is demonstrated by simulations. Note to Practitioners—With the increasing application of autonomous systems, more complex temporal logic tasks are imposed on system behaviors. This paper focuses on the distributed control problem for stochastic multi-agent systems under temporal logic tasks formulated as PrSTL specifications. A asynchronous DMPC controller is presented to improve task satisfaction against uncertainties and coupled spatial-temporal relationships between agents. The control performance including the recursive feasibility and soundness is formally guaranteed. For applications to autonomous systems, the practitioners can employ PrSTL for specifying spatial and temporal requirements on agents and employ the proposed PrSTL-DMPC algorithm for the task implementation.

A comprehensive architecture for an intelligent diabetes monitoring system, covering various components and their technologies is presented. Core aspects are systematically outlined, including advancements in glucose sensors using biofluidics and a comparison of communication standards. Bluetooth Low Energy (BLE) and narrowband Internet of Things (IoT) are chosen for short-range and remote communication. We explore cloud computing’s efficiency in data processing, noting drawbacks like high energy use and latency. Edge computing is considered for these challenges, reducing reliance on remote servers. Emphasizing artificial intelligence’s (AI) role in data analysis and modeling, we highlight its benefits for adaptable monitoring outcomes. From our analysis of glucose biosensors, communication standards, cloud tech, and AI, we outline applications for intelligent glucose monitoring systems. Notable achievements and challenges are summarized, guiding future research. Our review aims to aid technologists and medical professionals in understanding such systems, offering guidance for diabetes care enhancement. Ultimately, we strive to advance diabetes diagnosis, treatment, and quality of life using intelligent medical technologies.