Artificial intelligence has many fields of application with an increasing computational processing power, and the algorithms are reaching human performance on complex tasks. Entomological characterization of insects represents an essential activity to drive actions to control the vector-borne diseases. Identification of the species and sex of insects is essential to map and organize the control measurements by the public health system in most areas where transmission is actively occurring. In many places in the world, the methodology done for identification of the mosquitos is by visual examination from human trained researchers or technicians. This activity is time-consuming and requires several years of experience to have skills to do the job. This chapter addresses the application of artificial intelligence for identification of mosquitos associated with vector-borne diseases. Benefits, limitations, and challenges of the use of artificial intelligence on the control of vector-borne diseases are discussed in this review.
Part of the book: Vectors and Vector-Borne Zoonotic Diseases
The conventional sources of energy such as oil, natural gas, coal, or nuclear are finite and generate environmental pollution. Alternatively, renewable energy source like wind is clean and abundantly available in nature. Wind power has a huge potential of becoming a major source of renewable energy for this modern world. It is a clean, emission-free power generation technology. Wind energy has been experiencing very rapid growth in Brazil and in Uruguay; therefore, it’s a promising industry in these countries. Thus, this rapid expansion can bring several regional benefits and contribute to sustainable development, especially in places with low economic development. Therefore, the scope of this chapter is to estimate short-term wind speed forecasting applying computational intelligence, by recurrent neural networks (RNN), using anemometers data collected by an anemometric tower at a height of 100.0 m in Brazil (tropical region) and 101.8 m in Uruguay (subtropical region), both Latin American countries. The results of this study are compared with wind speed prediction results from the literature. In one of the cases investigated, this study proved to be more appropriate when analyzing evaluation metrics (error and regression) of the prediction results obtained by the proposed model.
Part of the book: Aerodynamics
The adoption of augmented reality-based instructions enhances maintenance operations by shortening job completion time and reducing errors. However, scaling augmented reality in industrial settings remains costly since content authoring demands computational skills such as 3D modeling and programming. Furthermore, processes can easily become obsolete, causing maintainers to abandon written instructions. So, we propose an augmented reality-based participatory content authoring workflow for maintenance tasks. We followed the Design Science Research paradigm, which included a literature review, the conception of a workflow, and a simulation to evaluate the proposed workflow’s validity. We found that current workflows overlook participatory content authoring involving maintainers and that most research focuses on describing the technical architecture of proposed systems rather than a workflow that supports the use of technology in industrial settings. Regarding our proposed participatory workflow, most respondents stated it was simple to use, improved their capacity to develop augmented reality content and would help the industry adopt augmented reality. As a result, our participatory authoring workflow can optimize augmented reality content authoring during maintenance, encouraging the maintainers’ interaction, and provide opportunities for procedure improvement. We conclude that non-programmer-friendly augmented reality software tools save content production time while enhancing users’ perceptions of their own technological talents.
Part of the book: Modern Development and Challenges in Virtual Reality