Study on the generalized formulations with the aim to reproduce the viscoelastic dynamic behavior of polymers

Appropriate modelling of the real behavior of viscoelastic materials is of fundamental importance for correct studies and analyses of structures and components where such materials are employed. In this paper, the potential to employ a generalized Maxwell model and the relative fraction derivative model is studied with the aim to reproduce the experimental behavior of viscoelastic materials. For both models, the advantage of using the pole-zero formulation is demonstrated and a specifically constrained identification procedure to obtain the optimum parameters set is illustrated. Particular emphasis is given on the ability of the models to adequately fit the experimental data with a minimum number of parameters, addressing the possible computational issues. The question arises about the minimum number of experimental data necessary to estimate the material behavior in a wide frequency range, demonstrating that accurate results can be obtained by knowing only the data of the upper and low frequency plateaus plus the ones at the loss tangent peak

Development of advanced algorithms based on physical thermal models for the estimation of tyre dynamic behaviour by smart tyre devices data

The theme of this work is part of the broad context of the industrial research development described by the challenges of the Horizon 2020 society, with particular reference to the "Digital Agenda, Smart Communities, Intelligent Mobility Systems" line. It is inserted in the following areas: • Intelligent urban mobility systems for logistics and people; • Safety systems of the urban environment, environmental monitoring and prevention of critical or risk events; • Embedded electronic systems, intelligent sensor networks, internet of things. In particular, the huge technological progress achieved in past few years has contributed to give a great drive towards the development of advanced safety and monitoring systems for automotive applications. In a context of great revolution in the transport system that the inhabitants of the planet are preparing to live, the primary targets are the raising of road safety standards and the creation of a vehicular network for the sharing of traffic information and the dynamic state of the car park. The automotive sector is experiencing a phase of profound change, fuelled by the progressive establishment of embedded systems oriented to the well-known concept of smart and sustainable mobility, with the target set by autonomous driving systems on the horizon. The object of the study are the innovative tyres called "smart tyres", characterized by a structure equipped with integrated sensors useful for acquiring information on the vehicle and on the road. The smart tyre is an element of great innovation as it allows to increase the level of interaction between vehicle and driver and allows to observe the phenomena of tyre-road interaction. In fact, the availability of a measuring element located in the contact area, provides numerous points of use, both in science and industry such as monitoring of the tread wear level, evaluation of the friction level, monitoring of user behaviour and manoeuvres (aimed at studying traffic dynamics), etc. These models allow great progress both in order to increase performance, but above all to safeguard the safety of passengers and pedestrians. Thanks to the continuous monitoring offered by a sensorized car park, it is possible to have information about, for example, soil roughness mapping (useful in the planning of maintenance interventions), the management of emergency assistance in the event of accidents, the supply of intelligent traffic light networks, the offer of insurance rates based on the most frequent itineraries and driving style. The development of physical and real-time algorithms would provide the additional level of predictability that such systems need in view of large-scale deployment. The development of real-time simulation models related to the tyre / road interaction, the identification of the vehicle subsystems with which the smart tyres are appropriate to interact, as well as the evaluation of these interactions are aimed at optimizing the dynamic behaviour of the vehicle. The integration of multiple subsystems will represent one of the added values providing important insights regarding the communication methodologies between physical systems and the related virtual representations. The traditional approaches SIL (software-in-the-loop) and HIL (hardware-in-the-loop) will evolve in the direction of a scenario that foresees the human being as a central element in the testing and validation chain, arriving at configurations of the DIL type (driver-in the-loop). In perspective it is also expected that the driver can be replaced by an autonomous control system

A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider

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

An analysis on stress field distribution of a deformable rubber specimen due to indentation

Knowledge of adherence phenomena in tire/road interaction is a key factor in the automotive field for safety and performance studies. In particular, many developments are focused on innovative tire structures and compounds, able to minimize braking distances, to preserve vehicle stability in panic situations and to guarantee optimal road holding on wet/icy surfaces. During road tire contact, two friction mechanisms prevail i.e. the adhesive and hysterical contribution. The complex tire/road interaction, as concerns macro roughness scale, is usually studied considering a rigid body (asphalt) which indents with a deformable body (rubber). The friction force deforming hysteresis component is a function of the time stress-field variation in the deformable body, due to indentation. The main aim of this study is to find out a formulation of the stress field in the rubber overcoming the limits of the one taken into account in the Gr.e.t.a. Model (Grip Estimation for Tire Analysis), based on the Kuznetsov–Gorokhovsky formulation. This approach shows evident limits being a bi-dimensional theory applied to a tri-dimensional domain. In this paper, an analysis on the stress field distribution of a deformable rubber parallelepiped specimen due to indentation has been conducted using the Hamilton formulation for stresses calculation. This theory refers to a deformable infinite half-space and it has been applied to a finite domain. To find out the minimum domain dimensions a finite element model has been developed, carrying out simulation under analogous working conditions, in no-sliding condition. The results show a good correlation between the numerical and analytical model in an opportunely dimensioned finite domain for the analyzed working condition

A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider

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

Basophil activation test for staphylococcus enterotoxins in severe asthmatic patients

