Injury severity prediction of cyclist crashes using random forests and random parameters logit models.

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network.

Vehicle dynamics control systems have a fundamental role in smart and autonomous mobility, where one of the most crucial aspects is the vehicle body velocity estimation. In this paper, the problem of a correct evaluation of the vehicle longitudinal velocity for dynamic control applications is approached using a neural networks technique employing a set of measured samples referring to signals usually available on-board, such as longitudinal and lateral acceleration, steering angle, yaw rate and linear wheel speed. Experiments were run on four professional driving circuits with very different characteristics, and the vehicle longitudinal velocity was estimated with different neural network training policies and validated through comparison with the measurements of the one acquired at the vehicle's center of gravity, provided by an optical Correvit sensor, which serves as the reference (and, therefore, exact) velocity values. The results obtained with the proposed methodology are in good agreement with the reference values in almost all tested conditions, covering both the linear and the nonlinear behavior of the car, proving that artificial neural networks can be efficiently employed onboard, thereby enriching the standard set of control and safety-related electronics.

Use of high-strength electromagnetic radiation to remove phototrophic biofilms from terracotta artifacts.

A novel technique, effective in eliminating biodeteriogens from biofilms encrusting terracotta artifacts, is presented here. This method is based on the use of high-strength electromagnetic radiation (EMR) in the radiofrequency band. Shards of terracotta from historical pots at the Botanical Garden of Naples, Italy, were used. The shards, after sterilization, were inoculated with several phototrophic microorganisms previously isolated from whole terracotta pots. The newly formed biofilms were exposed to EMR amplitude modulated by a train of rectangular pulses with Tr = 200 ns repetition time and 10% duty cycle. The exposure protocol consisted of three applications of 2 h each, every other day. Denaturing gradient gel electrophoresis analyses conducted on the newly formed biofilms showed that, after the first exposure to EMR, all species in the biofilms but one were still alive. The second exposure resulted in the disappearance of 9 out of 13 species that were initially present on the samples. After the third exposure, all species disappeared. Superficial layers of terracotta from the exposed samples, transferred to a culture medium at 24 °C for 72 h, did not show any re-growing of organisms. Petrographic analyses of the sampleswere carried out before and after the treatments; they showed that exposure to EMR did not cause structural alterations in the treated substrates. Moreover, the amplitude of the EMR that samples were exposed to was not high enough to cause any significant increase in the temperature of the substrates; that is, no thermal effect, which is the most relevant effect when matter or organisms containing water are exposed to EMR, was observed. Finally, the field strength of the EMR showed to be non-invasive for the artifacts and non-dangerous for operators and the environment as compared to other techniques adopted in the field of conservation of cultural heritage.

Exposure to 50 Hz electromagnetic field raises the levels of the anti-apoptotic protein BAG3 in melanoma cells.

The expression of the anti-apoptotic protein BAG3 is induced in several cell types by exposure to high temperature, oxidants, and other stressful agents. We investigated whether exposure to 50 Hz electromagnetic fields raised BAG3 levels in the human melanoma cell line M14, in vitro and in orthotopic xenografts. Exposure of cultured cells or xenografts for 6 h or 4 weeks, respectively, produced a significant (P < 0.01) increase in BAG3 protein amounts. Interestingly, at the same times, we could not detect any significant variation in the levels of HSP70/72 protein or cell apoptosis. These results confirm the stressful effect of exposure to ELF in human cells, by identifying BAG3 protein as a marker of ELF-induced stress. Furthermore, they suggest that BAG3 induction by ELF may contribute to melanoma cell survival and/or resistance to therapy.