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This paper describes a study of operational parameters by using the multivariate data analysis and neural networks for a municipal waste incinerator located in Majorca (Spain). The basis of the study also includes the chemometric techniques: linear multivariate regression to develop a model with certain predictive capabilities; linear principal component analysis, which allow the number of variables to be reduced from 17 to 4, thus fostering visualization in a low-dimension space; and linear discriminant analysis to categorize plant data according to the month (probability ≈ 70%). Neural network predictive capability was good, with relative errors around 6-8%. These techniques allow all the variables to be analysed simultaneously and focus on the variables which have a significant impact. In this way, the interrelationships between sets of variables, causal relations among input/output variables, seasonal motivated deviations as well as observation variations have been identified. [ABSTRACT FROM AUTHOR]

Background: Acute Lymphoblastic Leukemia (ALL) is the most prevalent neoplasia in children and teenagers in Mexico. Although epidemiological data supports that children's residence close to emissions from vehicular traffic or industrial processes increases the risk of ALL; and the IARC states that benzene, PAHs, and PM 2.5 are well-known environmental carcinogens, there is a gap in linking these carcinogenic hazards with the sources and their distribution from scenario perspective., Aim: To identify ALL clusters in the population under 19 years of age and characterize the environment at the neighborhood level by integrating information on sources of carcinogenic exposure using spatial analysis techniques in the Metropolitan Area of San Luis Potosi, Mexico., Methods: Using the Kernel Density test, we designed an ecological study to identify ALL clusters from incident cases in the population under 19 years of age. A multicriteria analysis was conducted to characterize the risk at the community level from carcinogenic sources. A hierarchical cluster analysis was performed to characterize risk at the individual level based on carcinogenic source count within 1 km for each ALL case., Results: Eight clusters of carcinogenic sources were located within the five identified ALL clusters. The multicriteria analysis showed high-risk areas (by density of carcinogenic source) within ALL clusters., Conclusions: This study has a limited source and amount of available data on ALL cases, so selection bias is present as well as the inability to rule out residual confounding factors, since covariates were not included. However, in this study, children living in environments with high vehicular density, gas stations, brick kilns, incinerators, commercial establishments burning biomass, or near industrial zones may be at higher risk for ALL., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Data-driven diabetes research has increased its interest in exploring the heterogeneity of the disease, aiming to support in the development of more specific prognoses and treatments within the so-called precision medicine. Recently, one of these studies found five diabetes subgroups with varying risks of complications and treatment responses. Here, we tackle the development and assessment of different models for classifying Type 2 Diabetes (T2DM) subtypes through machine learning approaches, with the aim of providing a performance comparison and new insights on the matter., Methods: We developed a three-stage methodology starting with the preprocessing of public databases NHANES (USA) and ENSANUT (Mexico) to construct a dataset with N = 10,077 adult diabetes patient records. We used N = 2,768 records for training/validation of models and left the remaining (N = 7,309) for testing. In the second stage, groups of observations -each one representing a T2DM subtype- were identified. We tested different clustering techniques and strategies and validated them by using internal and external clustering indices; obtaining two annotated datasets Dset A and Dset B. In the third stage, we developed different classification models assaying four algorithms, seven input-data schemes, and two validation settings on each annotated dataset. We also tested the obtained models using a majority-vote approach for classifying unseen patient records in the hold-out dataset., Results: From the independently obtained bootstrap validation for Dset A and Dset B, mean accuracies across all seven data schemes were [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]), respectively. Best accuracies were [Formula: see text] and [Formula: see text]. Both validation setting results were consistent. For the hold-out dataset, results were consonant with most of those obtained in the literature in terms of class proportions., Conclusion: The development of machine learning systems for the classification of diabetes subtypes constitutes an important task to support physicians for fast and timely decision-making. We expect to deploy this methodology in a data analysis platform to conduct studies for identifying T2DM subtypes in patient records from hospitals., (© 2023. BioMed Central Ltd., part of Springer Nature.)

