Higher Institute on Territorial Systems for Innovation
PORTO Publications Open Repository TOrinoNot a member yet
91193 research outputs found
Sort by
A new test device for the study of metal wear in conditioned granular soil used in EPB shield tunneling
No full textThe wear phenomenon evaluation in EPB shield tunneling machines is not a simple issue, as a large number of parameters are involved, such as soil and tool material properties, soil conditioning and pressure in the bulk chamber. The evaluation of the influence of these parameters and predicting this influence is a complex task and the research has proposed different test procedures and approaches. In this paper a new procedure for testing wear of tools with an innovative concept and design is presented. The experimental results obtained using conventional steel and hard material tools, tested with natural and conditioned soils, are discussed. The outcomes show the feasibility of the proposed procedure and the quality of the measurements that can be obtained using the proposed wear tool shape
Thermodynamic considerations on the role of heat and mass transfer in biochemical causes of carcinogenesis
No full textCellular homoeostasis involves a continuous interaction between the cell and its microenvironment. As such, active and passive transport of ions, nutrients, molecules and water are the basis for biochemical-physical cell life. These transport phenomena change the internal and external ionic concentrations, and, as a consequence, the cell membrane's electric potential and the pH. In this paper we focus on the relationship between these ion transport-induced pH and membrane voltage changes to highlight their impact on carcinogenesis. The preliminary results suggest a critical role for Cl- in driving tumour transformation towards a more malignant phenotype
Analysis of a protected Loss Of Flow Accident (LOFA) in the ITER TF coil cooling circuit
Full text linkIn moving towards ITER operation, the detailed analysis of fault conditions for the magnets becomes of increasing importance, to verify that the magnet protection system can safely manage them without any damage to the magnets. A "protected" Loss of Flow Accident in the ITER Toroidal Field (TF) coils, de-tected by the coil flow meters and managed by the Central Inter-lock System, is investigated here using the validated thermal-hydraulic code 4C. We simulate the entire sequence of events that is foreseen to protect the magnet, aiming at verifying the impact on the magnet. The LOFA consequences are investigated in terms of both the temperature margin in the winding pack and of the needed re-cooling time, which will affect the availability of the machine. It turns out that, for an "accelerated" discharge (i.e., a linear ramp-down) of the magnet current lasting less than 30 min, no quench should occur, while the corresponding re-cooling time should not exceed 1h. During the transient, ~ 10% of the He mass in the coil is vented to the quench tank due to the opening of the safety valves, and requires re-cooling
Time series prediction and forecasting using Deep learning Architectures
No full textNature brings time series data everyday and everywhere, for example, weather data, physiological signals and biomedical signals, financial and business recordings. Predicting the future observations of a collected sequence of historical observations is called time series forecasting. Forecasts are essential, considering the fact that they guide decisions in many areas of scientific, industrial and economic activity such as in meteorology, telecommunication, finance, sales and stock exchange rates. A massive amount of research has already been carried out by researchers over many years for the development of models to improve the time series forecasting accuracy. The major aim of time series modelling is to scrupulously examine the past observation of time series and to develop an appropriate model which elucidate the inherent behaviour and pattern existing in time series. The behaviour and pattern related to various time series may possess different conventions and infact requires specific countermeasures for modelling. Consequently, retaining the neural networks to predict a set of time series of mysterious domain remains particularly challenging. Time series forecasting remains an arduous problem despite the fact that there is substantial improvement in machine learning approaches. This usually happens due to some factors like, different time series may have different flattering behaviour. In real world time series data, the discriminative patterns residing in the time series are often distorted by random noise and affected by high-frequency perturbations. The major aim of this thesis is to contribute to the study and expansion of time series prediction and multistep ahead forecasting method based on deep learning algorithms. Time series forecasting using deep learning models is still in infancy as compared to other research areas for time series forecasting.Variety of time series data has been considered in this research. We explored several deep learning architectures on the sequential data, such as Deep Belief Networks (DBNs), Stacked AutoEncoders (SAEs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). Moreover, we also proposed two different new methods based on muli-step ahead forecasting for time series data. The comparison with state of the art methods is also exhibited. The research work conducted in this thesis makes theoretical, methodological and empirical contributions to time series prediction and multi-step ahead forecasting by using Deep Learning Architectures
