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Air pollution is an issue of great concern globally due to the risks to the health of humanity, animals, and ecosystems. On the one hand, air quality monitoring systems allow for determining the concentration level of air pollutants and health risks through an air quality index (AQI). On the other hand, accurate future predictions of air pollutant concentration levels can provide valuable information for data-driven decision-making to reduce health risks from short- and long-term exposure when indicators exceed permissible limits. In this paper, five deep learning architectures are evaluated to predict the concentration of particulate matter pollutants (in their fractions PM2.5 and PM10) and carbon monoxide (CO) in consecutive hours. The proposed prediction models are based on recurrent neural networks (RNNs), long short-term memory (LSTM), vanilla LSTM, Stacked LSTM, Bi-LSTM, and encoder–decoder LSTM networks. Moreover, a methodology is presented to guide the construction of the prediction model, encompassing raw data processing, model design and optimization, and neural network training, testing, and evaluation. The results underscore the precision and reliability of the Stacked LSTM model in predicting the hourly concentration level for PM2.5, with an RMSE of 3.4538 μg/m3. Similarly, the encoder–decoder LSTM model accurately predicts the concentration level for PM10 and CO, with an RMSE of 3.2606 μg/m3 and 2.1510 ppm, respectively. These evaluations, with their minimal differences in error metrics and coefficient of determination, validate the effectiveness and superiority of the deep learning models over other reference models, instilling confidence in their potential. [ABSTRACT FROM AUTHOR]

Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments. [ABSTRACT FROM AUTHOR]

Domain ontologies facilitate the organization, sharing, and reuse of domain knowledge. The construction of ontologies from text deals with the extraction of concepts and relations from text collection. A huge challenge is the learning of more expressive ontologies which includes relations such as disjointness or equivalence between classes. In our work, we exploit a text collection written in English with occurrence of named entities giving information about individuals in an specific domain knowledge for generating axioms. The co-ocurrence in texts of named entities is exploited to establish axiomatic relations between classes such as subClassOf, disjointWith, and equivalentClass; as well as between individuals as sameIndividualAs and differentFrom by using the instanceOf relation. For this purpose, a Named Entity Recognition tool was used for the identification of instanceOf relation, the linguistic context where the classes co-occur was extracted for the identification of axioms. The experiments were made on a text collection about the tourist domain. The results show that named entities provide good evidence for the identification of axiomatic relations. [ABSTRACT FROM PUBLISHER]

The incursion of low-cost sensors (LCS) for monitoring particulate matter in different fractions of particles (PM 10 , PM 2.5 , and PM 1 ) allows the characterization of the concentration levels of specific sources or events, including the analysis of ultrafine fractions (PM 1 ). Several studies have documented adverse effects on human health due to exposure to PM 1 , 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 1 is imperative. LCS is an alternative to understanding contaminant concentration levels by considering spatial and temporal community dynamics by monitoring critical zones. Furthermore, collecting and managing large amounts of data through automatic processing and analysis generates information to support decision-making to reduce exposure and risks to people's health. The dataset presents the concentration level of PM 1 (µg/m 3 ) calculated from the particles of size 0.03 µm, 0.05 µm, and 1.0 µm recorded and counted by the sensor in a sample per minute for 24 h for seven continuous days. The values of the meteorological factors of relative humidity, temperature, and heat index complement these attributes. The dataset comprises records collected (in the same period) at four particulate matter monitoring stations, which compose an LCS network supported by Internet of Things (IoT) technologies. The data collection points were located in different areas of Reynosa, Mexico, considering strategic places for monitoring environmental pollution, such as industrial parks, residential areas, avenues with high vehicular traffic and transportation of heavy cargo, and an airport., Competing Interests: 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., (© 2024 The Author(s).)

We propose to couple the R 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 R 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. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four well-known MOEAs. Additionally, we validate our proposal in many-objective optimization problems. In these problems, our approach showed its main strength, since it could outperform another well-known indicator-based MOEA.