Advances in informatics technologies have enabled new ways of attention in a health-care system. This kind of approach is commonly referred as a telemedicine. In this context, this work proposes an environment that facilitates the execution of a specific protocol of physical rehabilitation on a patient by a health care specialist through tele-operating technical aids. For this purpose, a service-oriented architecture was designed and implemented. In addition, considering the dynamics of the system oriented to occurrence of discrete events, formal modelling tool known as Coloured Petri Nets were used for validation and verification. Finally, numerous rehabilitation protocols were tested without presenting alterations with respect to the technical model.

Anaerobic codigestion of organic residues is a proven strategy for enhancing methane recovery. However, the complexity of microbial interactions and variability in operational conditions make it difficult to estimate methane concentration in real time, particularly in rural contexts. This study developed a multiple linear regression model to predict methane concentration using operational data and microbial community profiles derived from 16S rRNA gene sequencing. The system involved the codigestion of cassava by-product and pig manure in a two-phase anaerobic reactor. Predictor variables were selected through a hybrid approach combining statistical correlation with microbial functional relevance. The final model, trained on 70% of the dataset, demonstrated satisfactory generalization capability on the other 30 test set, achieving a coefficient of determination (R²) of 0.92 and a mean relative error (MRE) of 6.50%. Requiring only a limited set of inputs and minimal computational resources, the model offers a practical and accessible solution for estimating methane levels in decentralized systems. The integration of microbial community data represents a meaningful innovation, improving prediction by capturing biological variation not reflected in operational parameters alone. This approach can support local decision making and contribute to Sustainable Development Goal 7 by promoting reliable and affordable technologies for clean energy generation in rural and resource-constrained settings.

Accurate detection of spontaneous breathings (SBs) and respiratory asynchronies during mechanical ventilation (MV) is essential for optimizing patient care and preventing lung injuries. Conventional models often fail to capture these events with sufficient accuracy. To address this gap, this study introduces new equations incorporating custom shape functions and the Slice method, aiming to deliver a more robust, "bedside" model with potential applications in real-time asynchrony detection. Three new equations were developed to incorporate shape functions accounting for pressure- and volume-dependent changes in elastance, and a fourth model combined these shape functions with the Slice method. Retrospective data from 8 ICU patients (each providing 6 mins of ventilatory data) were split into two datasets of 4 patients each: one for model development and refinement, and the other for testing performance in reproducing ventilatory waveforms. Model accuracy was assessed using the coefficient of determination (R²) and Mean Residual Error (MRE). This evaluation focused on how effectively each model captured actual patient breathing mechanics, particularly in the presence of SBs or respiratory asynchronies. The proposed models, especially the one combining shape functions with the Slice method-Recruitment Distention Elastance Analysis + Slice (RDEA + Slice)-exhibited a strong correlation with patient data, evidenced by high R² values. While conventional models achieved R² coefficients between 0.25 and 0.87, the new models improved these to 0.90-0.97. The RDEA + Slice model attained significantly lower MRE values (0.012-0.032), underscoring its superior accuracy in capturing dynamic changes. Furthermore, a unique identifiability analysis confirmed that the model parameters can be reliably estimated, supporting its potential for clinical application. The new bedside models, especially RDEA + Slice, demonstrate promise in enhancing mechanical ventilation management. By accurately capturing ventilatory mechanics in presence of SBs, they hold potential to refine ventilator settings, reduce lung injury risks, and integrate with real-time diagnostic tools for detecting patient-ventilator asynchronies-ultimately supporting more personalized and effective ICU care.

Anaerobic codigestion of organic residues is a proven strategy for enhancing methane recovery. However, the complexity of microbial interactions and variability in operational conditions make it difficult to estimate methane concentration in real time, particularly in rural contexts. This study developed a multiple linear regression model to predict methane concentration using operational data and microbial community profiles derived from 16S rRNA gene sequencing. The system involved the codigestion of cassava by-product and pig manure in a two-phase anaerobic reactor. Predictor variables were selected through a hybrid approach combining statistical correlation with microbial functional relevance. The final model, trained on 70% of the dataset, demonstrated satisfactory generalization capability on the 30) test set, achieving a coefficient of determination (R²) of 0.92 and a mean absolute error (MRE) of 6.50%. Requiring only a limited set of inputs and minimal computational resources, the model offers a practical and accessible solution for estimating methane levels in decentralized systems. The integration of microbial community data represents a meaningful innovation, improving prediction by capturing biological variation not reflected in operational parameters alone. This approach can support local decision-making and contribute to Sustainable Development Goal 7 by promoting reliable and affordable technologies for clean energy generation in rural and resource-constrained settings.

Access to high-fidelity ventilatory waveform data remains a significant challenge in respiratory mechanics research, particularly in resource-constrained environments. This study introduces and validates a novel, accessible framework for acquiring ventilatory data by applying image processing techniques to ventilator screen recordings. The accuracy of the framework was evaluated, demonstrating high fidelity ( R 2 > 0.99) when compared with sensor-derived data from a laboratory emulator. Its reliability was further confirmed by demonstrating that image-derived patient data reproduced established correlations in respiratory mechanics previously obtained from sensor-based measurements. As a proof of concept for the utility of this validated framework, a novel predictive single compartment model was developed to estimate expiratory parameters from inspiratory phase data. By introducing two auxiliary parameters ( L and H ), this model established the first predictive relationship for expiratory airway resistance and reference pressure, addressing a key limitation of previous approaches. The predictive model performed consistently in sedated patients but as expected, its linear nature was unable to reproduce the complex dynamics of spontaneous breathing and asynchrony. Overall, this study establishes image processing as a reliable and accessible method for ventilatory data acquisition, warranting further validation across diverse technical conditions and patient cohorts. It demonstrates how this data can support new predictive tools and concludes that while linear models fail to capture asynchrony, this predictive failure itself shows promise as a potential non-invasive indicator for its detection.