In this work, black-box modeling techniques are applied to Bacterial Cellulose-based sensors to characterize their dynamic behavior. Several classes of linear and nonlinear models, including Finite Impulse Response, AutoRegressive with eXogenous Input, Nonlinear Finite Impulse Response, Nonlinear AutoRegressive with eXogenous Input, and Long Short-Term Memory networks, are developed and compared. The performance of each model is evaluated based on a one-step-ahead prediction and a ∞-step-ahead simulation using standard performance metrics such as Root Mean Square Error, Mean Absolute Error and the coefficient of determination. The results show the strengths and limitations of the different modeling approaches in capturing the dynamics of BC-based transducers. The ARX model showed the best results for the prediction in one step, but poor results were obtained when the prediction was considered in ∞ steps. The NFIR model is instead the best choice for long-term prediction.

Bacterial Cellulose (BC) functionalized with Ionic Liquids (ILs) is a promising candidate for sustainable electroactive sensors. While single-carrier transport models are well-established in piezoionic electroactive polymers, their applicability to BC-IL systems remains unverified. This study introduces a simplified 1D finite element model, significantly improving computational efficiency while preserving key physical insights. A detailed parametric analysis investigates the impact of different charge transport assumptions, revealing that single-carrier models are insufficient to fully describe the mechanoelectric transduction behavior. The results emphasize the necessity of a dual-carrier framework to accurately model BC-based transducers, offering a deeper understanding of multi-ionic interactions within the porous BC structure. By highlighting key mechanisms and limitations, this work provides a foundation for optimizing BC-IL sensors, preparing the way for more reliable and scalable bioelectronic applications.

This paper presents a novel Clinical Decision Support System based on eXplainable Artificial Intelligence (XAI-CDSS) as a comprehensive structured tool consisting of three main parts: predictive models, XAI interpretation, and a graph-based visualization of non-communicable pathologies. Machine learning models are proposed to predict the risk factors related to the direct association between obesity and comorbidities such as cardiovascular, heart disease, and diabetes. Multilayer perceptron and extreme gradient boosting are chosen among different machine learning algorithms as the best performing for the risk factors prediction of the selected comorbidities. They perform prediction with an accuracy of 0.72 for diabetes, and 0.73 for cardiovascular and heart disease. The intuitive XAI interface gives the end-user insight into the machine learning decision process, while the graph visualization links such co-occurrent pathologies to many other non-communicable diseases and provides a global view to healthcare professionals, usable for obesity and associated pathologies prevention and long-term treatment and care.

Magneto encephalographic (MEG) brain signals are studied using a method for characterizing nonlinear dynamics. This approach uses the value of dinfin(d-infinite) to characterize the system's asymptotic chaotic behavior. A novel procedure was developed to extract this parameter from time series when the system's structure and laws are unknown. The implementation of the algorithm has proven to be general and computationally efficient. The information characterized by this parameter is furthermore independent and complementary to the signal power since it considers signals normalized with respect to their amplitude. The algorithm implemented here is applied to whole-head 148 channel MEG data during two highly structured yogic breathing meditation techniques. Results, which are relative to spatio-temporal distributions of the calculated dinfin on the MEG channels, are analyzed and compared during different phases of the yogic protocol.