Upgrading Bio-Platform Molecules in the Gas-Phase: From Levulinic Acid to Bio-Chemicals

Levulinic acid (LA) is a polyfunctional molecule obtained from biomass. Because of its structure, the United States Department of energy classified LA as one of the top 12 building block chemicals. Typically, it is valorized through chemical reduction to γ-valerolactone (GVL). It is usually done with H2 in batch systems with high H2 pressures and noble metal catalysts, making it expensive and less applicable. Therefore, alternative approaches such as catalytic transfer hydrogenation (CTH) through the Meerwein–Ponndorf–Verley (MPV) reaction over heterogeneous catalysts have been studied. This uses organic molecules (alcohols) which act as a hydride transfer agent (H-donor), to reduce molecules containing carbonyl groups. Given the stability of the intermediate, reports have shown the batch liquid-phase CTH of levulinate esters with secondary alcohols, and remarkable results (GVL yield) have been obtained over ZrO2, given the need of a Lewis acid (LASites) and base pair for CTH. However, there were no reports of the continuous gas-phase CTH of levulinate esters. Therefore, high surface area ZrO2 was tested for gas-phase CTH of methyl levulinate (ML) using ethanol, methanol and isopropanol as H-donors. Under optimized conditions with ethanol (250 ℃), the reaction is selective towards GVL (yield 70%). However, heavy carbonaceous materials over the catalyst surface progressively blocked LASites changing the chemoselectivity. The in situ regeneration of the catalyst permitted a partial recovery of the LASites and an almost total recovery of the initial catalytic behavior, proving the deactivation reversible. Tests with methanol were not promising (ML conversion 35%, GVL yield 4%). As expected, using isopropanol provided complete conversion and a GVL yield of 80%. The reaction was also tested using bioethanol derived from agricultural waste. In addition, a preliminary study was performed for the hydrogenolysis of polyols to produce bioethanol, were Pd-Fe catalyst promoted the ethanol selective (37%) hydrogenolysis of glycerol.

Global Fatigue Assessment of a Decommissioned Jacket Platform for a Sustainable Reuse as an Offshore Wind Turbine

When the offshore oil and gas supplies exhaust, offshore platforms must be decommissioned and removed. The present thesis highlights the importance of evaluating the possibility of reuse of decommissioned offshore jacket platforms for offshore wind energy. In order to shift to the new structure, the topside must be removed from the substructure and a wind turbine can be installed in its place. The feasibility of this project was investigated using a finite element analysis software called Sesam. To study fatigue life in offshore structures, an exhaustive review of the background and state of the art was done. A finite element model was created by the means of Sesam and two different fatigue analysis approaches were applied and compared. In the end, an analysis methodology is suggested for the structural fatigue analysis of offshore wind turbine structures based on international standards, addressing the industry’s need to account for the combined effect of wind and hydrodynamic loads in these type of structures.

Metocean Climate Impact on the Fatigue Assessment of a Jacket-Type Offshore Platform

When the offshore oil and gas supplies exhaust, most offshore platforms are decommissioned and removed. The purpose of this paper is to evaluate the fatigue damage that will occur during the service life of a jacket-type offshore platform using different fatigue approaches in particular locations. The locations considered for this metocean climate impact study were Norway (North Sea), Portugal (Atlantic Ocean - Leixões) and Italy (Adriatic Sea). A finite element model was created by the means of Sesam and two different fatigue analysis, deterministic and spectral, were applied. For the fatigue assessment, an appropriate description of the site-specific wave environment, during the jacket platform service life, must be accomplished. This description is usually provided by a wave scatter diagram. Wave scatter diagrams usually represent the long-term wave environment during a (typical) year and are based on several years of site-specific data to ensure that they adequately represent the wave environment at the location of the structure. In this thesis, the comparison between these fatigue approaches will serve as a pilot study for planned reliability analysis in decommissioned offshore platforms in order to maximize the reuse of these platforms for future wind generation systems.

