With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multi-floor localization. Specifically, we devise a novel classification model and a novel positioning model by combining a Stacked Auto-Encoder (SAE) with a one-dimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high accuracy in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset and compare the performance with several state-of-the-art methods. Moreover, we further propose a newly collected WiFi fingerprinting dataset UTSIndoorLoc and test the positioning model of CNNLoc on it. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on building-level localization and floor-level localization, respectively.

Engineered extracellular vesicles (EVs) carrying therapeutic molecules are promising candidates for disease therapies. Yet, engineering EVs with optimal functions is a challenge that requires careful selection of functionally specific vesicles and a proper engineering strategy. Here, we constructed chimeric apoptotic bodies (cABs) for on-demand inflammation modulation by combining pure membrane from apoptotic bodies (ABs) as a bioconjugation/regulation module and mesoporous silica nanoparticles (MSNs) as a carrier module. MSNs were preloaded with anti-inflammatory agents (microRNA-21 or curcumin) and modified with stimuli-responsive molecules to achieve accurate cargo release at designated locations. The resulting cABs actively target macrophages in the inflammatory region and effectively promote M2 polarization of these macrophages to modulate inflammation due to the synergistic regulatory effects of AB membranes and the intracellular release of preloaded cargos. This work provides strategies to arbitrarily engineer modular EVs that integrate the advantages of natural EVs and synthetic materials for various applications.

With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most accessible and practical approaches to localize a mobile user. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization framework with WiFi fingerprints for multi-building and multi-floor localization. We propose a novel classification model by combining Stacked Auto-Encoder (SAE) with one-dimensional CNN. The SAE can be used to extract key features more precisely from sparse Received Signal Strength (RSS) data, and the CNN can be trained to effectively achieve high success rates in the localization phase. We evaluate CNNLoc with state-of-the-arts as benchmarks on the UJIIndoor-Loc dataset and Tampere dataset. CNNLoc shows its excellence in both building-level and floor-level classifications and outperforms the existing solutions with 100% success on building success rate and an average success rate over 95% on floor-level localization.

The preparation and characterization of a soybean protein isolate peptide-Se chelate with remarkably antioxidant activity in vitro and in vivo.

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