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The knee is one of the joints most vulnerable to disease and injury, particularly in athletes and older adults. Surface temperature monitoring provides insights into the health of the analysed area, supporting early diagnosis and monitoring of conditions such as osteoarthritis and tendon injuries. This study presents an innovative approach that combines infrared thermography techniques with a Resnet 152 (DeepLabCut based) to detect and monitor temperature variations across specific knee regions during repeated sit-to-stand exercises. Thermal profiles are then analysed in relation to weight distribution data collected using a Wii Balance Board during the exercise. DeepLabCut was used to automate the selection of the region of interest (ROI) for temperature assessments, improving data accuracy compared to traditional time-consuming semi-automatic methods. This integrative approach enables precise and marker-free measurements, offering clinically relevant data that can aid in the diagnosis of knee pathologies, evaluation of the rehabilitation progress, and assessment of treatment effectiveness. The results emphasize the potential of combining thermography with DeepLabCut-driven data analysis to develop accessible, non-invasive tools for joint health monitoring or preventive diagnostics of pathologies.

This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using ’filtered’ estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model’s efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency.

The use of components obtained through the additive manufacturing (AM) technique has become increasingly widespread in recent years, playing a central role in industrial production, and in particular in some fields such as automotive, biomedical, aerospace and electronics. Among all AM techniques, FDM (Fused Deposition Modelling) represents the most used printing technique to produce polymeric and composite components, thanks to the flexible printing process, the low cost and the diversity of the materials adopted. The aim of the present work concerns the comparison between the mechanical properties of three plastic materials printed with the FDM technique (polylactic acid PLA, polyethylene terephthalate glycol-modified PETG and Acrylonitrile-butadiene-styrene ABS) using an Original Prusa i3 MK3S, by varying the raster angle between 0�, 45� and 90� degrees. Infrared Thermography has been adopted to monitor the temperature evolution during static tensile tests and to assess stress level that can initiate damage within the material. Failure analysis was performed to correlate the mechanical behaviour with the microstructural characteristics of the materials

This work aimed to develop an automatic new methodology based on establishing if a mechanical component, designed for a conventional propulsion system, is also suitable for hybrid electric propulsion. Change in propulsion system leads to different power delivery and vehicle dynamics, which will be reflected in different load conditions acting on the mechanical components. It has been shown that a workflow based on numerical simulations and experimental tests represents a valid approach for the evaluation of the cumulative fatigue damage of a mechanical component. In this work, the front half-shaft of a road car was analyzed. Starting from the acquisition of a speed profile and the definition of a reference vehicle, in terms of geometry and transmission, a numerical model, based on longitudinal vehicle dynamics, was developed for both conventional and hybrid electric transmission. After the validation of the model, the cumulative fatigue damage of the front half-shaft was evaluated. The new design methodology is agile and light; it has been dubbed “Smart Design”. The results show that changing propulsion led to greater fatigue damage, reducing the fatigue life component by 90%. Hence, it is necessary to redesign the mechanical component to make it also suitable for hybrid electric propulsion.

Thanks to the progress of research on thermoplastic materials, the properties of composite materials have improved considerably. The aim of this study is the evaluation of fatigue strength of glass-fibre-reinforced polypropylene composite (PPGF35) by applying both the Risitano Thermographic Method (RTM) and the new Static Thermographic Method (STM).

The calculation of manual tightening torque applied by clinicians plays a critical role in achieving optimal preload for dental implants. However, there is a research gap when it comes to understanding the specific calculus involved in this process. This study aims to address this gap by analyzing the bending and torsional moments during manual tightening torque application by physicians of various specialties and genders. Additionally, the rates of early complications associated with clinician-calculated preload will be evaluated. The findings of this study will contribute to enhancing the understanding of clinician-controlled preload and guide future practices for successful dental implant outcomes.

The aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign was performed in the wind tunnel, testing 16 helmets in two different configurations of neck stiffness. The dataset was collected within a convolutional neural network (CNN or ConvNet) of images, creating a ranking by identifying the best and the worst helmets. The results revealed that each helmet has unique aerodynamic characteristics. Depending on the ranking scale previously created, the aerodynamic comfort of each helmets can be classified within the scale.

