MODELING AND HARDWARE-IN-THE-LOOP SIMULATION OF POWER-SPLIT HYBRID ELECTRIC VEHICLES

Conventional vehicles are creating pollution problems, global warming and the extinction of high density fuels. To address these problems, automotive companies and universities are researching on hybrid electric vehicles where two different power devices are used to propel a vehicle. This research studies the development and testing of a dynamic model for Prius 2010 Hybrid Synergy Drive (HSD), a power-split device. The device was modeled and integrated with a hybrid vehicle model. To add an electric only mode for vehicle propulsion, the hybrid synergy drive was modified by adding a clutch to carrier 1. The performance of the integrated vehicle model was tested with UDDS drive cycle using rule-based control strategy. The dSPACE Hardware-In-the-Loop (HIL) simulator was used for HIL simulation test. The HIL simulation result shows that the integration of developed HSD dynamic model with a hybrid vehicle model was successful. The HSD model was able to split power and isolate engine speed from vehicle speed in hybrid mode.

Early stage assessment of the inspectability of welded components : A case from the aerospace industry

This paper proposes a method to indicate potential problems when planning dye penetrant and x-ray inspection of welded components. Inspection has been found to be an important part of the manufacturability evaluation made in a large CAD-based parametric environment for making multidisciplinary design simulations in early stages of design at an aircraft component manufacturer. The paper explains how the proposed method is to be included in the design platform at the company. It predicts the expected probability of detection of cracks (POD) in situations where the geometry of the parts is unfavourable for inspection so that potential problems can be discovered and solved in early stages. It is based on automatically extracting information from CAD-models and making a rule-based evaluation. It also provides a scale for how favourable the geometry is for inspection. In the paper it is also shown that the manufacturability evaluation need to take into consideration the expected stresses in the structures, highlighting the importance of multi-disciplinary simulations.

Modelling the generation of customised poetry in Jess

The present paper presents an application that composes formal poetry in Spanish in a semiautomatic interactive fashion. JASPER is a forward reasoning rule-based system that obtains from the user an intended message, the desired metric, a choice of vocabulary, and a corpus of verses; and, by intelligent adaptation of selected examples from this corpus using the given words, carries out a prose-to-poetry translation of the given message. In the composition process, JASPER combines natural language generation and a set of construction heuristics obtained from formal literature on Spanish poetry.

Microwave-assisted synthesis and local analyses of positive insertion electrodes for Li-ion batteries

Efficient energy storage holds the key to reducing waste energy and enabling the use of advanced handheld electronic devices, hydrid electric vehicles and residential energy storage. Recently, Li-ion batteries have been identified and employed as energy storage devices due to their high gravimetric and volumetric energy densities, in comparison to previous technologies. However, more research is required to enhance the efficiency of Li-ion batteries by discovering electrodes with larger electrochemical discharge capacities, while maintaining electrochemical stability. The aims of this study are to develop new microwave-assisted synthesis routes to nanostructured insertion cathodes, which harbor a greater affinity for lithium extraction and insertion than bulk materials. Subsequent to this, state-of-the-art synchrotron based techniques have been employed to understand structural and dynamic behaviour of nanostructured cathode materials during battery cell operation. In this study, microwave-assisted routes to a-LiFePO4, VO2(B), V3O7, H2V3O8 and V4O6(OH)4 have all been developed. Muon spin relaxation has shown that the presence of b-LiFePO4 has a detrimental effect on the lithium diffusion properties of a-LiFePO4, in agreement with first principles calculations. For the first time, a-LiFePO4 nanostructures have been obtained by employing a deep eutectic solvent reaction media showing near theoretical capacity (162 mAh g–1). Studies on VO2(B) have shown that the discharge capacity obtained is linked to the synthesis method. Electrochemical studies of H2V3O8 nanowires have shown outstanding discharge capacities (323 mAh g–1 at 100 mA g–1) and rate capability (180 mAh g–1 at 1 A g–1). The electrochemcial properties of V4O6(OH)4 have been investigated for the first time and show a promising discharge capacity of (180 mAh g–1). Lastly, in situ X-ray absorption spectroscopy has been utilised to track the evolution of the oxidation states in a-LiFePO4, VO2(B) and H2V3O8, and has shown these can all be observed dynamically.

