|
|
Alejo, L., Atkinson, J., & Lackner, S. (2020). Looking deeper – exploring hidden patterns in reactor data of N-removal systems through clustering analysis. Water Sci. Technol., 81(8), 1569–1577.
Abstract: In this work, clustering analysis of two partial nitritation-anammox (PN-A) moving bed biofilm reactors (MBBR) containing different types of carrier material was explored for the identification of patterns and operational conditions that may benefit process performance. The systems ran for two years under fluctuations of temperature and organic matter. Ex situ batch activity tests were performed every other week during the operation of these reactors. These datasets and the parameters, which were monitored online and in the laboratory, were combined and analyzed applying clustering analysis to identify non-obvious information regarding the performance of the systems. The initial results were consistent with the literature and from an operational perspective, which allowed the parameters to be explored further. The new information revealed that the oxidation reduction potential (ORP) and the anaerobic ammonium oxidizing bacteria (AnAOB) activity correlated well. ORP also dropped when the reactors were exposed to real wastewater (presence of organic matter). Moreover, operating conditions during nitrite accumulation were identified through clustering, and also revealed inhibition of anammox bacteria already at low nitrite concentrations.
|
|
|
|
Baler, R. V., Wijnhoven, I. B., del Valle, V. I., Giovanetti, C. M., & Vivanco, J. F. (2019). Microporosity Clustering Assessment in Calcium Phosphate Bioceramic Particles. Front. Bioeng. Biotechnol., 7(281), 7 pp.
Abstract: There has been an increase in the application of different biomaterials to repair hard tissues. Within these biomaterials, calcium phosphate (CaP) bioceramics are suitable candidates, since they can be biocompatible, biodegradable, osteoinductive, and osteoconductive. Moreover, during sintering, bioceramic materials are prone to form micropores and undergo changes in their surface topographical features, which influence cellular physiology and bone ingrowth. In this study, five geometrical properties from the surface of CaP bioceramic particles and their micropores were analyzed by data mining techniques, driven by the research question: what are the geometrical properties of individual micropores in a CaP bioceramic, and how do they relate to each other? The analysis not only shows that it is feasible to determine the existence of micropore clusters, but also to quantify their geometrical properties. As a result, these CaP bioceramic particles present three groups of micropore clusters distinctive by their geometrical properties. Consequently, this new methodological clustering assessment can be applied to advance the knowledge about CaP bioceramics and their role in bone tissue engineering.
|
|
|
|
Heredia, C., Moreno, S., & Yushimito, W. (2024). ODMeans: An R package for global and local cluster detection for Origin–Destination GPS data. SoftwareX, 26, 101732.
Abstract: The ODMeans R package implements the OD-Means model, a two-layer hierarchical clustering algorithm designed for extracting both global and local travel patterns from Origin–Destination Pairs (OD-Pairs). In contrast to existing models, OD-Means automates cluster determination and offers advantages such as smaller Within-Cluster Distance (WCD) and dual hierarchies. The package includes functions for applying the model and visualizing the results on maps. Using real taxi data from Santiago, Chile, we demonstrate the package’s capabilities, showcasing its flexibility and impact on understanding urban mobility patterns.
|
|
|
|
Moreno, S., Pereira, J., & Yushimito, W. (2020). A hybrid K-means and integer programming method for commercial territory design: a case study in meat distribution. Ann. Oper. Res., 286(1-2), 87–117.
Abstract: The objective of territorial design for a distribution company is the definition of geographic areas that group customers. These geographic areas, usually called districts or territories, should comply with operational rules while maximizing potential sales and minimizing incurred costs. Consequently, territorial design can be seen as a clustering problem in which clients are geographically grouped according to certain criteria which usually vary according to specific objectives and requirements (e.g. costs, delivery times, workload, number of clients, etc.). In this work, we provide a novel hybrid approach for territorial design by means of combining a K-means-based approach for clustering construction with an optimization framework. The K-means approach incorporates the novelty of using tour length approximation techniques to satisfy the conditions of a pork and poultry distributor based in the region of Valparaiso in Chile. The resulting method proves to be robust in the experiments performed, and the Valparaiso case study shows significant savings when compared to the original solution used by the company.
|
|
