Abstract
In this chapter, other areas of application for optical clearing agents (OCAs) are presented. The osmotic properties of agents are highly important in dermatology, cosmetics, and pharmacology, if topical application to the skin is desired. After addressing this application in Sect. 8.2, tissue poisoning and discussing the osmotic properties of certain poisons or toxic compounds will be done in Sect. 8.3. The importance of evaluating the diffusion properties of those substances in the skin, eye, and other inner tissue is indicated as a tool for optimizing treatment or decontamination dosage and procedures. Section 8.4 is used to discuss the application of agents in food industry. The dehydration capabilities of certain agents, such as sodium chloride or glycerol, are presented, and the advantages of treating fruit, meat, or fish with sugars to improve their organoleptic properties during preservation are also presented. Finally, the application of OCAs for tissue or organ preservation is presented in Sect. 8.5, where some cases for preservation of eye tissues at room temperature made with glycerol will be discussed. The use of OCAs as cryoprotectants at low temperatures is also explained. In all these applications, we refer the applicability of the method described in Sect. 6.4 to evaluate the diffusion properties of water, poisons, or drugs for ex vivo tissue samples. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
To overcome the high light-scattering problem that occurs in biological tissues, we present in this chapter the different clearing methods known today. Most of these methods have benefits and downsides, depending on the application for which they are used. The optical immersion method is introduced as a better, reliable, and reversible way to turn tissues clear. The major benefits and advantages of this method such as its reversibility, the lack of side effects, and application in large wavelength range will be presented. A description of the molecular diffusion of optical clearing agents is given to explain the reduction in the refractive index mismatch that natural tissues have. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This study analyzed the accuracy of a BGL predictive model (BGL-PM) for type 1 diabetes mellitus patients (T1DM) in a real-world environment. The study population consisted of 10 individuals with T1DM, half of them were female, age 33 (SD:11.2), BMI of 26.1 (4.2) and 60% were under carbohydrate-count treatment. After consent, patients underwent a medical evaluation and registered their daily activities using a smartphone application (GlucoTrends) for 28 days, with BGL and heart rate continuously monitored. BGL-PM was developed using a Deep Learning architecture, based on Recurrent Neural Networks. Models were trained for each patient using different training sets sizes (7, 14, 21 days). Prediction accuracy was evaluated by Mean Absolute Percentage Error (MAPE) on the last 5 days for different Prediction Horizons (PH): 30, 60, 120, 180 and 360 minutes, comparing full day and nocturnal period. The model predicted BGL with relevant accuracy for the dataset with 21 training days up to 60 minutes in both periods: full day (median MAPE 22.5%) and nocturnal (14.3%) (Figure). The BGL-PM was able to provide useful BGL predictions, especially during the night period, which can be improved by increasing the training period. Consequently, this BGL-PM poses as a complementary tool for the prevention of acute complications such as hypoglycemia and hyperglycemia in the management of DM. Disclosure M. Foss-Freitas: None. G.S. Moreira: Stock/Shareholder; Self; GlucoGear Tecnologia. V.P. Antloga: Stock/Shareholder; Self; GlucoGear Tecnologia. C.R. Neto: Research Support; Self; University of Sao Paulo. E.M. Rodrigues: Consultant; Self; GlucoGear Tecnologia. M.F. da Costa: Research Support; Self; GlucoGear. A.P. dos Santos: None. Y.K. Matsumoto: Board Member; Self; GlucoGear. Stock/Shareholder; Self; GlucoGear. Other Relationship; Self; GlucoGear.

Abstract
Precision agriculture nowadays has great importance as it brings together the knowledge acquired through traditional cultivation techniques with precision and technological automation. One of the inherent techniques of precision agriculture is hydroponics, with plants growing using aqueous solutions and without soil availability. Although NFT (Nutrient Film Technique) systems are already well-developed systems, there is a big difference between home projects and highly automated processes, which in turn require high investment values. Among other things, in this work, the aim was to study and developed algorithms that allow the efficient recirculation of water, allowing electricity savings to be around 40% compared to more traditional systems. © 2019 IEEE.

Abstract

Abstract
Line-following robots can recognize and follow a line drawn on a surface. Their operating principles have elements that could be used in the development of numerous autonomous technologies, with applications in education and industry. This class of robots usually represent the first contact students have with educational robotics, being used to develop students' logic thinking and programming skills. The cost of robotic platforms is still prohibitive in low-budget schools and universities, which makes almost impossible having a platform for each small group of students in a classroom, harming the learning process. This work proposes a 3D web-based open-source simulator for Pololu's 3Pi line-following robots, making such technology more accessible and available even for distance learning courses. The developed software simulates the robot's physical structure, behavior, and operations-as being able to read surfaces-, enabling the user to observe the robot following the line as the code commands. The simulator was validated based on experiments that included motion analysis and time measurements of pre-stablished tasks so that its execution could be more coherently based on what happens in reality.