Portfolios for learning; course design and platform choices

Presentation at M25 Learning Technology Group, FutureLearn, 15 November 2017

Development of a numerical platform for the modeling and optimal control of liquid metal flows

The main purpose of this work is to develop a numerical platform for the turbulence modeling and optimal control of liquid metal flows. Thanks to their interesting thermal properties, liquid metals are widely studied as coolants for heat transfer applications in the nuclear context. However, due to their low Prandtl numbers, the standard turbulence models commonly used for coolants as air or water are inadequate. Advanced turbulence models able to capture the anisotropy in the flow and heat transfer are then necessary. In this thesis, a new anisotropic four-parameter turbulence model is presented and validated. The proposed model is based on explicit algebraic models and solves four additional transport equations for dynamical and thermal turbulent variables. For the validation of the model, several flow configurations are considered for different Reynolds and Prandtl numbers, namely fully developed flows in a plane channel and cylindrical pipe, and forced and mixed convection in a backward-facing step geometry. Since buoyancy effects cannot be neglected in liquid metals-cooled fast reactors, the second aim of this work is to provide mathematical and numerical tools for the simulation and optimization of liquid metals in mixed and natural convection. Optimal control problems for turbulent buoyant flows are studied and analyzed with the Lagrange multipliers method. Numerical algorithms for optimal control problems are integrated into the numerical platform and several simulations are performed to show the robustness, consistency, and feasibility of the method.

A robotic platform for precision agriculture and applications

Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.

Hybrid ground-aerial mesh networks for IoT monitoring applications: network design and software platform development

The Internet of Things (IoT) has grown rapidly in recent years, leading to an increased need for efficient and secure communication between connected devices. Wireless Sensor Networks (WSNs) are composed of small, low-power devices that are capable of sensing and exchanging data, and are often used in IoT applications. In addition, Mesh WSNs involve intermediate nodes forwarding data to ensure more robust communication. The integration of Unmanned Aerial Vehicles (UAVs) in Mesh WSNs has emerged as a promising solution for increasing the effectiveness of data collection, as UAVs can act as mobile relays, providing extended communication range and reducing energy consumption. However, the integration of UAVs and Mesh WSNs still poses new challenges, such as the design of efficient control and communication strategies. This thesis explores the networking capabilities of WSNs and investigates how the integration of UAVs can enhance their performance. The research focuses on three main objectives: (1) Ground Wireless Mesh Sensor Networks, (2) Aerial Wireless Mesh Sensor Networks, and (3) Ground/Aerial WMSN integration. For the first objective, we investigate the use of the Bluetooth Mesh standard for IoT monitoring in different environments. The second objective focuses on deploying aerial nodes to maximize data collection effectiveness and QoS of UAV-to-UAV links while maintaining the aerial mesh connectivity. The third objective investigates hybrid WMSN scenarios with air-to-ground communication links. One of the main contribution of the thesis consists in the design and implementation of a software framework called "Uhura", which enables the creation of Hybrid Wireless Mesh Sensor Networks and abstracts and handles multiple M2M communication stacks on both ground and aerial links. The operations of Uhura have been validated through simulations and small-scale testbeds involving ground and aerial devices.

Design of a wearable platform for PPG analysis with multicore low-power processor

Photoplethysmography (PPG) sensors allow for noninvasive and comfortable heart-rate (HR) monitoring, suitable for compact wearable devices. However, PPG signals collected from such devices often suffer from corruption caused by motion artifacts. This is typically addressed by combining the PPG signal with acceleration measurements from an inertial sensor. Recently, different energy-efficient deep learning approaches for heart rate estimation have been proposed. To test these new solutions, in this work, we developed a highly wearable platform (42mm x 48 mm x 1.2mm) for PPG signal acquisition and processing, based on GAP9, a parallel ultra low power system-on-chip featuring nine cores RISC-V compute cluster with neural network accelerator and 1 core RISC-V controller. The hardware platform also integrates a commercial complete Optical Biosensing Module and an ARM-Cortex M4 microcontroller unit (MCU) with Bluetooth low-energy connectivity. To demonstrate the capabilities of the system, a deep learning-based approach for PPG-based HR estimation has been deployed. Thanks to the reduced power consumption of the digital computational platform, the total power budget is just 2.67 mW providing up to 5 days of operation (105 mAh battery).

Attenuation of thermal radiation from flare through water curtains on offshore platform.

