Conventional aquaculture (ACUA-C) in Mexico is an activity that maintains an annual growth rate of 6%. Tilapia (Oreochromis niloticus) aquaculture is the predominant aquaculture in the country with 4623 farms, most of which operate with semi-intensive (SIS) and extensive (ES) production systems, discharging untreated wastewater that negatively impacts the environment. To address this problem, new ecotechnologies such as biofloc (BFT), recirculation systems for aquaculture (RAS), constructed wetlands (CWs) for water treatment, and aquaponics (AS) have emerged to mitigate the environmental impacts of untreated wastewater. The objective of this work is to evaluate the feasibility of tilapia farming with BFT, RAS, CW, and AS ecotechnologies through an economic and financial feasibility analysis, considering their environmental and social implications in a Mexican aquaculture farm located in the state of Veracruz. Open interviews, direct observation, and diagnosis of the semi-intensive tilapia system was carried out between 15 May 2019 and 15 February 2020 in order to calculate, design, and theoretically evaluate the four ecotechnological alternatives (TBF, RAS, CW, and AS). Economic (13), environmental (10), and social (5) variables were applied, making use of the same facilities. The results reveal that the four ecotechnologies implemented in an SIS are economically viable and environmentally acceptable, especially in water management, in addition to being socially feasible. It is concluded that BFT offers an alternative to the producer by having a lower additional cost to improve problems, despite the possible complexity of operation and energy requirements to manage wastewater treatment. The technological transition to BFT is recommended to solve the problem of sustainable water management.

Objective: This study evaluates machine learning algorithms’ effectiveness in classifying Parkinson’s disease and Huntington’s disease based on biomarker data obtained non-invasively from patients and healthy controls. Methods: Datasets containing biomarker data ( x, y, and z values of accelerometers) from sensors were collected from Parkinson’s disease, Huntington’s disease patients, and healthy controls. An automatic selection model method was implemented for disease classification, using a unique Mexican database of human gait biomarkers, which we consider the only one of its kind. Random forest, random subspace method, and K-star algorithms were employed, with parameters optimized through an automated model selection. Results: The study achieved a 0.893 precision rate for Parkinson’s disease and Huntington’s disease using the random subspace method. The findings underscore the potential of machine learning techniques in medical diagnosis, particularly in neurological disorders. Conclusion: The automatic selection model method demonstrated efficacy in classifying Parkinson’s disease and Huntington’s disease based on non-invasive biomarker data. This research contributes to advancing non-invasive diagnostic approaches in neurological disorders, highlighting the significance of machine learning in healthcare.