The aim of this work is to respond to the need to rethink the behavior and motivation of employees in their relationship with managers and social groups, i.e., one’s main goal is based on increasing engagement in order to reach organizational goals and job workers satisfaction, a complex concept that is influenced by different causes. Indeed, in this work it is analyzed the impact of working conditions on job satisfaction. This is where attention is drawn to the concept of entropy, since we are not focusing on the value a variable can take, but on the effort that has been expended to obtain it. The idea of entropy comes from a principle of thermodynamics dealing with energy. It usually refers to the idea that everything in the universe eventually moves from order to disorder, and entropy is the measurement of that change, that is used here to understand and assess the workers behavior and motivation. The subsequent formal model is based on a set of logical structures for knowledge representation and reasoning that conform to the above entropic view, then leading to an Artificial Neural Network approach to computation, an archetypal that considers the motive behind the action.

A number of guidelines for Psychosocial Risk Management in organizations have been proposed in recent decades; however, some reviews on the subject also highlights that the terms Stress and Psychosocial Risks (PRs) are not mentioned explicitly in most pieces of legislation, leading to lack of clarity on the terminology used. To improve the way of dealing with this type of vulnerability and to allow organizations to successfully manage PRs, this work proposes and characterizes a workable problem-solving method in which the PRs can be evaluated for the entropy they generate within the organization. The analysis and development of such a system is based on a series of logical formalisms for Knowledge Representation and Reasoning that are grounded on Logic Programming, complemented with an Artificial Neural Network approach to computing.

Over the last decades a number of guidelines have been proposed for best practices, frameworks, and cyber risk assessment in present computational environments. In order to improve cyber security vulnerability, in this work it is proposed and characterized a feasible methodology for problem solving that allows for the evaluation of cyber security in terms of an estimation of its entropic state, i.e., a predictive evaluation of its risk and vulnerabilities, or in other words, the cyber security level of such ecosystem. The analysis and development of such a model is based on a line of logical formalisms for Knowledge Representation and Reasoning, consistent with an Artificial Neural Networks approach to computing, a model that considers the cause behind the action.

Diabetes Mellitus is now a prevalent disease in both developed and underdeveloped countries, being a major cause of morbidity and mortality. Overweight/obesity and hypertension are potentially modifiable risk factors for diabetes mellitus, and persist during the course of the disease. Despite the evidence from large controlled trials establishing the benefit of intensive diabetes management in reducing microvasculars and macrovasculars complications, high proportions of patients remain poorly controlled. Poor and inadequate glycemic control among patients with Type 2 diabetes constitutes a major public health problem and a risk factor for the development of diabetes complications. In clinical practice, optimal glycemic control is difficult to obtain on a long-term basis, once the reasons for feebly glycemic control are complex. Therefore, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centred on Artificial Neural Networks, to evaluate the Diabetes states and the Degree-of-Confidence that one has on such a happening.

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

The objective of this paper is presents an experience report carried out during the learning-teaching processes in the courses involving the study of algorithms, programming logic, and programming language. in the Degree in Information Systems of Federal University of Santa Maria in the city of Frederico Westphalen - RS. The report presents the strategies and results, obtained from the application of active learning methodologies, placing the student as the center of the process, an active subject in the construction of his knowledge.

It is evident that with the advancement of technology, the frequency of its use in the most varied environments is increasing. In the educational field, we can see the growth of this scenario of technological integration, including its use aimed at obtaining knowledge through educational games or digital platforms. These two factors, together, result in the increase of initiatives for the learning of Information Technology (IT) and consequently the teaching of algorithms and logic programming, since it is the basis for a lot of knowledge in IT. The presented study focuses on demonstrating how logic programming can be an important learning tool for children through the main educational software and tools for this purpose, starting with a bibliographic research based on researchers in the education and development of children's thinking. In addition, the article approaches proposals for the insertion of Computational Thinking in education, also showing the results of a case study with an experimental class with 17 students from the 4th and 5th year of Elementary School. Through a descriptive analysis of the results obtained, it was possible to analyze the children's understanding of the concepts taught from their point of view, as well as observing the satisfactory performance in unanimity when using tools elucidated in the study, since all of them completed the proposed activities.

The identification, classification and recording of events leading to deterioration of wine quality is essential for developing appropriate strategies avoid them. This work introduces an adverse event reporting and learning system that can help preventing hazards and ensure the quality wines. The Eindhoven Classification Method (ECM) has been extended and adapted to the incidents of the wine industry. Logic Programming was used for Knowledge Representation and Reasoning (KRR) in order to model the universe of discourse, even in the presence of incomplete data, information or knowledge. On the other hand, the evolutionary process of the body of knowledge is to be understood as a process of devaluation, enabling the automatic extraction of knowledge and the generation of reports to identify the most relevant causes of errors lead to a poor wine quality. In addition, the answers to the problem are object of formal evidence through theorem proving.

School dropout is one of the major concerns of our society. Indeed, it is a complex phenomenon, resulting in economic and social losses, either to the individual, family or the community to which the person belongs. Academic difficulty and failure, poor attendance, retention, disengagement from school together with family and socio-economic reasons can lead to such occurrence. In this work Logic Programming was used for knowledge representation and reasoning, letting the modeling of the universe of discourse in terms of defective data, information and knowledge. Artificial Neural Networks were used in order to evaluate potential situations of school dropout and the degree of confidence that one has on such a happening.

GlioBastoma Multiforme (GBM) is an aggressive primary brain tumor characterized by a heterogeneous cell population that is genetically unstable and resistant to chemotherapy. Indeed, despite advances in medicine, patients diagnosed with GBM have a median survival of just one year. Magnetic Resonance Imaging (MRI) is the most widely used imaging technique for determining the location and size of brain tumors. Indisputably, this technique plays a major role in the diagnosis, treatment planning, and prognosis of GBM. Therefore, this study proposes a new Case Based Reasoning approach to problem solving that attempts to predict a patient’s GBM volume after five months of treatment based on features extracted from MR images and patient attributes such as age, gender, and type of treatment.