Abstract
In this paper, we describe the combination of ultraviolet (UV) spectroscopy with the optical clearing technique to induce new tissue windows, evaluate their efficiency, study the diffusion properties of agents and discriminate cancer. The use of highly concentrated glycerol solutions has induced high efficiency clearing effects in the UV, both in human colorectal and gingival tissues. The protein dissociation rate obtained for colorectal tissues was approximately 3 times higher in pathological than in normal mucosa and the kinetics of diffuse reflectance in the UV allowed to estimate the diffusion coefficient for water in gingival mucosa at glycerol action as (1.78 +/- 0.26) x 10(-6) cm(2)/s.

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Abstract

Abstract
Erosion has been recognized as one of the major threats for industries involving multiphase transportation pipelines. Within the last decades, effective parameters on wear pattern have been identified. As a result, the (famous) V-shaped erosion profile has been detected for the pipes' elbow section. In this study, CFD is employed to investigate the erosion mechanism on the square duct elbows. A novel erosion pattern has been observed for square ducts in comparison with the pipes. The impact of several parameters (particle and flow velocity, secondary flow, turbulent intensity, particle streamline) has been inspected as well. It has been led to the conclusion that the erosion rate of square ducts is lower than common pipes, especially when either higher flow velocities or bigger particles size are employed.

Abstract
Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. Reacting to such situations requires accurate classification for uncommon events, which in turn depends on the selection of large, diverse, and high-quality training data. In fact, the data available at a vehicle (e.g., photos of road signs) may be affected by errors or have different levels of resolution and freshness. To tackle this challenge, we propose an active learning framework that, leveraging the information collected through onboard sensors as well as received from other vehicles, effectively deals with scarce and noisy data. Given the information received from neighboring vehicles, our solution: (i) selects which vehicles can reliably generate high-quality training data, and (ii) obtains a reliable subset of data to add to the training set by trading off between two essential features, i.e., quality and diversity. The results, obtained with different real-world datasets, demonstrate that our framework significantly outperforms state-of-the-art solutions, providing high classification accuracy with a limited bandwidth requirement for the data exchange between vehicles.

Abstract
Medical data exchange between diverse e-health entities can lead to a better healthcare quality, improving the response time in emergency conditions, and a more accurate control of critical medical events (e.g., national health threats or epidemics). However, exchanging large amount of information between different e-health entities is challenging in terms of security, privacy, and network loads, especially for large-scale healthcare systems. Indeed, recent solutions suffer from poor scalability, computational cost, and slow response. Thus, this article proposes medical-edge-blockchain (MEdge-Chain), a holistic framework that exploits the integration of edge computing and blockchain-based technologies to process large amounts of medical data. Specifically, the proposed framework describes a healthcare system that aims to aggregate diverse health entities in a unique national healthcare system by enabling swift, secure exchange, and storage of medical data. Moreover, we design an automated patients monitoring scheme, at the edge, which enables the remote monitoring and efficient discovery of critical medical events. Then, we integrate this scheme with a blockchain architecture to optimize medical data exchanging between diverse entities. Furthermore, we develop a blockchain-based optimization model that aims to optimize the latency and computational cost of medical data exchange between different health entities, hence providing effective and secure healthcare services. Finally, we show the effectiveness of our system in adapting to different critical events, while highlighting the benefits of the proposed intelligent health system.