A Digital Twin Based Self-Calibration Tool for Fault Prediction of FDM Additive Manufacturing Systems

Among the advantages of introducing digital twins on production systems, there is the ability to identify their eventual critical state and to enable predictive maintenance policies. The failure of a manufacturing process, in general, can be anticipated in phase of simulation, if tied up to wrong settings, or in phase of operation, if tied up to environmental variables. In both cases, knowing the conditions that could cause the failure allows to intervene in a timely and effective manner. Here a method is proposed to explore the system operating parameters in a systematic way: the system is able to process signals collected in real time by machine's sensors and to reproduce both the trajectories of the moving parts and the material deposition process. This also makes possible to predict manufacturing tolerances that will be obtained. On a FDM Cartesian 3D printer a self-calibration procedure is used to find the maximum torque that can be delivered by the drives at different speeds in an automatic and repeatable way to find the maximum speed and acceleration at which the machine can operate safely. Additional accelerometers were installed on the machine to validate the adopted procedure: tests results demonstrate the effectiveness of the system

Experimental multiphase estimation on a chip

Multiparameter estimation is a general problem that aims at measuring unknown physical quantities, obtaining high precision in the process. In this context, the adoption of quantum resources promises a substantial boost in the achievable performances with respect to the classical case. However, several open problems remain to be addressed in the multiparameter scenario. A crucial requirement is the identification of suitable platforms to develop and experimentally test novel efficient methodologies that can be employed in this general framework. We report the experimental implementation of a reconfigurable integrated multimode interferometer designed for the simultaneous estimation of two optical phases. We verify the high-fidelity operation of the implemented device, and demonstrate quantum-enhanced performances in two-phase estimation with respect to the best classical case, post-selected to the number of detected coincidences. This device can be employed to test general adaptive multiphase protocols due to its high reconfigurability level, and represents a powerful platform to investigate the multiparameter estimation scenario.Comment: 10+7 pages, 7+4 figure

An Experimental Study on Developing a Cognitive Model for Human Reliability Analysis

Serious incidents that occur inside or caused by industrial plants represent a very critical issue. In this context, the human reliability analysis (HRA) is an important tool to assess human factors that influence human behaviour in disasters scenario. In fact, the reliability assessment of interaction between human-machine systems is an important factor that affects the overall performance and safety in industrial plants. However, even though HRA techniques have been available for decades, there is not a universal method/procedure to reduce human errors that affect human performance. This study aims to design a novel approach to investigate the relationship between human reliability and operator performance considering the dependence on the available time to make decisions

The coherent dynamics of photoexcited green fluorescent proteins

The coherent dynamics of vibronic wave packets in the green fluorescent protein is reported. At room temperature the non-stationary dynamics following impulsive photoexcitation displays an oscillating optical transmissivity pattern with components at 67 fs (497 cm-1) and 59 fs (593 cm-1). Our results are complemented by ab initio calculations of the vibrational spectrum of the chromophore. This analysis shows the interplay between the dynamics of the aminoacidic structure and the electronic excitation in the primary optical events of green fluorescent proteins.Comment: accepted for publication in Physical Review Letter

Assessment of the effects of dietary vitamin D levels on olanzapine-induced metabolic side effects : focus on the endocannabinoidome-gut microbiome axis

Vitamin D deficiency is associated with poor mental health and dysmetabolism. Several metabolic abnormalities are associated with psychotic diseases, which can be compounded by atypical antipsychotics that induce weight gain and insulin resistance. These side-effects may be affected by vitamin D levels. The gut microbiota and endocannabinoidome (eCBome) are significant regulators of both metabolism and mental health, but their role in the development of atypical antipsychotic drug metabolic side-effects and their interaction with vitamin D status is unknown. We studied the effects of different combinations of vitamin D levels and atypical antipsychotic drug (olanzapine) exposure on whole-body metabolism and the eCBome-gut microbiota axis in female C57BL/6J mice under a high fat/high sucrose (HFHS) diet in an attempt to identify a link between the latter and the different metabolic outputs induced by the treatments. Olanzapine exerted a protective effect against diet-induced obesity and insulin resistance, largely independent of dietary vitamin D status. These changes were concomitant with olanzapine-mediated decreases in Trpv1 expression and increases in the levels of its agonists, including various N-acylethanolamines and 2-monoacylglycerols, which are consistent with the observed improvement in adiposity and metabolic status. Furthermore, while global gut bacteria community architecture was not altered by olanzapine, we identified changes in the relative abundances of various commensal bacterial families. Taken together, changes of eCBome and gut microbiota families under our experimental conditions might contribute to olanzapine and vitamin D-mediated inhibition of weight gain in mice on a HFHS diet

Use of Biochar-Based Cathodes and Increase in the Electron Flow by Pseudomonas aeruginosa to Improve Waste Treatment in Microbial Fuel Cells