Recent studies suggest that IgE to Staphylococcus aureus (SA) enterotoxins represent a risk factor for severe asthma even in asthmatic patients considered non atopic. SA enterotoxins can stimulate specific IgE responses but, acting as superantigens, they can promote a polyclonal IgE response, airway inflammation, and bronchial hyperresponsiveness. In comparison with the measurement of serum specific IgE, Basophil Activation Test (BAT) can give more relevant results because it measures only functional IgE capable of activating basophils. BAT for SA enterotoxins has never been performed until to now.METHODS:We recruited 35 patients with severe asthma treated according to GINA guidelines. They were tested for skin prick test to common aeroallergens. Total and specific IgE to SA enterotoxins (ImmunoCAP) were measured. Nasal swabs and sputum cultures were obtained. Basophil activation tests (BAT) using CD203c expression was done after stimulation with different concentrations of enterotoxins A, B, and toxic shock syndrome toxin (Sigma).RESULTSBAT for at least one of SA enterotoxin was positive in 13 among 35 severe asthmatic patients (37%), higher in non atopic than in atopic asthmatic patients (41% vs 33%). Specific IgE to SA enterotoxins were detected in 19 patients ( 54%). No relationship was observed between SA nasal colonization and the presence of IgE or the positivity of basophil activation test for SA enterotoxins.CONCLUSIONS:In this study we demonstrate the involvement of specific IgE mechanisms in severe asthmatic patients sensitised to staphylococcus enterotoxins. The potential benefit of anti-IgE therapy in this subgroup of severe asthmatic patients has to be investigated

Primary surgical cytoreduction in advanced ovarian cancer: An outcome analysis within the MITO (Multicentre Italian Trials in Ovarian Cancer and Gynecologic Malignancies) Group

Objective To draw a reliable picture of the surgical management of advanced ovarian cancer (AOC) within the MITO Group, trying to correlate the disease extent at presentation, the category of center, and surgical outcome. Methods Three tertiary referral centers for gynecologic oncology and four non-oncologic referral gynecologic surgical centers, participated in the project. A questionnaire was adopted to register perioperative data on AOCs (FIGO Stage IIIC-IV) consecutively operated on for a period of 12 months. Results A total of 205 patients were registered into the study: 140 and 65 were recruited in oncological referral centers and non-referral centers, respectively. Following a multivariate analysis, the Eisenkop score and the category of center resulted the most potent predictors of complete surgical cytoreduction followed by PCI, preoperative CA125, and ASA score. Complete surgical cytoreduction was associated with oncological referral centers (60% vs 24.6%, p < 0.001). The proportion of patients undergoing additional surgical procedures was significantly different comparing the two categories of centers (at least one additional procedure was performed in 81.4% vs 50.8% in oncological referral centers compared to the others, p < 0.001). Despite the more aggressive surgery performed in oncological referral centers, the perioperative outcome measures were not significantly different in the two groups. Conclusions The chance of obtaining a complete cytoreduction mainly depends on patient characteristics, tumor spread, and quality of treatment. The latter is amenable for direct influence, and therefore, seems to be of utmost importance when considering efforts aiming at improvement in the outcome of this disease

Anti-cancer activity of grape seed semi-polar extracts in human mesothelioma cell lines

Malignant mesothelioma is a tumor that affects pleural surface and has very poor prognosis. The standard therapeutic modalities for this cancer have yielded unsatisfactory outcomes, therefore the development of alternative and effective therapies is currently an urgent requirement. Grapevine is a plant rich of bioactive compounds, known for its therapeutic effects. Here, we describe the anti-cancer activity of grape seeds semi-polar extracts of two Italian grape varieties (Aglianico and Falanghina) in mesothelioma in vitro. Seed extracts from both varieties induced intrinsic apoptosis in a dose and time-dependent manner in three different human mesothelioma cell lines. Global metabolic analysis of this fraction revealed a higher accumulation of phenylpropanoid precursors and proanthocyanidins and expression of genes involved in the aforementioned pathways. These findings suggest that new phenolic molecules from grape seeds could be viewed as new drugs to be used alone or in combinations with standard chemotherapeutics in mesothelioma treatment

Aglianico Grape Seed Semi-Polar Extract Exerts Anticancer Effects by Modulating MDM2 Expression and Metabolic Pathways

Grapevine (Vitis vinifera L.) seeds are rich in polyphenols including proanthocyanidins, molecules with a variety of biological effects including anticancer action. We have previously reported that the grape seed semi-polar extract of Aglianico cultivar (AGS) was able to induce apoptosis and decrease cancer properties in different mesothelioma cell lines. Concomitantly, this extract resulted in enriched oligomeric proanthocyanidins which might be involved in determining the anticancer activity. Through transcriptomic and metabolomic analyses, we investigated in detail the anticancer pathway induced by AGS. Transcriptomics analysis and functional annotation allowed the identification of the relevant causative genes involved in the apoptotic induction following AGS treatment. Subsequent biological validation strengthened the hypothesis that MDM2 could be the molecular target of AGS and that it could act in both a p53-dependent and independent manner. Finally, AGS significantly inhibited tumor progression in a xenograft mouse model of mesothelioma, confirming also in vivo that MDM2 could act as molecular player responsible for the AGS antitumor effect. Our findings indicated that AGS, exerting a pro-apoptotic effect by hindering MDM2 pathway, could represent a novel source of anticancer molecules