Abstract
With the objective of developing a diagnostic tool, we have used the immersion optical clearing method and studied normal and pathological tissues (cancer, diabetes) under treatment by optical clearing agents (OCAs). In order to quantify pathology status OCA diffusion properties in different tissues were measured. We have demonstrated that free water content in cancerous tissues is higher than in normal.

Abstract
The design of web and mobile applications is one of the most challenging fields of the current information technology landscape. Increasingly, companies intend to have a strong presence in the information society, which allows them to advertise their products, services, make online business, interact with customers, among others. However, the development and design of web and mobile solutions have numerous challenges and best practices that should be known and applied. In this chapter, the authors adopt a qualitative methodology based on multiple case studies that allow them to identify a total of six challenges and best practices that are later confronted and compared with the recent findings on the coverage of the topic. © 2018, IGI Global.

Abstract
The Industry 4.0 advent, advocating the digitalization and transformation of current production systems towards the factories of future, is introducing significant social and technological challenges. Cyber-physical systems (CPS) can be used to realize these Industry 4.0 compliant systems, integrating several emergent technologies, such as Internet of Things, big data, cloud computing and multi-agent systems. The paper analyses the advantages of using biological inspiration to empower CPS, and particularly those developed using distributed and intelligent paradigms such as multi-agent systems technology. For this purpose, the self-organization capability, as one of the main drivers in this industrial revolution is analysed, and the way to translate it to solve complex industrial engineering problems is discussed. Its applicability is illustrated by building a self-organized cyber-physical conveyor system composed by different individual modular and intelligent transfer modules.

Abstract
This paper evaluates a robust Model Predictive Controller (MPC) based on Sliding Modes (SMPC) for the downstream level control in irrigation canal pools. Its features are compared with the conventional Generalized Predictive Controller (GPC), regarding set point tracking (water level) and output disturbances (offtake discharges). Simulation results suggest feasibility of applying SMPC for gate manipulation, with suitable command signals and robustness.

Abstract
Concentrating solar power (CSP) plants with thermal energy storage (TES) are emerging renewable technologies with the advantage that TES decreases the uncertainty in the generation of CSP plants. This study introduces a stochastic mixed integer linear programming model, where the objective function is the maximization of the expected profit that can be obtained by selling the energy generated by the CSP plant in the day-ahead electricity market. The proposed model considers three main blocks of constraints, namely, renewable generator constraints, TES constraints, and electricity market constraints. The last category of constraints considers the penalties incurred due to positive or negative imbalances in the balancing market. A case study using data from the Spanish electricity market is introduced, described and analyzed in terms of trading of the CSP plant generation. The conclusions highlight the influence of TES capacity on the energy trading profile, the expected profits and the volatility (risk) in the trading decisions.

Abstract
In this paper, a new system identification algorithm is proposed for linear and time invariant systems with multiple input and single output. The system is described by a state-space model in the canonical observable form and represented by a Luenberger observer with a known state matrix. Thence, the identification problem is reduced to the estimation of the system input matrix and the observer gain which can be performed by a simple Least Square Estimator. The quality of the estimator depends on the observer state matrix. In the proposed algorithm, this matrix is found by an iterative process where, in each iteration, a state matrix called curiosity is generated. A weight depending on the value of the Least Square Cost is associated to each curiosity. The optimal state matrix is the barycenter of the curiosities. This iterative process is a free derivative optimization algorithm with its roots in non-iterative barycenter methods previously introduced to solve adaptive control and system identification problems. Although the Barycenter iterative version was recently proposed as an optimization method, here it will be implemented in an identification algorithm for the first time.