Characterization of an Additive Manufactured TiAl Alloy-Steel Joint Produced by Electron Beam Welding
No full textIn this work, the characterization of the assembly of a steel shaft into a γ-TiAl part for turbocharger application, obtained using Electron Beam Welding (EBW) technology with a Ni-based filler, was carried out. The Ti-48Al-2Nb-0.7Cr-0.3Si (at %) alloy part was produced by Electron Beam Melting (EBM). This additive manufacturing technology allows the production of a lightweight part with complex shapes. The replacement of Nickel-based superalloys with TiAl alloys in turbocharger automotive applications will lead to an improvement of the engine performance and a substantial reduction in fuel consumption and emission. The welding process allows a promising joint to be obtained, not affecting the TiAl microstructure. Nevertheless, it causes the formation of diffusive layers between the Ni-based filler and both steel and TiAl, with the latter side being characterized by a very complex microstructure, which was fully characterized in this paper by means of Scanning Electron Microscopy, Energy Dispersive X-ray Spectroscopy, and nanoindentation. The diffusive interface has a thickness of about 6 µm, and it is composed of several layers. Specifically, from the TiAl alloy side, we find a layer of Ti₃Al followed by Al₃NiTi₂ and AlNi₂Ti. Subsequently Ni becomes more predominant, with a first layer characterized by abundant carbide/boride precipitation, and a second layer characterized by Si-enrichment. Then, the chemical composition of the Ni-based filler is gradually reached
Data Reduction Techniques for Near Real-Time Decision Making in Fall Prediction Systems
No full textUnintentional falls are a frequent cause of hospitalization that mostly increases health service costs due to injuries. Fall prediction systems strive to reduce injuries and provide fast help to the users. Typically, such systems collect data continuously at a high speed through a device directly attached to the user. Whereas such systems are implemented in devices with limited resources, data volume is significantly important. In this chapter, a real-time data analyzer and reducer is proposed in order to manage the data volume of fall prediction systems
Mathematical methods for magnetic resonance based electric properties tomography
Full text linkMagnetic resonance-based electric properties tomography (MREPT) is a recent quantitative imaging technique that could provide useful additional information to the results of magnetic resonance imaging (MRI) examinations. Precisely, MREPT is a collective name that gathers all the techniques that elaborate the radiofrequency (RF) magnetic field B1 generated and measured by a MRI scanner in order to map the electric properties inside a human body. The range of uses of MREPT in clinical oncology, patient-specific treatment planning and MRI safety motivates the increasing scientific interest in its development. The main advantage of MREPT with respect to other techniques for electric properties imaging is the knowledge of the input field inside the examined body, which guarantees the possibility of achieving high-resolution. On the other hand, MREPT techniques rely on just the incomplete information that MRI scanners can measure of the RF magnetic field, typically limited to the transmit sensitivity B1+. In this thesis, the state of art is described in detail by analysing the whole bibliography of MREPT, started few years ago but already rich of contents. With reference to the advantages and drawbacks of each technique proposed for MREPT, the particular implementation based on the contrast source inversion method is selected as the most promising approach for MRI safety applications and is denoted by the symbol csiEPT. Motivated by this observation, a substantial part of the thesis is devoted to a thoroughly study of csiEPT. Precisely, a generalised framework based on a functional point of view is proposed for its implementation. In this way, it is possible to adapt csiEPT to various physical situations. In particular, an original formulation, specifically developed to take into account the effects of the conductive shield always employed in RF coils, shows how an accurate modelling of the measurement system leads to more precise estimations of the electric properties. In addition, a preliminary study for the uncertainty assessment of csiEPT, an imperative requirement in order to make the method reliable for in vivo applications, is performed. The uncertainty propagation through csiEPT is studied using the Monte Carlo method as prescribed by the Supplement 1 to GUM (Guide to the expression of Uncertainty in Measurement). The robustness of the method when measurements are performed by multi-channel TEM coils for parallel transmission confirms the eligibility of csiEPT for MRI safety applications
Dispositivi pressoterapici per arto superiore
No full textPRESENTIAMO LO STUDIO E LA COSTRUZIONE DI ALCUNI PROTOTIPI DI DISPOSITIVI APPLICABILI IN AMBITO BIOMEDICO PER TRATTAMENTI PRESSOTERAPIC
