Roxana Zaricell Bautista López

This article establishes a non-parametric validation framework for photoplethysmography (PPG) signals intended for heart rate monitoring, formulated as a methodological proof-of-concept study. The study applies the two-sample Kolmogorov–Smirnov test as a robust and versatile method for comparing the distributions of PPG signals. By integrating the two-sample Kolmogorov–Smirnov test into the validation process of cardiac pulse measurement devices, the work demonstrates its effectiveness in enhancing the accuracy and reliability of biomedical signal analysis. Results show that, when comparing signals against calibrated reference devices and visualizing cumulative distribution functions, the two-sample Kolmogorov–Smirnov test is capable of detecting subtle differences in signal behavior. This innovative use of the two-sample Kolmogorov–Smirnov test provides valuable insights for the design and validation of biomedical signal processing systems and contributes to the advancement of non-invasive health monitoring technologies. The proposed framework is evaluated under controlled experimental conditions using data acquired from a single subject. Consequently, the results should be interpreted as a methodological validation of the proposed statistical approach rather than as a definitive clinical validation, and are intended to demonstrate feasibility and analytical relevance rather than population-level generalizability.

Photoplethysmography is a widely studied noninvasive optical technique with great potential for application in clinical medicine. There is evidence that the PPG signal can provide information about heart rate, oxygen saturation in arterial blood, respiratory rate among other cardiovascular signs. The two-wavelength photoplethysmographic signal is generally used to determine oxygen saturation in arterial blood under the principle that oxygenated and deoxygenated hemoglobin differentially absorb red and near-infrared wavelengths. In this work it is proposed to use the same wavelengths that are used in the pulse oximetry technique (660 nm and 940 nm) to calculate heart rate; Heart rate recovery is done by analyzing the data using the synchronous demodulation technique. The sampled data was generated from measurement carried out on two people. The heart rate obtained with the pulse oximetry technique in conjunction with the synchronous demodulation technique was corroborated with the heart rate obtained using the PPG signal at a single wavelength (940 nm).

The inclusion of process variables in mixture experimental design is crucial for optimizing final products with precision. Unlike standard response surface designs, which are limited by the requirement that proportions must sum to 100%, mixture-process experiments enable a thorough evaluation of how operational factors, such as particle size and mixing time, interact with mixture components. This approach enhances the understanding of how processing conditions affect product properties and leads to more accurate predictive models, thereby improving production consistency and reliability. Regression analysis reveals that interactions between PET and both particle size and mixing time significantly impact the response variable. The model demonstrates strong predictive accuracy, with R-squared and adjusted R-squared values of 92% and 86%, respectively, and a low root mean square error (S) of 0.2818. The PRESS value of 3.38 confirms the model’s ability to accurately predict new data. The absence of high multicollinearity, as indicated by variance inflation factor (VIF) values below 5, further supports the model's stability and interpretability. Contour plots illustrate the effect of varying mixture proportions on the response, such as tensile strength, showing a positive impact of both particle size and mixing time. The highest tensile strength is achieved at maximum levels of these variables, indicating a synergistic effect. Response variable optimization identifies the optimal mixture composition as 90% PET, 10% wood, and no coupling agent. To maximize tensile strength, the largest particle size and longest mixing time should be used, though extrapolating beyond the studied parameters should be done with caution.

A mixture experiment is one where the response depends only on the relative proportions of the ingredients present in the mixture. Different regression models are used to analyze mixture experiments, such as the Scheffé model, the Slack Variable model, and models with inverse terms. Models with inverse terms are worthy of consideration in certain applications. These models have been analyzed considering their fit quality, but not their numerical stability. This article analyzes the numerical stability of the model with inverse terms and the use of pseudo components. Likewise, a criterion is defined for the selection of the regression model with inverse terms, based on the quality of fit and numerical stability.

