This paper describes the implementation of a comprehensive clinical decision support system (CDSS) for the risk factors prediction of comorbidities related to obesity and for the characterization of indirect connections between such comorbidities and non-communicable diseases. In particular, the direct correlation between obesity, diabetes, cardiovascular, and heart disease is analyzed by using machine learning (ML) predictive models, while the connection of the co-occurring disorders to the numerous additional non-communicable diseases is analyzed via a graph-based user interface. The CDSS here proposed is, therefore, structured with three main components: ML predictive models based on publicly available datasets, explainable artificial intelligence (XAI) local and global model interpretation, and graph-based representation of non-communicable disease connections. Multiple ML models are presented for risk assessment and a comparison is carried out based on performance key performance indicators. The best-performing model for each disease was proved to be: the multi-layer perceptron for diabetes and heart disease, and extreme gradient boosting for cardiovascular disease. Comorbidities risk factor prediction and a XAI local model explanation is performed on significant case studies. In addition, XAI global model interpretation is given for the entire dataset providing insights on the features’ contribution to the models’ implementation. Moreover, the graph-based visualization of indirect disease co-occurrence is performed by filtering connections according to different relative risk factor thresholds. This interface can be exploited by healthcare professionals to obtain, according to the needs and the clinical approach, a global perspective on obesity and its associated pathologies prevention as well as long-term treatment and care provision.

Ionic polymer metal composites (IPMCs) are electroactive materials made of ionic polymer thin membranes with platinum metallization on their surfaces. They are interesting materials due to not only their electromechanical applications as transducers but also to their electrochemical features and the relationship between the ionic/solvent current and the potential field. Their electrochemical properties thus suggest the possibility for exploiting them as compact fractional-order elements (FOEs) with a view of defining fabrication processes and production strategies that assure the desired performances. In this paper, the experimental electrical characterization of a brand new IPMC setup in a fixed sandwich configuration is proposed. Two IPMC devices with different platinum absorption times (5 h and 20 h) are characterized through experimental data: first, a preliminary linearity study is performed for a fixed input voltage amplitude in order to determine the frequency region where IPMC can be approximated as linear; then, a frequency analysis is carried out in order to identify a coherent fractional-order dynamics in the bode diagrams. Such analyses take the first steps towards a simplified model of IPMC as a compact electronic FOE for which the fractional exponent value depends on fabrication parameters as the absorption time.

The possibilities envisioned by the characterization of microfluidic systems cover a wide range of both scientific demands and industrial requirements, from life sciences to fine chemistry, from food quality to other microbiology applications. In these fields, the development of analytical methods and technological solutions oriented to the creation of a firm and structured link between models and experimentation on microfluidic systems, opens up the way for the study and characterization of microfluidic devices and phenomena from a point of view related to system and control theory.

A new approach for the measurement of red blood cell velocity at the level of microcirculation has been developed and characterized. The new real-time and automated measurement system is based on the dual-slit methodology, and blood flow information is extracted from images and transduced into two analog photometric signals and then processed using a hybrid analog-digital system that performs the cross correlation of the signals in real time. The characterization of the system consists of a calibration with a known velocity target, yielding to the hyperbolic calibration curve velocity versus delay and the determination of the velocity detectable range from 0.3 to 120 mm/s. A theoretical study of the measurement uncertainty and parametric studies were carried out to test the system robustness to changes of parameters and to determine the optimal configuration that is applicable to various experimental conditions. The system was further tested in in vivo experiments in the rat cremaster preparation in different types of vessels and flow velocities to verify the consistency of the results, as compared with those from conventional measuring systems. In addition, the dynamic behavior of the system and its response to changes in the measured velocity were studied through a continuous velocity record that was obtained during an experimental procedure.