Considering the exponential growth of today’s industry and the wastewater results of its processes, it needs to have an optimal treatment system for such effluent waters to mitigate the environmental impact generated by its discharges and comply with the environmental regulatory standards that are progressively increasing their demand. This leads to the need to innovate in the control and management information systems of the systems responsible to treat these residual waters in search of improvement. This paper proposes the development of an intelligent system that uses the data from the process and makes a prediction of its behavior to provide support in decision making related to the operation of the wastewater treatment plant (WWTP). To carry out the development of this system, a multilayer perceptron neural network with 2 hidden layers and 22 neurons each is implemented, together with process variable analysis, time-series decomposition, correlation and autocorrelation techniques; it is possible to predict the chemical oxygen demand (COD) at the input of the bioreactor with a one-day window and a mean absolute percentage error (MAPE) of 10.8%, which places this work between the adequate ranges proposed in the literature.

Today reading comprehension is considered an essential skill in modern life, therefore, higher education students require more specific skills to understand, interpret and evaluate texts effectively. Short answer questions (SAQs) are one of the relevant and proper tools for assessing reading comprehension skills. Unlike multiple-choice questions, SAQs allow for the assessment of cognitive abilities such as attention, language, perception, and problem solving. However, the task of SAQs scoring is time-consuming and susceptible to ambiguity. Automatic Short Answer Grading (ASAG) is a new paradigm that could help solve these problems. This experimental analysis aims to implement ASAG using several approaches to sentence embedding based on deep learning with a multilayer perceptron regression layer on the top, trained with a reading comprehension dataset based on aphorisms. For experimental testing, the available dataset is composed of answers given by 199 undergraduate students in Spanish. BERT and Skip-Thought models are tested with different hyperparameters to find the best performance in terms of Pearson correlation coefficient and RMSE against human experts grades. The result of the current study showed that BERT model performed better than other approaches.

This paper considers the problem of characterize the way people drive applied to driver assistance systems and integrated safety systems without using direct driver signals. To make this, is proposed the design of a driver behaviors classifier based on a previous intelligent driving diagnosis system development by us [1]. This, take into account signals that can be acquired by a GPS data logging system: position, velocity, accelerations and steering angle. The classifier proposed, present the structure of an intelligent driver behaviors model based on neural networks and using as inputs statistical transformations of the driving diagnosis time signals: steering profiles, pedals uses, speeding and getting out of the lane and road. The validation of this classifier is developed in two applications: driver identification for security systems and to classify a driver into one of two categories, aggressive and moderate. The proposed approach has been implemented in real environment and its performance tested in simulation runs. Experimental results presented in this paper shows that our intelligent driving diagnosis system is able to classify different kinds of drivers with a high degree of reliability.

To reduce demand and discharge, instead of industrial wastewater being not enough treated and disposed of, it can be recycled, reused, or recovered considering well management. Thus, having a substantial decrease in the water need and environmental impacts. The challenge is to select the proper combination of processes based on the wastewater quality and budget limitations. Therefore, we aimed to provide a distinctive and comprehensive perspective on the current state of industrial wastewater treatment technologies by giving a complete overview of their major advantages, disadvantages, and upcoming challenges in the context of the circular economy. This study considered 198 peer-reviewed scientific publications from the Scopus and Google Scholar databases, mostly from the last 5 years. For this, specific search terms and Boolean Operators were applied as inclusion and exclusion criteria. Up to 57 different technologies, from conventional to advanced, were reviewed for reliable and sustainable industrial wastewater treatment and derived sludges. We evidence a vast potential to exploit and make them viable for the circular economy, even in the mature ones. Overall, we identify that regardless of the alternative, in particular those not vastly implemented, future investigations should prioritize: (1) The capacity to remove selected pollutants and decrease impurities; (2) energy recovery and efficiency; (3) environmental safety; (4) economic viability and industrial implementation; (5) hybridization; and (6) sustainability through waste processing. GRAPHICAL ABSTRACT

This paper presents the Hybrid Renewable Energy System (HYRES), a powerful tool to contribute to the viability analysis of energy systems involving renewable generators. HYRES considers various input parameters related to climatic conditions, statistical reliability, and economic views; in addition to offering multi-objective optimizations using Genetic Algorithms (GAs) that have a better cost-benefit ratio than mono-objective optimization, which is the technique used in several commercial systems like HOMER, a worldwide leader in microgrid modeling. The use of intelligent techniques in HYRES allows optimal sizing of hybrid renewable systems with wind and solar energy generators adapted to different conditions and case studies. The elements that affect the system design like buying and selling energy from/to the grid and the use of storage units can be included in system configuration according to the need. Optimization approaches are selectable and include Initial Cost, Life Cycle Cost, Loss of Power Probability, and Loss of Power Supply Probability.

The increasing frequency of technological events has resulted in significant damage to the environment, human health, social stability, and economy, driving ongoing scientific development and interest in emergency management (EM). Traditional EM approaches are often inadequate because of incomplete and imprecise information during crises, making fast and effective decision-making challenging. Computational Intelligence techniques (CI) offer decision-supporting capabilities that can effectively address these challenges. However, there is still a need for deeper integration of emerging computational intelligence techniques to support evidence-based decision-making while also addressing gaps in metrics, standards, and protocols for emergency response and scalability. This study presents a coordinated decision-making system for multiple types of emergency case scenarios for technological disaster management based on CI techniques, including an Improved Genetic Algorithm (IGA), and Multi-objective Particle Swarm Optimization (MOPSO). The IGA enhances emergency performance by optimizing the task assignment for multiple agents involved in emergency response with coordination mechanisms, resulting in an approximately 15% improvement compared to other state-of-the-art methods. Ultimately, this study offers a promising foundation for future research to develop effective strategies for mitigating the impact of technological disasters on society and the environment.