Improvement of manufacturing technologies through a modelling approach: an air-steam sterilization case-study

Abstract A milestone of Industry 4.0 is the improvement of the design procedures requiring models of complex processes. Models can be used to simulate the process, being accurate even if complex, and to predict process behaviour for control action, requiring simplicity and stability. In the last years, machine learning approaches came up alongside of the standard identification techniques for prediction purposes. In this work we propose two models of an industrial autoclave to describe the evolution of temperature and pressure. The first model (PhM) involves a physical structure with data-driven adaptation of the parameters, the second one is a Long Short-Term Memory network (LSTM), trained ensuring Input-to-State stability. Both models obtained good performance: FIT of 94.26% (91.55%) for the temperature (pressure) with PhM; 84.59% (78.31 %) for the temperature (pressure) with the LSTM. Future developments involve the synthesis of an MPC based on the LSTM to be tested in simulation via PhM

Rheological characterisation of cold bitumen emulsion slurries

The performance of cold bitumen emulsion (CBE) mixtures is strongly linked to an optimised design of the binder blends and mastics. Types and dosages of bitumen, mineral additions and the workability must be characterised and optimised. This study aims at providing an approach for the fundamental characterisation of CBE materials using rotational viscometry. Firstly, a procedure for measuring the viscosity of CBE slurries using the Brookfield viscometer was investigated by comparing results obtained by using a traditional spindle geometry and a novel impeller engineered to avoid phase separation: the dual helical ribbon (DHR). Afterwards, the effect of mineral additions and bitumen emulsions types was measured and modelled, also considering the influence of their concentration. The Krieger-Dougherty model proved to be a powerful tool to fit results and provide fundamental parameters for improved CBE materials engineering characterisation. Overall, the DHR was found a promising tool for CBE slurries rheological characterisation

Antibiotic Resistance Profiling, Analysis of Virulence Aspects and Molecular Genotyping of Staphylococcus aureus Isolated in Sicily, Italy

Staphylococcus aureus is the major cause of foodborne diseases worldwide. In this retrospective study, 84 S. aureus strains were characterized. The collection comprises 78 strains isolated during 1998 and 2014 from dairy products and tissue samples from livestock bred for dairy production in Sicily. One isolate was obtained from a pet (dog), one from an exotic animal (a circus elephant), and four human isolates were obtained during a severe food poisoning outbreak that occurred in Sicily in 2015. All the strains were characterized by pulsed-field gel electrophoresis (PFGE), for antibiotic resistance and presence of toxin genes. PFGE results showed 10 different pulsotypes, with three relatively frequent and three unique. The antibiotic resistance profiling showed that penicillin G (35.7%) and tetracycline (20.2%) resistance is largely spread. Most isolates contained at least one toxin gene making them a potential threat for public health. Enterotoxin sec gene was observed in 28.6% and seg in 23.8% of the strains, respectively; the human isolates were the only ones to concurrently harbor both seg and sei genes. In addition, 24 isolates were randomly selected and analyzed by multilocus sequence typing. Interestingly, the analysis showed the presence of 12 sequence types (STs), of which 6 were novel. One of them, ST700, was detected in 29% of the isolates and was found to be spread throughout Sicily. ST700 has been present in the island for almost 16 years (1998-2014) and it shows no host preference since it was isolated from different ruminant species. Four human isolates shared both the pulsotype (PT10) and the sequence type (ST9), as well as the virulence genes (seg-sei); this observation suggests that the isolates originated from a single clone, although they were obtained from two different individuals

IGF-I induces upregulation of DDR1 collagen receptor in breast cancer cells by suppressing MIR-199a-5p through the PI3K/AKT pathway.

Discoidin Domain Receptor 1 (DDR1) is a collagen receptor tyrosine-kinase that contributes to epithelial-to-mesenchymal transition and enhances cancer progression. Our previous data indicate that, in breast cancer cells, DDR1 interacts with IGF-1R and positively modulates IGF-1R expression and biological responses, suggesting that the DDR1-IGF-IR cross-talk may play an important role in cancer.In this study, we set out to evaluate whether IGF-I stimulation may affect DDR1 expression. Indeed, in breast cancer cells (MCF-7 and MDA-MB-231) IGF-I induced significant increase of DDR1 protein expression, in a time and dose dependent manner. However, we did not observe parallel changes in DDR1 mRNA. DDR1 upregulation required the activation of the PI3K/AKT pathway while the ERK1/2, the p70/mTOR and the PKC pathways were not involved. Moreover, we observed that DDR1 protein upregulation was induced by translational mechanisms involving miR-199a-5p suppression through PI3K/AKT activation. This effect was confirmed by both IGF-II produced by cancer-associated fibroblasts from human breast cancer and by stable transfection of breast cancer cells with a human IGF-II expression construct. Transfection with a constitutively active form of AKT was sufficient to decrease miR-199a-5p and upregulate DDR1. Accordingly, IGF-I-induced DDR1 upregulation was inhibited by transfection with pre-miR-199a-5p, which also impaired AKT activation and cell migration and proliferation in response to IGF-I.These results demonstrate that, in breast cancer cells, a novel pathway involving AKT/miR-199a-5p/DDR1 plays a role in modulating IGFs biological responses. Therefore, this signaling pathway may represent an important target for breast cancers with over-activation of the IGF-IR axis

Guidelines for the implementation of SMARTI: Sustainable Multifunctional Automated Resilient Transport Infrastructure

The World's transport infrastructures (TI) network is facing fast changes due to population growth, mobility, business trades and globalization. More challenges are coming from unforeseen natural and human-induced hazards, including climate change's effects. Meanwhile, technology development continues apace, and new solutions from multi-disciplinary sectors could help solve the main challenges faced by the TI industry. This work presents “SMARTI”, a vision that aims at engineering and implementing concepts such as Sustainability, Multifunctionality, Automation and Resilience within the design, construction and management of TI. As a result, the paper provides roadmaps for each of the above-mentioned pillars, identifying aims, current practices and stepping stones that infrastructure managers, policymakers and governors should consider toward more sustainable TI within 2030

A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications

The "Investigating and translating genomic evidence for public health response to SARS-CoV-2 (INSIDE SARS-CoV-2)" project - Network of excellence. Commentary

The “Investigating and translating genomic evidence for public health response to SARS- CoV-2 (INSIDE SARS-CoV-2)” project is part of the initiative “Joint science and tech- nology cooperation call for joint project proposals for the years 2021-2023” promoted by the Italian Ministry of Foreign Affairs and International Cooperation (MAECI) and the Republic of India. To start the project activities, the pandemic response and the epidemiological situation in Italy and in India, together with the genomic surveillance strategies for SARS-CoV-2 virus in the two countries, are here described