Wood–plastic composites (WPCs) are materials that combine a polymeric phase with a filler, typically wood powder or fiber. This common practice significantly enhances certain mechanical properties of WPCs, such as tensile, compressive, and flexural strength, especially when coupling agents are added. Polyethylene terephthalate (PET) is a thermoplastic commonly used due to its high stiffness, mechanical strength, and chemical resistance. However, the increasing demand for PET has led to a rise in plastic waste accumulation, posing an environmental challenge. Therefore, recycling and promoting a circular economy are considered key strategies to reduce environmental impact and achieve sustainable development. The main challenge in WPC production lies in its formulation—specifically, determining the appropriate proportion of thermoplastic polymer, wood filler, and coupling agent. To optimize the mechanical properties of WPCs, it is necessary to vary these component proportions and understand how these changes affect the composite properties. This article describes a statistical approach for optimizing the mechanical properties of a WPC with PET as the polymer matrix. It is assumed that the mechanical properties of the WPC are a function of the proportions of the components that make up the mixture, so the optimization consists of determining these proportions in a way that maximizes the mechanical properties. With the experimental design, three models with inverse terms were fitted, which modeled the relationship between the component proportions and the flexural, compression, and tensile strength. The optimal formulation for maximizing flexural strength was found to be 58% PET, 39% wood, and 3% E‐GMA. The optimal formulation for maximizing compressive strength was found to be 58% PET, 40% wood, and 2% E‐GMA. The optimal formulation for maximizing tensile strength was found to be 86% PET, 11% wood, and 3% E‐GMA. The results are validated through analysis of variance of the mixture experimental design for mixtures. Finally, three response surface plots are presented for each mechanical property, which graphically describe the relationship between the different proportions of the mixture components and the mechanical properties.

The abalone, a gastropod mollusk belonging to the Haliotidae family, has been a subject of research with a focus on its behavior in response to various environmental factors, including temperature fluctuations and hypoxia. While existing studies have primarily explored abalone behavior in relation to environmental changes, there remains a significant amount to be discovered regarding their behavioral patterns. There are studies in which recordings of readings have been made (indirect methods) of the heart rate of the abalone since it is believed that it is one of the parameters affected due to the changes in temperature and dissolved oxygen in its environment. The main disadvantage of some procedures to measure heart rate in abalone is that an incision is made near the heart, through this incision the measurement is made using the photoplethysmography technique. Another parameter that is affected by these changes (temperature and hypoxia) is respiratory rate. Temperature is an important factor that can reduce oxygen solubility and thus alter abalone respiration; so in this work, we propose to use the technique of photoplethysmography: a method involving the measurement of changes in volume within a biological organ, which offers a unique opportunity to monitor these changes. We adapt this technique to measure gill movements in abalone, a critical indicator of respiratory activity in these marine mollusks. By utilizing a pulse oximetry setup and synchronous detection, we establish an innovative approach to gill movement analysis. This approach employs a pulse train and synchronous demodulation technique, the train of pulses is guided through an optical fiber to one of the respiratory orifices of the abalone to effectively detect and analyze the gill movements of abalone. Additionally, the role of frequency of its gill movements is highlighted as another critical variable of interest within the broader study of Haliotis fulgens, shedding light on the multifaceted nature of abalone behavior and responses to their surroundings. This research contributes to a deeper understanding of abalone physiology and behavior, offering insights into their adaptation to changing environmental conditions.

In this article we compared the Kronecker model against the Intercept model for a mixture experiment. We investigated conditioning using the variance inflation factor, the maximum and minimum eigenvalues of the information matrix, and the conditional number, to assess conditioning. The pseudocomponents transformation are also discussed. Practical examples are provided to support the conclusions. Recommendations regarding when to use the Kronecker and Intercept modeling approaches are provided. Conditioning was assessed in both adjusted models using the variance inflation factor and condition number. Evidence was given that, for the Intercept model, the choice of which component is replaced for the constant term is crucial in the sense on numerical stability.

The progressive climate change that the planet is going through is affecting the structure and functionality of the marine ecosystem, causing increases in temperature, deoxygenation and acidification. Consequently, this has affected the health of many marine species, in particular the population of the blue abalone Haliotis fulgens (Philippi, 1845), which is one of the species of the rocky reefs of the western coast of the Baja California Peninsula, for the which there are reports of effects due to environmental changes such as detachment of organisms from their substrates, and even mortality events. Therefore, the objective of the present work is to monitor the oxygen levels in the blood of abalone. To monitor the oxygen saturation in your blood, a sensor was designed that is suitable for working with biological samples under the principles of pulse measurement combined with photoplethysmography.