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.

Reliable electricity supply in Colombia’s Non-Interconnected Zones requires sustainable and low-cost energy technologies. Vertical-axis hydrokinetic turbines are promising for this purpose; however, their relatively low power coefficient remains a major challenge. This study combines transient 2D and 3D $k$–$\omega$ SST computational fluid dynamics (CFD) simulations with hydraulic channel experiments to investigate a hybrid Darrieus–Savonius turbine. A 27-case Design of Experiments (DoE) based on 2D CFD was first applied to screen the effects of rotor radius ratio (RR), attachment angle (AA), and water velocity. Within the investigated design space, the configuration with $\mathrm{R}\mathrm{R}=0.5$ and $\mathrm{A}\mathrm{A}=0^{\circ }$ produced the most favorable average performance. The selected configuration was subsequently analyzed using 3D CFD and experimentally evaluated at TSR values of 1.0, 1.1, and 1.2. At TSR = 1.0, the 3D model predicted ${\mathrm{C}}_{\mathrm{P}}=0.1525$, closely matching the experimental value of 0.1541 with a relative error of 1.05%. The results demonstrate that 2D CFD is useful for computationally efficient parameter screening and qualitative trend identification, but it overpredicts absolute performance because it neglects blade tip vortices, spanwise flow, and volumetric wake interactions. Three-dimensional CFD is therefore required for reliable performance prediction and analysis of the complex flow structures governing hybrid hydrokinetic turbine behavior.

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.