Abstract
The concept of 'tissue optical windows' and method of optical clearing (OC) based on controllable and reversible modification of tissue optical properties by their soaking with a biocompatible optical clearing agent (OCA) are prsented. Fundamentals and major mechanisms of OC allowing one to enhance optical imaging facilities and laser treatment efficiency of living tissues are described. Perspectives of immersion optical clearing/contrasting technique aiming to enhance optical imaging of living tissues by using different imaging modalities working in the ultra-broad wavelength range from deep UV to terahertz waves are discussed. It demonstrated that immersion OC method can be applied to evaluate the characteristic diffusion properties of water and OCA in various tissues and even discriminate between the mobile water content in normal and pathological tissues.

Abstract
The evaluation of the optical clearing mechanisms in tissues provides information about the efficiency of the clearing treatment. One of such mechanisms is the refractive index matching, which is created by the partial replacement of tissue water by an optical clearing agent with higher refractive index, better matched to the index of tissue scatterers. With the objective of evaluating the refractive index matching mechanism for a wide spectral range and comparing its magnitude between treatments with different clearing agent osmolarities, thickness and collimated transmittance measurements were obtained from human colorectal muscle samples under treatment with 20%-, 40% and 60%-glycerol. Such measurements were used in a calculation model to obtain the refractive index kinetics for the interstitial fluid and for the whole tissue. The calculation results show that the refractive index matching has a stronger effect in the ultraviolet and that such matching is more effective for higher agent concentrations in the treating solutions.

Abstract
AbstractNowadays, the low efficiency of solar energy power plant systems brings about uneconomical performance and high-cost uncompetitive industries compared with the traditional fossil fuel ones. In order to overcome these kinds of issues, in this study, the performance of a solar tower coupled with a Rankine cycle is scrutinized using the first and second laws of thermodynamic. Moreover, a numerical code has been developed in the GNU Octave software environment to calculate the precise value for both energy and exergy losses of each component. Furthermore, the sensitivity analysis approach corresponds to the variation of several inner parameters such as direct normal irradiation (DNI), molten salt outlet temperature (MSOT), and the molten salt velocity (MSV), environmental parameters such as wind speed and ambient temperature have been performed. In addition, the overall losses are calculated by considering all possible forms of losses such as convection, conduction, reflection, and emission, and also, the portion of each source of these losses are determined. The obtained results indicate that the maximum exergy loss occurs in the central receiver system (CRS), while the major energy loss occurs in the turbine located in the power block. The sensitivity analysis shows that the rise of DNI significantly increases the exergy%energy efficiency of the cycle and also, the portion of loss related to emission heat transfer would be enhanced. Moreover, the variation of the MSV and MSOT illustrate influencing the performance of the cycle; consequently, the MSOT changes demonstrate a reverse relation with energy and direct relation with exergy efficiency. Accordingly, an optimum value of 650K is calculated for the MSOT as well as an optimum value of 2 m%s for the MSV. The environmental sensitivity analysis indicates that the enhancement of wind speed and ambient temperature has a negative impact on the net output of the cycle, which is not desirable.

Abstract
The future of healthcare systems is being shaped by incorporating emerged technological innovations to drive new models for patient care. By acquiring, integrating, analyzing, and exchanging medical data at different system levels, new practices can be introduced, offering a radical improvement to healthcare services. This article presents a novel smart and secure Healthcare system (ssHealth), which, leveraging advances in edge computing and blockchain technologies, permits epidemics discovering, remote monitoring, and fast emergency response. The proposed system also allows for secure medical data exchange among local healthcare entities, thus realizing the integration of multiple national and international entities and enabling the correlation of critical medical events for, for example, emerging epidemics management and control. In particular, we develop a blockchain-based architecture and enable a flexible configuration thereof, which optimize medical data sharing between different health entities and fulfil the diverse levels of Quality of Service (QoS) that ssHealth may require. Finally, we highlight the benefits of the proposed ssHealth system and possible directions for future research.

Abstract
Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. A key challenge, however, is to collect and select real-time and reliable information for the correct classification of unexpected, and often uncommon, events that may happen on the road. Indeed, the data generated by vehicles, or received from neighboring vehicles, 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. In particular, given the available information, our solution selects the data to add to the training set by trading off between two essential features: quality and diversity. The results, obtained using realworld data sets, show that our method significantly outperforms state-of-the-art solutions, providing high classification accuracy at the cost of a limited bandwidth requirement for the data exchange between vehicles.

Abstract