RED-Bridge: A Multiprotocol Tool Prototype for IoT Sensors

The IoT is growing more and more each year and is becoming so ubiquitous that it includes heterogeneous devices with different hardware and software constraints leading to an highly fragmented ecosystem. Devices are using different protocols with different paradigms and they are not compatible with each other; some devices use request-response protocols like HTTP or CoAP while others use publish-subscribe protocols like MQTT. Integration in IoT is still an open research topic. When handling and testing IoT sensors there are some common task that people may be interested in: reading and visualizing the current value of the sensor; doing some aggregations on a set of values in order to compute statistical features; saving the history of the data to a time-series database; forecasting the future values to react in advance to a future condition; bridging the protocol of the sensor in order to integrate the device with other tools. In this work we will show the working implementation of a low-code and flow-based tool prototype which supports the common operations mentioned above, based on Node-RED and Python. Since this system is just a prototype, it has some issues and limitations that will be discussed in this work.

Are there genetic breaks between Atlantic and Pacific Yellowfin tuna (Thunnus albacares) populations? A preliminary study based on microsatellites gene variation

Yellowfin tuna (Thunnus albacares, YFT, Bonnaterre 1788) is one of the most important market tuna species in the world. The high mortality of juveniles is in part caused by their bycatch. Indeed, if unregulated, it could permanently destabilize stocks health. For this reason investigating and better knowing the stock boundaries represent a crucial concern. Aim of this thesis was to preliminary investigate the YFT population structure within and between Atlantic and Pacific Oceans through the analysis of genetic variation at eight microsatellite loci and assess the occurrence of barriers to the gene flow between Oceans. For this propouse we collected 4 geographical samples coming from Atlantic and Pacific Ocean and selected a panel of 8 microsatellites loci developped by Antoni et al., (2014). Samples 71-2-Y and 77-2-Y, came from rispectively west central pacific ocean (WCPO) and east central pacific ocean (ECPO), instead samples 41-1-Y and 34-2-Y derive from west central atlantic ocean (WCAO) and east central atlantic ocean (ECAO). Total 160 specimens were analyzed (40 per sample) and were carried out several genetic information as allele frequencies, allele number, allelic richness, HWE (using He and Ho) and pairwise Fst genetic distance. Results obtained, may support the panmictic theory of this species, only one of pairwise Fst obtained is statistically significant (Fst= 0.00927; pV= 0.00218) between 41-1-Y and 71-2-Y samples. Results suggest low genetic differentiation and consequent high level of gene flow between Atlantic and Pacific populations. Furthermore, we performed an analysis of molecular taxonomy through the use of ATCO (the flaking region between ATPse6 and cytochrome oxidase subunit III genes mt DNA, to discriminate within the gener Thunnus two of the related species (Yellofin and bigeye tuna) according with their difficult recognition at certain size (<40 cm). ATCO analysis in this thesis, has provided strong discriminate evidence between the target species proving to be one of the most reliable genetic tools capable to indagate within the genus Thunnus. Thus, our study has provided useful information for possible use of this protocol for conservation plans and management of this fish stocks.

Multiphase flow in porous media: meta-modeling techniques for sensitivity analysis and risk assessment

One of the biggest challenges that contaminant hydrogeology is facing, is how to adequately address the uncertainty associated with model predictions. Uncertainty arise from multiple sources, such as: interpretative error, calibration accuracy, parameter sensitivity and variability. This critical issue needs to be properly addressed in order to support environmental decision-making processes. In this study, we perform Global Sensitivity Analysis (GSA) on a contaminant transport model for the assessment of hydrocarbon concentration in groundwater. We provide a quantification of the environmental impact and, given the incomplete knowledge of hydrogeological parameters, we evaluate which are the most influential, requiring greater accuracy in the calibration process. Parameters are treated as random variables and a variance-based GSA is performed in a optimized numerical Monte Carlo framework. The Sobol indices are adopted as sensitivity measures and they are computed by employing meta-models to characterize the migration process, while reducing the computational cost of the analysis. The proposed methodology allows us to: extend the number of Monte Carlo iterations, identify the influence of uncertain parameters and lead to considerable saving computational time obtaining an acceptable accuracy.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.