A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques.

The aim of this paper is the development of a 7-DOF (Degrees Of Freedom) mathematical model of an IndyCar and the implementation of an Artificial Neural Network in order to predict the optimal setup parameters of the car, reducing time and costs for race teams. The mathematical model is created by using MATLABTM and Simulink software starting from a telemetry acquisition at the Houston circuit and is based on Vertical Vehicle Dynamic equations. The optimal setup parameters have been predicted through an Artificial Neural Network (ANN) by using the NFTOOL Toolbox of MATLABTM software. ANN is implemented in a Quarter Car model, firstly, in order to train the network and predict the parameters able to reduce tire deflection and suspension travel in the time domain and the resonance peaks amplitude in the frequency domain. Then, it is implemented in the 7-DOF model in order to predict the best setup parameters able to reduce body movements and the weight transfers of the car.

Thanks to the progress of research on thermoplastic materials, the properties of composite materials have improved considerably. The aim of this study is the evaluation of fatigue strength of glass-fibre-reinforced polypropylene composite (PPGF35) by applying both the Risitano Thermographic Method (RTM) and the new Static Thermographic Method (STM).

The aim of this work is to design a power split transmission for an urban street sweeper in order to reduce fuel consumption. The design process starts with the comparison between a hydrostatic and a hydromechanical power split transmission. Both transmissions have been tested through an acceleration test considering 30, 50, 70 and 100 percent of the rated engine power. The results of both models developed in the Simcenter AmesimTM environment show that the power split transmission presents a higher efficiency, which justifies the adoption of this type of transmission with respect to the hydrostatic system. Then, a pure mechanical gear is added to the base concept of the power split transmission. The mechanical gear is managed by a lockup clutch, which can be engaged during the working phase of the street sweeper, similar to an adaptive cruise control. In this case, both transmissions are tested through a regulated cycle, UNI-EN 151429-2, highlighting the advantage of using a pure mechanical branch. At the end, both transmissions are tested with a driving cycle acquired through an experimental setup consisting of a control unit, a GPS and a tablet for the monitoring of the speed profile. The results show that the adoption of a lockup clutch allows an increase in the system efficiency during the working phase, hence reducing the average fuel consumption during the mission test.

In recent years, thanks to the development of additive manufacturing techniques, pros-thetic surgery has reached increasingly cutting-edge levels, revolutionizing the clinical course of patients suffering from joint arthritis, rheumatoid arthritis, post-traumatic arthrosis, etc. This work aims to evaluate the best materials for prosthetic surgery in hip implants from a tribological and mechanical point of view by using a machine-learning algorithm coupling with multi-body modeling and Finite Element Method (FEM) simulations. The innovative aspect is represented by the use of machine learning for the creation of a humanoid model in a multibody software environment that aimed to evaluate the load and rotation condition at the hip joint. After the boundary conditions have been defined, a Finite Element (FE) model of the hip implant has been created. The material properties and the information on the tribological behavior of the material couplings under investigation have been obtained from literature studies. The wear process has been investigated through the implementation of the Archard’s wear law in the FE model. The results of the FE simulation show that the best wear behavior has been obtained by CoCr alloy/UHMWPE coupling with a volume loss due to a wear of 0.004 μm3 at the end of the simulation of ten sitting cycles. After the best pairs in terms of wear has been established, a topology optimization of the whole hip implant structure has been performed. The results show that, after the optimization process, it was possible to reduce implant mass making the implant 28.12% more lightweight with respect to the original one.

In this paper thermal analysis was applied to determine the “Critical Stress” of concrete, different from its ultimate strength, able to produce the first damage in the structures under compressive loads. The Critical Stress can be thought as the stress able to produce the beginning of fatigue rupture within the material. Several specimens of high strength concrete were tested in order to define the incipient crack phenomena, also in internal part of the specimen not accessible by direct inspections, with the aid of infrared thermography. A finite element analysis completes the study and compares, for the same static loading conditions, the stress state with the experimental thermographic images. The final results show as the coupling of normal compressive test and the acquisition of the thermal images can be a useful aid to estimate a security stress value, indeed the Critical Stress, before the Ultimate Serviceability Limit (SLU) of the structure, defined as the maximum load condition before its failure.

