Skin consists of epidermis and dermis layers that have distinct optical properties. The quantification of skin optical properties is commonly achieved by modeling photon propagation in tissue using Monte Carlo (MC) simulations and iteratively fitting experimentally measured diffuse reflectance spectra. In order to speed up the inverse fitting process, time-consuming MC simulations have been replaced by artificial neural networks to quickly calculate reflectance spectra given tissue geometric and optical parameters. In this study the skin was modeled to consist of three layers and different scattering properties of the layers were considered. A new inverse fitting procedure was proposed to improve the extraction of chromophore-related information in the skin, including the hemoglobin concentration, oxygen saturation and melanin absorption. The performance of the new inverse fitting procedure was evaluated on 40 sets of simulated spectra. The results showed that the fitting procedure without knowing the epidermis thickness extracted chromophore information with accuracy similar to or better than fitting with known epidermis thickness, which is advantageous for practical applications due to simpler and more cost-effective instruments. In addition, the melanin volume fraction multiplied by the thickness of the melanin-containing epidermis layer was estimated more accurately than the melanin volume fraction itself. This product has the potential to provide a quantitative indicator of melanin absorption in the skin. In-vivo cuff occlusion experiments were conducted and skin optical properties extracted from the experiments were comparable to the results of previously reported in vivo studies. The results of the current study demonstrated the applicability of the proposed method to quantify the optical properties related to major chromophores in the skin, as well as scattering coefficients of the dermis. Therefore, it has the potential to be a useful tool for quantifying skin optical properties in vivo.

The reduction of adverse environmental effects and the socioeconomic advantages of renewable energy systems promote greater integration of distributed energy systems into the traditional electrical networks. A new type of sharing economy is emerging with the sharing of energy resources to reduce transaction costs by using platform services in the cloud. Given the obstacles posed by the legacy system and various forms of renewable energy integration, Distributed Energy and Micro Grids (DE‐MG) are an efficient means of raising the quality of energy services. Rules for microgrid scalability, maintaining a budget, and security can make this difficult. Consumers are better at receiving the best renewable energy allotment price using a cloud‐based Peer‐to‐Peer (P2P) network. The main objective is to lower the daily energy cost of microgrid power in commercial buildings. In the proposed work, cloud‐based P2P for peer‐Multi Agent System (p‐MAS) optimization techniques are used to reduce system peak and integrated Demand Response (DR) with Energy Management System (EMS) in a commercial MG. To fill knowledge gaps about how various power market architectures and individual decision‐making processes impact local interactions and market outcomes, cloud‐based P2P for Modelling Leveraging Agents (MLA) is used for bill calculation. A performance measure is finally created for cost evaluation and reliability to measure the social benefits of cloud‐based P2P models for exchanging energy. For various price environments and resource types, a comparison between the proposed cloud‐based P2P model with an existing P2P model for exchanging energy is provided. The primary use of a distributed P2P model for exchanging power in a microgrid is to reduce electricity costs and increase grid environment reliability.

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