Fabio Lo Savio

The detrimental influence of Wi-Fi and mobile phones on human health has been extensively studied, yet the multiscale spatial complexity of electromagnetic (EM) energy absorption in biological tissues is still poorly understood. This paper offers a computational investigation of radiofrequency electromagnetic field (RF-EMF) exposure in the male urogenital system using fractals and neural networks. The specific absorption rate (SAR) produced by a 2.4[ ]GHz antenna positioned in anatomically important locations is calculated using a realistic voxel-based anatomical model (GUSTAV) implemented in CST Microwave Studio. Fractal geometry is used to statistically examine the spatial distribution of absorbed EM radiation beyond traditional SAR evaluation, revealing scale-dependent variability linked to anatomical complexity. To describe multiscale energy localization in prostate, testicular, and penile tissues, fractal dimension metrics are taken from SAR maps. The nonlinear link between antenna position and fractal-informed SAR distributions is then modeled by a feedforward neural network. The findings show that SAR patterns clearly display fractal behavior, with higher fractal dimensions indicating more localized energy concentration and tissue heterogeneity. These results demonstrate a strong connection between fractal complexity, anatomical multiscale structure, and EM exposure, placing the suggested framework firmly within the field of fractal modeling of biomedical systems.

This paper aims to identify methods for optimizing the geometry of dental prosthesis preparation, through both an analytical study and a numerical investigation. Assuming a 10° inclined load with respect to the median sagittal plane, a double algorithm was developed that was capable of calculating the axial resistance area and the cusp resistance area (if present) of a prosthetic abutment as the main geometric parameters vary. In particular, a coupling model with a pseudo-elliptical base was proposed, which is better than the circular shape representing the real shape assumed by the abutment following the dental intervention. The model also allows the presence of a cusp. This model was first analyzed using FEA, also simulating the presence of coupling cement between the abutment and the crown, and then physically made with a 10:1 scale prototype. The convergence of the experimental results found with the numerical–theoretical studies is an indication of the validity of the proposed model. A key contribution of this research using the proposed algorithm allowed to demonstrate that a superior limit of 0.25 for the ratio (HR) between the heights of the cusp sulcus (h-hc) and the abutment (h) is required to achieve good stability of the coupling. The methodology developed in this study is applicable to a variety of teeth, making it versatile and highly adaptable for broader clinical applications.

Nowadays the coastal waters of the Mediterranean Sea are increasingly affected by pollution from aromatic hydrocarbons. Therefore, in order to implement damage minimization actions, a continuous monitoring system with rapid response is needed. The most effective local method for distinguishing aromatic hydrocarbons from salt water is UV-induced fluorescence. This paper describes the design and building of a UV-induced fluorescence sensor that, mounted on board a sensor-buoy, detects the presence or absence of the pollutant in any atmospheric condition. The sensor consists of a measuring chamber, in which the water, loaded by a submersible pump, maintains a constant level thanks to a small siphon. The detection system, consisting of a UVC-LED and a phototransistor that responds in the visible light band, is located in the upper part of the chamber. In vitro tests, performed by comparing the sensor results with those obtained from a spectrofluorometer, have shown good sensitivity, repeatability, and accuracy, despite the inexpensiveness and low energy consumption of the sensor. An important goal achieved by the design and building methodology is that the sensor is able to withstand harsh weather conditions, the corrosive action of salt water and biofouling.

Prognostic and health management technologies are increasingly important in many fields where reducing maintenance costs is critical. Non-destructive testing techniques and the Internet of Things (IoT) can help create accurate, two-sided digital models of specific monitored objects, enabling predictive analysis and avoiding risky situations. This study focuses on a particular application: monitoring an endodontic file during operation to develop a strategy to prevent breakage. To this end, the authors propose an innovative, non-invasive technique for early fault detection based on digital twins and infrared thermography measurements. They developed a digital twin of a NiTi alloy endodontic file that receives measurement data from the real world and generates the expected thermal map of the object under working conditions. By comparing this virtual image with the real one acquired by an IR camera, the authors were able to identify an anomalous trend and avoid breakage. The technique was calibrated and validated using both a professional IR camera and an innovative low-cost IR scanner previously developed by the authors. By using both devices, they could identify a critical condition at least 11 s before the file broke.