| 1. |
- Ju, Ying, et al.
(författare)
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Joint Secure Offloading and Resource Allocation for Vehicular Edge Computing Network : A Multi-Agent Deep Reinforcement Learning Approach
- 2023
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 24:5, s. 5555-5569
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Tidskriftsartikel (refereegranskat)abstract
- The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share the frequency spectrum preoccupied with the vehicle-to-vehicle (V2V) communication links. We use Wyner's wiretap coding scheme to obtain the achievable secrecy rate and guarantee that confidential information cannot be decoded by multiple mobile eavesdroppers. We aim at minimizing the system processing delay while securing the wireless offloading process, by jointly optimizing the transmit power, the frequency spectrum selection and the computation resource allocation. We formulate the optimization problem as a multi-agent collaborative optimal decision problem and solve it with a double deep Q-learning algorithm. Besides, we set a punishment mechanism for the rate degradation to guarantee the communication quality of each V2V link. Simulation results demonstrate that multiple VU agents adopting the SORA scheme can rapidly adapt to the highly dynamic VEC networks and cooperate to improve the system delay performance while increasing the secrecy probability.
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| 2. |
- De Santis, Martina M, et al.
(författare)
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Extracellular-Matrix-Reinforced Bioinks for 3D Bioprinting Human Tissue
- 2021
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Ingår i: Advanced Materials. - : Wiley. - 1521-4095 .- 0935-9648. ; 33:3
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Tidskriftsartikel (refereegranskat)abstract
- Recent advances in 3D bioprinting allow for generating intricate structures with dimensions relevant for human tissue, but suitable bioinks for producing translationally relevant tissue with complex geometries remain unidentified. Here, a tissue-specific hybrid bioink is described, composed of a natural polymer, alginate, reinforced with extracellular matrix derived from decellularized tissue (rECM). rECM has rheological and gelation properties beneficial for 3D bioprinting while retaining biologically inductive properties supporting tissue maturation ex vivo and in vivo. These bioinks are shear thinning, resist cell sedimentation, improve viability of multiple cell types, and enhance mechanical stability in hydrogels derived from them. 3D printed constructs generated from rECM bioinks suppress the foreign body response, are pro-angiogenic and support recipient-derived de novo blood vessel formation across the entire graft thickness in a murine model of transplant immunosuppression. Their proof-of-principle for generating human tissue is demonstrated by 3D bioprinting human airways composed of regionally specified primary human airway epithelial progenitor and smooth muscle cells. Airway lumens remained patent with viable cells for one month in vitro with evidence of differentiation into mature epithelial cell types found in native human airways. rECM bioinks are a promising new approach for generating functional human tissue using 3D bioprinting.
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| 3. |
- Marlevi, David, doktorand, et al.
(författare)
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Combined spatiotemporal and frequency-dependent shear wave elastography enables detection of vulnerable carotid plaques as validated by MRI
- 2020
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Fatal cerebrovascular events are often caused by rupture of atherosclerotic plaques. However, rupture-prone plaques are often distinguished by their internal composition rather than degree of luminal narrowing, and conventional imaging techniques might thus fail to detect such culprit lesions. In this feasibility study, we investigate the potential of ultrasound shear wave elastography (SWE) to detect vulnerable carotid plaques, evaluating group velocity and frequency-dependent phase velocities as novel biomarkers for plaque vulnerability. In total, 27 carotid plaques from 20 patients were scanned by ultrasound SWE and magnetic resonance imaging (MRI). SWE output was quantified as group velocity and frequency-dependent phase velocities, respectively, with results correlated to intraplaque constituents identified by MRI. Overall, vulnerable lesions graded as American Heart Association (AHA) type VI showed significantly higher group and phase velocity compared to any other AHA type. A selection of correlations with intraplaque components could also be identified with group and phase velocity (lipid-rich necrotic core content, fibrous cap structure, intraplaque hemorrhage), complementing the clinical lesion classification. In conclusion, we demonstrate the ability to detect vulnerable carotid plaques using combined SWE, with group velocity and frequency-dependent phase velocity providing potentially complementary information on plaque characteristics. With such, the method represents a promising non-invasive approach for refined atherosclerotic risk prediction.
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