Flaring has been widely used in the upstream operation of the oil and gas industry, both onshore and offshore. It is considered a safe and reliable way to protect assets from overpressure and the environment from toxic gas using combustion. However, there are drawbacks to using flares, such as vibration and thermal radiation. Excessive contact with thermal radiation is harmful to offshore personnel and equipment. Research organizations and companies have invested time and money to combat this. Many technologies have been developed so far to reduce the risk of thermal radiation, one of them being the water curtain system. Several tests were done to see the effectiveness of the water curtain system in mitigating thermal radiation in an offshore environment. Each test varied in the flare output, wind speed, and the size of water droplets size of the water curtain. Later, the results of each test were compared and analyzed. The results showed that a water curtain system could be a solution to excessive thermal radiation that comes from an offshore flare. Moreover, the water curtain with smaller water droplets diameter gives a more favorable result in reducing thermal radiation. These results suggest that, although it offers simplicity and efficiency, designing an efficient water curtain system requires deep study. Various conditions, such as wind speed, flare intensity, and the size of the water droplets, plays a vital role in the effectiveness of the water curtain system in attenuating thermal radiation.

Motor Unit Spike Trains Recontruction from sEMG Signals for Gesture Classification on a Low-Power Processing Platform

Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency < 50 ms and an energy consumption < 1 mJ. Both the classification models tested (a support vector machine and a lightweight MLP) yielded an accuracy > 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG.

Ottimizzazione di un Data Warehouse con Google Cloud Platform

I dati sono una risorsa di valore inestimabile per tutte le organizzazioni. Queste informazioni vanno da una parte gestite tramite i classici sistemi operazionali, dall’altra parte analizzate per ottenere approfondimenti che possano guidare le scelte di business. Uno degli strumenti fondamentali a supporto delle scelte di business è il data warehouse. Questo elaborato è il frutto di un percorso di tirocinio svolto con l'azienda Injenia S.r.l. Il focus del percorso era rivolto all'ottimizzazione di un data warehouse che l'azienda vende come modulo aggiuntivo di un software di nome Interacta. Questo data warehouse, Interacta Analytics, ha espresso nel tempo notevoli criticità architetturali e di performance. L’architettura attualmente usata per la creazione e la gestione dei dati all'interno di Interacta Analytics utilizza un approccio batch, pertanto, l’obiettivo cardine dello studio è quello di trovare soluzioni alternative batch che garantiscano un risparmio sia in termini economici che di tempo, esplorando anche la possibilità di una transizione ad un’architettura streaming. Gli strumenti da utilizzare in questa ricerca dovevano inoltre mantenersi in linea con le tecnologie utilizzate per Interacta, ossia i servizi della Google Cloud Platform. Dopo una breve dissertazione sul background teorico di questa area tematica, l'elaborato si concentra sul funzionamento del software principale e sulla struttura logica del modulo di analisi. Infine, si espone il lavoro sperimentale, innanzitutto proponendo un'analisi delle criticità principali del sistema as-is, dopodiché ipotizzando e valutando quattro ipotesi migliorative batch e due streaming. Queste, come viene espresso nelle conclusioni della ricerca, migliorano di molto le performance del sistema di analisi in termini di tempistiche di elaborazione, di costo totale e di semplicità dell'architettura, in particolare grazie all'utilizzo dei servizi serverless con container e FaaS della piattaforma cloud di Google.

[almost An Autobiography: A Study Of Social Scientists In Health Based On The Lattes Curriculum].

Among the various ways of adopting the biographical approach, we used the curriculum vitaes (CVs) of Brazilian researchers who work as social scientists in health as our research material. These CVs are part of the Lattes Platform of CNPq - the National Council for Scientific and Technological Development, which includes Research and Institutional Directories. We analyzed 238 CVs for this study. The CVs contain, among other things, the following information: professional qualifications, activities and projects, academic production, participation in panels for the evaluation of theses and dissertations, research centers and laboratories and a summarized autobiography. In this work there is a brief review of the importance of autobiography for the social sciences, emphasizing the CV as a form of autobiographical practice. We highlight some results, such as it being a group consisting predominantly of women, graduates in social sciences, anthropology, sociology or political science, with postgraduate degrees. The highest concentration of social scientists is located in Brazil's southern and southeastern regions. In some institutions the main activities of social scientists are as teachers and researchers with great thematic diversity in research.