Hydraulic components are often subjected to sliding contacts under starved or mixed lubrication. The condition of starved lubrication occurs during the start-up phase of the hydraulic machines or at low working temperature, causing friction and wear of components such as the cylinder block or the valve plate. The aim of this paper was to evaluate the hydrophobicity and oleophilic behavior of lead/lead-free bronze coatings under different texture conditions obtained by varying the diameter and the density of the dimples. The wettability tests were performed using sessile drop tests with oil and water liquids. The dimple parameters were analyzed using confocal microscopy, while the XRF analyses were performed to evaluate the composition of the bronze coatings. Based on the wettability measurements using oil and water, it was possible to assess that the porous surface acted as oil reservoirs that could prolong the life of lubricating oil layer, and may have resulted in a superior wear resistance. Furthermore, a relevant hydrophobicity was highlighted, suggesting that the surface texturing promoted the water-repellent barrier action on the surface. The experimental results showed that the discrepancy in surface properties in oil and water was raised when using the lead bronze coating. These coupled oleophilic and hydrophobic behaviors could play a beneficial role in sustaining the durability of a lubricating oil layer under a condition of continuous water-droplet impact.

The purpose of this paper is to investigate the variation in the coefficient of friction (CoF) and also the wear in a lead bronze coating under different texture conditions. The tribological tests were performed using a tribometer with pin on disk configuration. Several kinds of textures, realised by a surface laser texturing, were tested by varying the diameter, depth, and density of the dimples under severe working conditions. The innovative aspect concerns the behaviour of the textured lead bronze coating and the lubrication conditions when the sample is subjected to extreme load conditions. Confocal microscopies and SEM (Scanning Electron Microscopy)/EDS (Energy Dispersive X-Ray Spectroscopy) analyses were performed to evaluate the texture behaviour and also the surface deterioration of the coating. The results show that the application of texture processing leads to an improvement in the tribological properties of the coating. By analysing separately the variation of the different geometric parameters of the dimples, it has been shown that the best results are obtained with a diameter of 50 μm, a density of 5%, and a depth of 5 μm.

The aim of this paper is to investigate the limits of the quasi-static, no-slip approach when modeling the activation of magnetorheological (MR) devices. A quasi-static model is implemented to define the hydraulic and magnetic characteristics of an MR damper prototype. Then, an FE (Finite Element) magnetic simulation activity is carried out to validate theoretical findings, and an optimization procedure is carried out to adjust nominal geometry to actual application. Furthermore, a prototype is realized re-using the maximum number of components that constitute the existing conventional shock absorber. Finally, experimental tests at bench stands are performed. The predictable results demonstrate that neglecting the transient slipping effects, the Force–Velocity performance of the device is correlated with the model findings only for low current intensities acting in the magnetic circuit.

Background: Repairing a restoration is a more advantageous and less invasive alternative to its total makeover. The aim of this study was to analyze the effects of chemical and mechanical surface treatments aimed at increasing the roughness of a supra-nano composite resin. Methods: 27 cylindrical blocks of microhybrid composite were made. The samples were randomly divided into nine groups (n = 3). The samples’ surface was treated differently per each group: acid etching (35% H3PO4, 30 s and 60 s), diamond bur milling, sandblasting and the combination of mechanical treatment and acid etching. The samples’ surface was observed by a scanning electron microscope (SEM) and a confocal microscope for observational study, and surface roughness (Ra) was recorded for quantitative analysis. Results: The images of the samples sandblasted with Al2O3 showed the greatest irregularity and the highest number of microcavities. The surfaces roughened by diamond bur showed evident parallel streaks and sporadic superficial microcavities. No significant roughness differences were recorded between other groups. The difference in roughness between the control group, diamond bur milled group and sandblasted group was statistically significant. (p < 0.01). Comparison between the diamond bur milled group and the sandblasted group was also significant (p < 0.01). Conclusion: According to our results, sandblasting is the best treatment to increase the surface roughness of a supra-nano composite.

