Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory disease of the lungs that obstructs airflow from the lungs. Symptoms include difficulty breathing, coughing, mucus production, and wheezing. The study was conducted with 769 patients. A total of 48.70% were women, and 51.29% were men. The average age of the patients enrolled was 60 years. The research included 67 variables considering the medical history and biochemical data. The objective is to evaluate chronic obstructive disease. We used automatic learning techniques to assess and identify the patient's determinant variables. The following classifiers were used: Vector Support Machines (SVM), K-Nearest Neighbors (kNN), Decision Tree, Random Forest, Neural Network, AdaBoost, and Logistic Regression. The model suggests that the determining variables for COPD in treated patients are the following: TA_dist, Cholesterol level, LDL levels, Dyslipidemia, Bradycardia, Venous Insufficiency, Systolic Dysfunction, Cardiac Arrhythmia, Vasomotor Headache, Smoking, and Esophageal Achalasia. Therefore, they are considered relevant in the decision-making process for choosing treatment or prevention. The analysis of the relationship between the presence of the variables and the classifiers used to measure COPD revealed that the Logistic Regression classifier, with the variables TA_dist, Cholesterol level, LDL levels, Dyslipidemia Bradycardia, Venous Insufficiency, Systolic Dysfunction, Cardiac Arrhythmia, Vasomotor Headache, Smoking, and Esophageal Achalasia, showed an accuracy of 0.90, precision 0.87 and an F1 score of 0.89. Therefore, we can conclude that the Logistic Regression classifier gives the best results for evaluating the determining variables for COPD assessment.

Information theory is concerned with the study of transmission, processing, extraction, and utilization of information. In its most abstract form, information is conceived as a means of resolving uncertainty. Shannon and Weaver (1949) were among the first to develop a conceptual framework for information theory. One of the key assumptions of the model is that uncertainty increases linearly with the amount of complexity (in bit units) of information transmitted or generated (C.E. Shannon, W. Weaver. The mathematical Theory of communication, University of Illinois, Urbana III, 1949). A whole body of data from the cognitive neurosciences has shown since that the time of human response or action increases in a similar fashion as a function of information complexity in various different situations and contexts. In this paper, I will discuss what is currently known about the limitations of human information processing. The implications for the development of parsimonious computational models in science and the idea of reliable Artificial Intelligence for science and society will be made clear under the light of arguments from the cognitive neurosciences and computational philosophy. The goal of the presentation is to carve out a conceptual framework that is to inspire future studies on the problems identified.

In this work, we study a wireless communication system designed to handle multi users, each communicating with their own frequency bands. We analyse the performance of the system - its information capacity – while considering different number of users, and different configurations for the multipath propagation scenarios for each user and their base frequency. Our interest is to discover worst- (blind spots) and best-case scenarios (maximal information transmission). Our results show that a prior effective choice of parameters, such as the natural frequency of the chaotic signal, lead to significant throughput improvement and, in some cases, even enable an error-free communication. It is often to be expected that physical constrains in the channel may block higher-frequency signals used in standard non-chaotic communication systems. For our chaos-based communication system, we show that a significant wireless channel constraint, the multipath propagation, becomes less disruptive, the higher the frequency of the user. That is a win-win result. Not only does the higher frequency allow more information to be transmitted, but it also prevents multipath interference. Moreover, there is an ideal relationship between the time of propagation for the signals with the user natural frequency that results in a communication with no interference due to multipath, thus effectively creating a wireless communication system with similar gains as that obtained for wired communication where multipath propagation is not a significant issue. As an application of our system, we study a sensor network configuration where sensors have the same frequency and show that given a particular set of time propagation for the signal to leave the sensor and to arrive in the receiving station, we can determine an optimal natural frequency for the sensors to maximise information transmission. Alternatively, the contrary is also true, and that is that optimal natural frequencies can be deduced, given the time propagation configurations. Some optimal spatial configurations with no interference due to multipath will be deduced from this analysis.

Open Government Data (OGD) is catalyzed with advances in electronic government, digital technologies, and citizen’s sense of ownership over the last decade. Earlier OGD studies has applied different theoretical bases to explore organization’s behavior to adopt or implement OGD but has also produced uneven results. This study combines various theoretical bases and conducts weight and meta-analysis in OGD research. Further, an interconnected model of existing factors impacting OGD adoption or implementation is portrayed by synthesizing empirical results in OGD studies. Inconsistencies in the level of effect and significance among several factors within empirical OGD studies at organizational level (represented by decision-makers) provide the basis for conducting a weight and meta-analysis. Therefore, the main purpose of this study is to carry out weight and meta-analysis of all factors for confirming their overall influence on adoption or implementation of OGD. The results of this study suggest that organizational capacity, external pressures, and organizational readiness are found to be significant factors of adoption behavior. While implementation intention, organizational arrangement, technical capacity, and organizational awareness are the significant factors of implementation behavior of OGD. The recommendations drawn from this research would help to decide if and when to use such antecedents for predicting adoption and implementation of OGD.

As we look around, we observe objects and their inter-relations embedded in 4.pi steradian space; consider relations and processes as objects too. We especially note that the description of objects is constructed of information. We further notice the apparent realism of the information, it is undeniable, concrete, and non-probabilistic regardless of whether or not the objects exist. Since information does not interact physically, yet undeniably apparent, it must have a non-falsifiable existence in reality. The causal function in nature must be directly responsible for this reality. The regularity in causal function of physical systems dictates that a resultant state S of a system must causally depend on the reality of precursor states of interacting systems within limits of causal function. Therefore, S intrinsically must correlate with the information of relation among precursor states, which is expressible quantitatively as -- disjunction of conjunctions of values (semantics) of states within limits of reality that may result in S (Singh 2018). For the same causal dependence, S must also correlate with this expression on the values of correlation of respective states in each configuration. The second level correlation is organizable in modular hierarchy to give rise to arbitrary structured and abstract semantics. Conjunction creates a configuration within limits of specificity, whereas disjunction of such conjunctions gives rise to abstraction of structure and relation. Hence, we have a quantitative mechanism of information processing in addition to its ontological basis. If the brain can process semantic values of information, there must exist a formal mechanism.