De Novo Assembly And Transcriptome Analysis Of The Rubber Tree (hevea Brasiliensis) And Snp Markers Development For Rubber Biosynthesis Pathways.

Hevea brasiliensis (Willd. Ex Adr. Juss.) Muell.-Arg. is the primary source of natural rubber that is native to the Amazon rainforest. The singular properties of natural rubber make it superior to and competitive with synthetic rubber for use in several applications. Here, we performed RNA sequencing (RNA-seq) of H. brasiliensis bark on the Illumina GAIIx platform, which generated 179,326,804 raw reads on the Illumina GAIIx platform. A total of 50,384 contigs that were over 400 bp in size were obtained and subjected to further analyses. A similarity search against the non-redundant (nr) protein database returned 32,018 (63%) positive BLASTx hits. The transcriptome analysis was annotated using the clusters of orthologous groups (COG), gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pfam databases. A search for putative molecular marker was performed to identify simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). In total, 17,927 SSRs and 404,114 SNPs were detected. Finally, we selected sequences that were identified as belonging to the mevalonate (MVA) and 2-C-methyl-D-erythritol 4-phosphate (MEP) pathways, which are involved in rubber biosynthesis, to validate the SNP markers. A total of 78 SNPs were validated in 36 genotypes of H. brasiliensis. This new dataset represents a powerful information source for rubber tree bark genes and will be an important tool for the development of microsatellites and SNP markers for use in future genetic analyses such as genetic linkage mapping, quantitative trait loci identification, investigations of linkage disequilibrium and marker-assisted selection.

Screening The Life Cycle Of Schistosoma Mansoni Using High-resolution Mass Spectrometry.

Schistosomiasis is a common tropical disease caused by Schistosoma species Schistosomiasis' pathogenesis is known to vary according to the worms' strain. Moreover, high parasitical virulence is directly related to eggs release and granulomatous inflammation in the host's organs. This virulence might be influenced by different classes of molecules, such as lipids. Therefore, better understanding of the metabolic profile of these organisms is necessary, especially for an increased potential of unraveling strain virulence mechanisms and resistance to existing treatments. In this report, direct-infusion electrospray high-resolution mass spectrometry (ESI(+)-HRMS) along with the lipidomic platform were employed to rapidly characterize and differentiate two Brazilian S. mansoni strains (BH and SE) in three stages of their life cycle: eggs, miracidia and cercariae, with samples from experimental animals (Swiss/SPF mice). Furthermore, urine samples of the infected and uninfected mice were analyzed to assess the possibility of direct diagnosis. All samples were differentiated using multivariate data analysis, PCA, which helped electing markers from distinct lipid classes; phospholipids, diacylglycerols and triacylglycerols, for example, clearly presented different intensities in some stages and strains, as well as in urine samples. This indicates that biochemical characterization of S. mansoni may help narrowing-down the investigation of new therapeutic targets according to strain composition and aggressiveness of disease. Interestingly, lipid profile of infected mice urine varies when compared to control samples, indicating that direct diagnosis of schistosomiasis from urine may be feasible.

Solid Lipid Nanoparticles As Nucleic Acid Delivery System: Properties And Molecular Mechanisms.

Solid lipid nanoparticles (SLNs) have been proposed in the 1990s as appropriate drug delivery systems, and ever since they have been applied in a wide variety of cosmetic and pharmaceutical applications. In addition, SLNs are considered suitable alternatives as carriers in gene delivery. Although important advances have been made in this particular field, fundamental knowledge of the underlying mechanisms of SLN-mediated gene delivery is conspicuously lacking, an imperative requirement in efforts aimed at further improving their efficiency. Here, we address recent advances in the use of SLNs as platform for delivery of nucleic acids as therapeutic agents. In addition, we will discuss available technology for conveniently producing SLNs. In particular, we will focus on underlying molecular mechanisms by which SLNs and nucleic acids assemble into complexes and how the nucleic acid cargo may be released intracellularly. In discussing underlying mechanisms, we will, when appropriate, refer to analogous studies carried out with systems based on cationic lipids and polymers, that have proven useful in the assessment of structure-function relationships. Finally, we will give suggestions for improving SLN-based gene delivery systems, by pointing to alternative methods for SLNplex assembly, focusing on the realization of a sustained nucleic acid release.