Abstract
There are Optimization Problems that are too complex to be solved efficiently by deterministic methods. For these problems, where deterministic methods have proven to be inefficient, if not completely unusable, it is common to use approximate methods, that is, optimization methods that solve the problems quickly, regardless of their size or complexity, even if they do not guarantee optimal solutions. In other words, methods that find acceptable solutions, efficiently. One particular type of approximate method, which is particularly effective in complex problems, are metaheuristics. Particle Swarm Optimization is a population-based metaheuristic, which has been particularly successful. In order to broaden the application and overcome the limitation of Particle Swarm Optimization, a discrete version of the metaheuristics is proposed. The Discrete Particle Swarm Optimization, DPSO, will change the PSO algorithm so it can be applied to discrete optimization problems. This alteration will focus on the velocity update equation. The DPSO was tested in an instance of the Traveling Salesman Problem, att48, 48 points problems proposed by Padberg and Rinaldi, which showed some promising results.

Abstract
There are different clean energy production technologies, including wind energy production. This type of energy, among renewable energies, is one of the least predictable due to the unpredictability of the wind. The wind prediction has been a deeply analysed field since has a considerable share on the green energy production, and the investments on this sector are growing. The efficiency and stability of power production can be increased with a better prediction of the main source of energy, in our case the wind. In this paper, some techniques inspired by Biological Inspired Optimization Techniques applied to wind forecast are compared. The wind forecast is very important to be able to estimate the electric energy production in the wind farms. As you know, the energy balance must be checked in the electrical system at every moment. In this study we are going to analyse different methodologies of wind and power prediction for wind farms to understand the method with best results.

Abstract
The need for sustainable power production has led to the development of more innovative approaches to production and storage. In light of this hydrogen production through wind power has emerged as sufficient in ensuring that the objectives of the Paris Agreement are made. This paper discusses the state-of-art models and controls used in ensuring that greater efficiency is achieved in the processes of energy to hydrogen transformation. The paper concludes with a comparison of the models and determination of one which suffices in ensuring that hydrogen/energy transformation is more efficient.

Abstract
In this paper, a decision support application, for the air cargo planning and distribution, is proposed. The freight forwarding sector has been working to be assertive and efficient in responding to the market through an efficient approach to planning and allocation problems. The main goal is to minimize costs and improve performance. A real air cargo distribution problem for a freight forwarder was addressed. This project emerged from the need to efficiently plan and minimize costs for the distribution of thousands of m(3) (cubic meters) of air cargo, while considering the market restrictions, such as aircraft availability and transportation fees. Through the GRG algorithm adaptation to the real problem, it was possible to respond to the main goal of this paper. The development of an easy-to-use application ensures a quick response in the air distribution planning, focusing on cost reduction in transportation. With the application development it is possible to obtain real earnings with immediate effect.

Abstract
Renewable energy sources such as solar and wind are now competitive with traditional fossil and nuclear power when generating but that is just the challenge. When not generating can be a problem for grid integration and the main challenge to the widespread acceptance and dissemination of solar and wind, and the focus of research for the next generation of energy engineers. Intermittent, the adjective most associated with solar and wind energy has been and continues to be the focus of research by power engineers, AI professionals, and system scientists from the late 20th century and is the critical factor in the design of the future power grids, The most obvious solution is energy storage but then the choice of the storage method and size are complex problems. Will best solutions involve pumped hydro, Li-Ion batteries, or hydrogen generation? Or will next-generation ultra-capacitors, or high-speed flywheels gyros, or some yet to be discovered device will be the dominating technologies? The primary objective of the storage designs will be based on what's best for the reliability and efficiency of the grid, and simultaneously optimizing cost and environmental impact functions. Socio-economic and geopolitical considerations must also be considered to satisfy local or regional constraints. There is also the question of purpose: will it be sized for grid stability, or medium, or long-term storage. This factor will depend on the specific grid requirements. The focus of this paper is to study multi-source renewable energy systems that include storage called HRES or Hybrid Renewable Energy with Storage. This study describes the development of a behind-the-meter Energy Management System (EMS) for an HRES, under the assumption that Real-Time Pricing (RTP) is offered by a utility supplying power to a medium-size office complex. A cost function to be minimized is introduced to optimize the contribution of each energy source. Also, this work develops the basis of a platform for decision-makers to evaluate the viability of the optimized system in conjunction with the financial feasibility analysis.

Abstract
Health Remote Monitoring Systems (HRMS) offer the ability to address health-care human resource concerns. In developing nations, where pervasive mobile networks and device access are linking people like never before, HRMS are of special relevance. A fundamental aim of this research work is the realization of technological-based solution to triage and follow-up people living with dementias so as to reduce pressure on busy staff while doing this from home so as to avoid all unnecessary visits to hospital facilities, increasingly perceived as dangerous due to COVID-19 but also raising nosocomial infections, raising alerts for abnormal values. Sensing approaches are complemented by advanced predictive models based on Machine Learning (ML) and Artificial Intelligence (AI), thus being able to explore novel ways of demonstrating patient-centered predictive measures. Low-cost IoT devices composing a network of sensors and actuators aggregated to create a digital experience that will be used and exposure to people to simultaneously conduct several tests and obtain health data that can allow screening of early onset dementia and to aid in the follow-up of selected cases. The best ML for predicting AD was logistic regression with an accuracy of 86.9%. This application as demonstrated to be essential for caregivers once they can monitor multiple patients in real-time and actuate when abnormal values occur.