The following document talks about the parameters of the maintenance system implemented in plastic injection machines of a maquiladora company located in Ciudad Juarez Chihuahua. The method used in this investigation is essentially ulti-criteria decision making, the technique for the order of preference for similarity to the ideal TOPSIS solution. Therefore, an evaluation of the maintenance system is carried out, using measurable collected in said company. With this evaluation the plastic injection machine is determined in better conditions, as well as the machine in worse conditions, which helps management to make better decisions regarding each machine. The objective of this research is to address the study of decision-making in the industrial field specifically in the maintenance system of plastic injection molding machines.

Multi-criteria decision-making methods (MCDM) have accelerated in recent years. Many authors have made remarkable manipulation of the uncertainty involved in MCDM problems as a constant to make the most appropriate decision among the alternatives proposed. For this reason, fuzzy logic has been integrated into the MCDM, achieving new hybrid methodologies. These have managed to increase decision-making capacity, allowing us to manipulate nonfuzzy (quantitative) and fuzzy (qualitative) information. The main of this paper presents the comparative analysis of the Pythagorean Fuzzy CODAS method in order to examine the advantages and differences in comparison to MOORA Pythagorean Fuzzy Set method. The analysis, in this sense, aims to present a new scenario for the selection of the best decision-making problem, taking its advantages and thus achieve a greater benefit in real problems applications. An illustrative case will be presented with the MOORA-PFS methodology, to see the results obtained with Pythagorean Fuzzy CODAS. Besides, this comparative study to academics to choose more operative methods for solve MCDM problems.

Functional hand rehabilitation requires adaptive and quantitative tools capable of addressing grip-specific challenges. This study presents Handly, an interactive virtual-reality (VR) platform that integrates surface electromyography (sEMG) and markerless optical tracking for the assessment and training of six functional grips (Card, Cylindrical, Spherical, Hook, Grain, Pencil). A total of 10 healthy adults (5 males, 5 females; 18–30 years) participated in a feasibility study involving virtual object manipulation tasks across three difficulty levels. Performance metrics included the execution time, success rate, and number of objects collected. An individualized calibration protocol based on root mean square (RMS) analysis was implemented, using standardized isometric contractions (125–147 N) measured with a handheld dynamometer to establish subject-specific dynamic thresholds. A repeated-measures ANOVA confirmed significant effects of grip type and task level on execution time ($p<0.001$, ${\eta }_p^2=0.68$) and success rate ($p<0.01$, ${\eta }_p^2=0.54$), whereas the number of objects collected showed no significant differences ($p=0.12$). These findings demonstrate that Handly can reliably discriminate functional differences in neuromotor performance while adapting to individual EMG activation profiles. Although EMG data were sampled at 100 Hz, this rate proved sufficient for amplitude-based event detection. The platform’s dual capacity for quantitative assessment and adaptive motor training supports its potential for personalized neurorehabilitation, pending clinical validation in populations with upper-limb impairments.