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PurposeIn this paper, the authors adopt the theory of the circular economy to study the transitions that take place in three case studies in Mexico and Ecuador. The work is aimed to systematize a circular economy assessment tool that fosters opportunities for improvement in business practices.Design/methodology/approachThe methodology is based on a descriptive quantitative analysis, where a checklist is made with 91 items and nine study variables. This is from the study of the bibliography and business practice. Furthermore, the neural network method is used in a case study to predict the level of circular economy and the importance of each variable according to the sensitivity by the Lek’s profile method.FindingsIt is based on a descriptive quantitative analysis, where a checklist with 91 items and nine study variables is made, defined from a bibliographic study and business practice. Furthermore, the neural network method is used in a case study to predict the level of circular economy and the importance of each variable based on sensitivity.Research limitations/implicationsThe application of the tool requires prior knowledge of the circular economy approach, which is why specialized personnel are needed for its application. This makes research more expensive in time and human resources.Practical implicationsThe practical and methodological contribution of this work lies in the feasibility of the tool that favors the definition of improvement actions for the implementation contribution to the circular economy in business practices.Social implicationsThe social contribution is framed in the gradual transition to circular economy approaches in underdeveloped countries.Originality/valueThe use of the neural network method to predict the level of circular economy in a case study allows making decisions in a predictive way. This encourages the development of the circular economy according to the context needs.

Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion...

Background: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology. Objective: In principle, we can perform a hand-on checking (Manual Curation). However, this is a hard task due to the high number of combinations of pairs of nodes (possible metabolic reactions). Method: In this work, we used Combinatorial, Perturbation Theory, and Machine Learning, techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis’ group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms. Results: The CPTML linear model obtained using LDA algorithm is able to discriminate nodes (metabolites) with correct assignation of reactions from not correct nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series. Conclusion: Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens a door to the study of MRNs of multiple organisms using PTML models.

Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0.83 to 0.90. Various aspects related to the comparison of modelling techniques, applicability domain and descriptor interpretations were discussed. Finally, consensus predictions of these models were used for screening HDAC2 inhibitors from four chemical libraries whose bioactivities against HDAC1, HDAC3, HDAC6 and HDAC8 have been known. According to the results of virtual screening assays, structures of some hits with pair-isoform-selective activity (between HDAC2 and other HDACs) were revealed. This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.

El crecimiento de la población, la industrialización, las prácticas agrícolas y la urbanización aumentan la demanda de agua y, por lo tanto, la cantidad de aguas residuales generadas. El propósito de este documento es determinar los principales impactos ambientales de la construcción de la planta de tratamiento de aguas residuales del subsistema Pindo Chico en la ciudad del Puyo, Ecuador. Se recolectó información de línea base, mediante la implementación de listas de chequeo, y para la identificación y evaluación de impactos se confeccionó un diagrama de redes y se emplearon estructuras matriciales de doble entrada basadas en el sistema de Leopold original. Se analizaron siete actividades del proyecto y 12 componentes ambientales, y se determinaron 24 interacciones, de las cuales 11 fueron categorizadas como impactos ambientales significativos y dos muy significativos. El componente más afectado es la alteración del suelo, debido a la remoción de la cobertura vegetal, apertura de zanjas, creación de vías de acceso y construcción de la planta. Finalmente, se propusieron medidas correctivas en el plan de manejo ambiental para mitigar, prevenir y monitorear los impactos negativos que se generan.

In this report are used two data sets involving the main antidiabetic enzyme targets α‐amylase and α‐glucosidase. The prediction of α‐amylase and α‐glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α‐amylase and 1546 compounds in the case of α‐glucosidase are selected to develop the tree model. In the case of CT‐J48 have the better classification model performances for both targets with values above 80%–90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10‐fold cross‐validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double‐target approach for increasing the estimation of antidiabetic chemicals identification aimed by double‐way workflow in virtual screening pipelines.