This paper regards the rapid determination of fatigue limit by using thermal data analysis. Different approaches available in the literature to estimate the fatigue limit of cold-drawn AISI 304L bars are analyzed and compared, namely, temperature- and energy-based methods. Among the temperature-based approaches, the Risitano Method (RM) and the method based on material temperature evolution recorded during a static tensile test were analyzed. Regarding the energy-based approaches, the input mechanical energy density stored in the material per cycle (i.e., the area of the hysteresis loop), the heat energy dissipated by the material to the surroundings per cycle, and the “2nd-harmonic-based” methods were considered. It was found that for the material analyzed, all the considered energy-based approaches provided a very good engineering estimation of the material fatigue limit compared to a staircase test.

Partendo dalla osservazione che provini precedentemente danneggiati hanno un limite di fatica differente (vedi Miner) viene esaminato il comportamento di un acciaio soggetto a serie di carichi affaticanti al fine di verificare la conseguente risposta termica. L’esame, mediante analisi termica, delle prove di fatica su provini di acciaio C40 danneggiati a diverso grado, da interessanti indicazioni sul ruolo dei carichi applicati in relazione alla possibilità di produrre danno nel materiale. Viene messo in evidenza come la temperatura, conseguenza dell’energia consumata dal provino, possa essere elemento indicativo dello stato del materiale. Vengono riportate le curve di fatica e le curve di temperatura di provini lisci sottoposti a storie di carico diverse. Da esse si può evincere che mentre è valida la legge di linearità del danno, la curva definita secondo la regola del Miner-Manson, che tiene solo conto del numero di volte in cui la tensione supera il limite di fatica del materiale (provini lisci), invece, non sempre rispecchia il reale stato di danno. Questo specialmente quando si è in presenza di carichi prossimi al limite di fatica che, per la sequenza dell’applicazione, possono diventare carichi affaticanti.

Partendo dalla osservazione che provini precedentemente danneggiati hanno un limite di fatica differente (vedi Miner) viene esaminato il comportamento di un acciaio soggetto a serie di carichi affaticanti al fine di verificare la conseguente risposta termica. L’esame, mediante analisi termica, delle prove di fatica su provini di acciaio C40 danneggiati a diverso grado, da interessanti indicazioni sul ruolo dei carichi applicati in relazione alla possibilità di produrre danno nel materiale. Viene messo in evidenza come la temperatura, conseguenza dell’energia consumata dal provino, possa essere elemento indicativo dello stato del materiale. Vengono riportate le curve di fatica e le curve di temperatura di provini lisci sottoposti a storie di carico diverse. Da esse si può evincere che mentre è valida la legge di linearità del danno (?ini/Ni= cost), la curva definita secondo la regola del Miner-Manson (?ini/Ni=1), che tiene solo conto del numero di volte in cui la tensione supera il limite di fatica del materiale (provini lisci), invece, non sempre rispecchia il reale stato di danno. Questo specialmente quando si è in presenza di carichi prossimi al limite di fatica che, per la sequenza dell’applicazione, possono diventare carichi affaticanti.

Parkinson's disease (PD) is a progressive neurodegenerative disorder related to the loss or absence of dopaminergic neurons in the brain. These deficits result in slowness of movement, tremor, rigidity, and dysfunction of behaviour. These symptoms negatively influence the patient’s capability to carry out the daily oral hygiene manoeuvres. The aim of this work is to record the oral health condition of PD patients evaluated at the IRCSS Bonino-Puleio in Messina. The oral health of 45 consecutive PD patients (study group) with neurologic diagnosis based on United Kingdom Brain Bank Criteria has been compared with that of another 45 no PD patients of the same age (control group). The evaluation of the general oral condition was recorded underlining tooth loss, active periodontal disease, and presence of untreated caries. The frequency of untreated caries, periodontal diseases, and missing teeth of the study group was significantly higher than in control group. Based on the data results, clinicians should direct high attention to the oral hygiene of patients with PD, above all at the early stages of the caries or periodontal disease, in order to prevent serious evolution of those pathologic dental conditions that may finally result in the tooth extraction event.