Particulate matter 1µm (PM) dataset collected by low-cost sensors in residential and industrial areas at the neighborhood level.

The incursion of low-cost sensors (LCS) for monitoring particulate matter in different fractions of particles (PM, PM, and PM) allows the characterization of the concentration levels of specific sources or events, including the analysis of ultrafine fractions (PM). Several studies have documented adverse effects on human health due to exposure to PM, such as morbidity and mortality from respiratory, cardiovascular, and, in some cases, carcinogenic diseases. Hence, studying the concentration levels and the sources that cause PM is imperative.

Association of environmental and meteorological factors on the spread of COVID-19 in Victoria, Mexico, and air quality during the lockdown.

This study aims to analyze the correlation between environmental factors and confirmed cases of COVID-19 pandemic in Victoria, Mexico. The analysis is performed at the micro-level, filtering only confirmed cases of COVID-19 that are located near air quality monitoring stations, within an approximate coverage of 2.5 km, in order to identify a possible specific association between PM, PM, carbon monoxide (CO), relative humidity, temperature, absolute humidity, and total confirmed cases of COVID-19.

2-Based Multi/Many-Objective Particle Swarm Optimization.

We propose to couple the 2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the 2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature.

A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms.

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model.

An Approach for Learning Expressive Ontologies in Medical Domain.

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.

Process-oriented integration and coordination of healthcare services across organizational boundaries.

The paper presents a methodology that follows a top-down approach based on a Model-Driven Architecture for integrating and coordinating healthcare services through cross-organizational processes to enable organizations providing high quality healthcare services and continuous process improvements. The methodology provides a modeling language that enables organizations conceptualizing an integration agreement, and identifying and designing cross-organizational process models. These models are used for the automatic generation of: the private view of processes each organization should perform to fulfill its role in cross-organizational processes, and Colored Petri Net specifications to implement these processes.