The data produced by sensors of IoT devices are becoming keystones for organizations to conduct critical decision-making processes. However, delivering information to these processes in real-time represents two challenges for the organizations: the first one is achieving a constant dataflow from IoT to the cloud and the second one is enabling decision-making processes to retrieve data from dataflows in real-time. This paper presents a cloud-based Web of Things method for creating digital twins of IoT devices (named sentinels ).The novelty of the proposed approach is that sentinels create an abstract window for decision-making processes to: (a) find data (e.g., properties, events, and data from sensors of IoT devices) or (b) invoke functions (e.g., actions and tasks) from physical devices ( PD ), as well as from virtual devices ( VD ). In this approach, the applications and services of decision-making processes deal with sentinels instead of managing complex details associated with the PDs , VDs , and cloud computing infrastructures. A prototype based on the proposed method was implemented to conduct a case study based on a blockchain system for verifying contract violation in sensors used in product transportation logistics. The evaluation showed the effectiveness of sentinels enabling organizations to attain data from IoT sensors and the dataflows used by decision-making processes to convert these data into useful information.

Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system's feasibility, where the participants show a positive interest in its functionalities.

The most common machine-learning methods solve supervised and unsupervised problems based on datasets where the problem's features belong to a numerical space. However, many problems often include data where numerical and categorical data coexist, which represents a challenge to manage them. To transform categorical data into a numeric form, preprocessing tasks are compulsory. Methods such as one-hot and feature-hashing have been the most widely used encoding approaches at the expense of a significant increase in the dimensionality of the dataset. This effect introduces unexpected challenges to deal with the overabundance of variables and/or noisy data. In this regard, in this paper we propose a novel encoding approach that maps mixed-type data into an information space using Shannon's Theory to model the amount of information contained in the original data. We evaluated our proposal with ten mixed-type datasets from the UCI repository and two datasets representing real-world problems obtaining promising results. For demonstrating the performance of our proposal, this was applied for preparing these datasets for classification, regression, and clustering tasks. We demonstrate that our encoding proposal is remarkably superior to one-hot and feature-hashing encoding in terms of memory efficiency. Our proposal can preserve the information conveyed by the original data.

The access to medical information (journals, blogs, web-pages, dictionaries, and texts) has been increased due to availability of many digital media. In particular, finding an appropriate structure that represents the information contained in texts is not a trivial task. One of the structures for modeling the knowledge are ontologies. An ontology refers to a conceptualization of a specific domain of knowledge. Ontologies are especially useful because they support the exchange and sharing of information as well as reasoning tasks. The usage of ontologies in medicine is mainly focussed in the representation and organization of medical terminologies. Ontology learning techniques have emerged as a set of techniques to get ontologies from unstructured information. This paper describes a new ontology learning approach that consists of a method for the acquisition of concepts and its corresponding taxonomic relations, where also axioms disjointWith and equivalentClass are learned from text without human intervention. The source of knowledge involves files about medical domain. Our approach is divided into two stages, the first part corresponds to discover hierarchical relations and the second part to the axiom extraction. Our automatic ontology learning approach shows better results compared against previous work, giving rise to more expressive ontologies.

We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization's performance in terms of metaheuristics' operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics' performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.

In this paper, we evaluate the performance of simulated annealing (SA) and the genetic algorithm (GA) when used for electroencephalographic (EEG) source localization. The performance is evaluated on the variance of the estimated localizations as a function of the optimization's initialization parameters and the signal-to-noise ratio (SNR). We use the concentrated likelihood function (CLF) as objective function and the Cramér-Rao bound (CRB) as a reference on the performance. The CRB sets the lower limit on the variance of our estimated values. Then, our simulations on realistic EEG data show that both SA and GA are highly sensitive to noise, but adjustments on their parameters for a fixed SNR value do not improve performance significantly. Our results also confirm that SA is more sensitive to noise and its performance may be affected by correlated sources.