In this paper, we tested the combined use of a biochar-based material at the cathode and of Pseudomonas aeruginosa strain in a single chamber, air cathode microbial fuel cells (MFCs) fed with a mix of shredded vegetable and phosphate buffer solution (PBS) in a 30% solid/liquid ratio. As a control system, we set up and tested MFCs provided with a composite cathode made up of a nickel mesh current collector, activated carbon and a single porous poly tetra fluoro ethylene (PTFE) diffusion layer. At the end of the experiments, we compared the performance of the two systems, in the presence and absence of P. aeruginosa, in terms of electric outputs. We also explored the potential reutilization of cathodes. Unlike composite material, biochar showed a life span of up to 3 cycles of 15 days each, with a pH of the feedstock kept in a range of neutrality. In order to relate the electric performance to the amount of solid substrates used as source of carbon and energy, besides of cathode surface, we referred power density (PD) and current density (CD) to kg of biomass used. The maximum outputs obtained when using the sole microflora were, on average, respectively 0.19 Wm−2kg−1 and 2.67 Wm−2kg−1 , with peaks of 0.32 Wm−2kg−1 and 4.87 Wm−2kg−1 of cathode surface and mass of treated biomass in MFCs with biochar and PTFE cathodes respectively. As to current outputs, the maximum values were 7.5 Am−2 kg−1 and 35.6 Am−2kg−1 in MFCs with biochar-based material and a composite cathode. If compared to the utilization of the sole acidogenic/acetogenic microflora in vegetable residues, we observed an increment of the power outputs of about 16.5 folds in both systems when we added P. aeruginosa to the shredded vegetables. Even though the MFCs with PTFE-cathode achieved the highest performance in terms of PD and CD, they underwent a fouling episode after about 10 days of operation, with a dramatic decrease in pH and both PD and CD. Our results confirm the potentialities of the utilization of biochar-based materials in waste treatment and bioenergy production

Lockdown: How the COVID-19 Pandemic Affected the Fishing Activities in the Adriatic Sea (Central Mediterranean Sea)

The coronavirus disease 2019 (COVID-19) has brought a global socio-economic crisis to almost all sectors including the fishery. To limit the infection, governments adopted several containment measures. In Italy, Croatia, and Slovenia, a lockdown period was imposed from March to May 2020, during which many activities, including restaurants had to close or limit their business. All of this caused a strong reduction in seafood requests and consequently, a decrease in fishing activities. The aim of this study is to investigate the effects of the COVID-19 in the Northern and Central Adriatic fleet, by comparing the fishing activities in three periods (before, during, and after the lockdown) of 2019 and 2020. The use of the Automatic Identification System(AIS) data allowed us to highlight the redistribution of the fishing grounds of the trawlers, mainly located near the coasts during the 2020 lockdown period, as well as a reduction of about 50% of fishing effort. This reduction resulted higher for the Chioggia trawlers (−80%) and, in terms of fishing effort decrease, the large bottom otter trawl was the fishing segment mainly affected by the COVID-19 event. Moreover, by analysing the landings of the Chioggia fleet and the Venice lagoon fleets, it was possible to point out a strong reduction both in landings and profits ranging from −30%, for the small-scale fishery operating at sea, to −85%, for the small bottom otter trawl

Concept Matching for Low-Resource Classification

We propose a model to tackle classification tasks in the presence of very little training data. To this aim, we approximate the notion of exact match with a theoretically sound mechanism that computes a probability of matching in the input space. Importantly, the model learns to focus on elements of the input that are relevant for the task at hand; by leveraging highlighted portions of the training data, an error boosting technique guides the learning process. In practice, it increases the error associated with relevant parts of the input by a given factor. Remarkable results on text classification tasks confirm the benefits of the proposed approach in both balanced and unbalanced cases, thus being of practical use when labeling new examples is expensive. In addition, by inspecting its weights, it is often possible to gather insights on what the model has learned

Profiles of Risky Driving Behaviors in Adolescent Drivers: A Cluster Analysis of a Representative Sample from Tuscany Region (Italy)

(1) Background: Research on patterns of risky driving behaviors (RDBs) in adolescents is scarce. This study aims to identify distinctive patterns of RDBs and to explore their characteristics in a representative sample of adolescents. (2) Methods: this is a cross-sectional study of a representative sample of Tuscany Region students aged 14–19 years (n = 2162). The prevalence of 11 RDBs was assessed and a cluster analysis was conducted to identify patterns of RDBs. ANOVA, post hoc pairwise comparisons and multivariate logistic regression models were used to characterize cluster membership. (3) Results: four distinct clusters of drivers were identified based on patterns of RDBs; in particular, two clusters—the Reckless Drivers (11.2%) and the Careless Drivers (21.5%)—showed high-risk patterns of engagement in RDBs. These high-risk clusters exhibited the weakest social bonds, the highest psychological distress, the most frequent participation in health compromising and risky behaviors, and the highest risk of a road traffic accident. (4) Conclusion: findings suggest that it is possible to identify typical profiles of RDBs in adolescents and that risky driving profiles are positively interrelated with other risky behaviors. This clustering suggests the need to develop multicomponent prevention strategies rather than addressing specific RDBs in isolation