Fluorescence of nitrobenzoxadiazole (NBD)-labeled lipids in model membranes is connected not to lipid mobility but to probe location

Nitrobenzoxadiazole (NBD)-labeled lipids are popular fluorescent membrane probes. However, the understanding of important aspects of the photophysics of NBD remains incomplete, including the observed shift in the emission spectrum of NBD-lipids to longer wavelengths following excitation at the red edge of the absorption spectrum (red-edge excitation shift or REES). REES of NBD-lipids in membrane environments has been previously interpreted as reflecting restricted mobility of solvent surrounding the fluorophore. However, this requires a large change in the dipole moment (Dm) of NBD upon excitation. Previous calculations of the value of Dm of NBD in the literature have been carried out using outdated semi-empirical methods, leading to conflicting values. Using up-to-date density functional theory methods, we recalculated the value of Dm and verified that it is rather small (B2 D). Fluorescence measurements confirmed that the value of REES is B16 nm for 1,2-dioleoyl-sn-glycero-3- phospho-L-serine-N-(NBD) (NBD-PS) in dioleoylphosphatidylcholine vesicles. However, the observed shift is independent of both the temperature and the presence of cholesterol and is therefore insensitive to the mobility and hydration of the membrane. Moreover, red-edge excitation leads to an increased contribution of the decay component with a shorter lifetime, whereas time-resolved emission spectra of NBD-PS displayed an atypical blue shift following excitation. This excludes restrictions to solvent relaxation as the cause of the measured REES and TRES of NBD, pointing instead to the heterogeneous transverse location of probes as the origin of these effects. The latter hypothesis was confirmed by molecular dynamics simulations, from which the calculated heterogeneity of the hydration and location of NBD correlated with the measured fluorescence lifetimes/REES. Globally, our combination of theoretical and experiment-based techniques has led to a considerably improved understanding of the photophysics of NBD and a reinterpretation of its REES in particular.

Automotive Radars:towards smarter vehicles

Radars are expected to become the main sensors in various civilian applications, especially for autonomous driving. Their success is mainly due to the availability of low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. This thesis focuses on the study and the development of different deterministic and learning based techniques for colocated multiple-input multiple-output (MIMO) radars. In particular, after providing an overview on the architecture of these devices, the problem of detecting and estimating multiple targets in stepped frequency continuous wave (SFCW) MIMO radar systems is investigated and different deterministic techniques solving it are illustrated. Moreover, novel solutions, based on an approximate maximum likelihood approach, are developed. The accuracy achieved by all the considered algorithms is assessed on the basis of the raw data acquired from low power wideband radar devices. The results demonstrate that the developed algorithms achieve reasonable accuracies, but at the price of different computational efforts. Another important technical problem investigated in this thesis concerns the exploitation of machine learning and deep learning techniques in the field of colocated MIMO radars. In this thesis, after providing a comprehensive overview of the machine learning and deep learning techniques currently being considered for use in MIMO radar systems, their performance in two different applications is assessed on the basis of synthetically generated and experimental datasets acquired through a commercial frequency modulated continuous wave (FMCW) MIMO radar. Finally, the application of colocated MIMO radars to autonomous driving in smart agriculture is illustrated.

Development of Optimal Energy Management Strategies for a Hybrid Boat

The present work proposes different approaches to extend the mathematical methods of supervisory energy management used in terrestrial environments to the maritime sector, that diverges in constraints, variables and disturbances. The aim is to find the optimal real-time solution that includes the minimization of a defined track time, while maintaining the classical energetic approach. Starting from analyzing and modelling the powertrain and boat dynamics, the energy economy problem formulation is done, following the mathematical principles behind the optimal control theory. Then, an adaptation aimed in finding a winning strategy for the Monaco Energy Boat Challenge endurance trial is performed via ECMS and A-ECMS control strategies, which lead to a more accurate knowledge of energy sources and boat’s behaviour. The simulations show that the algorithm accomplishes fuel economy and time optimization targets, but the latter adds huge tuning and calculation complexity. In order to assess a practical implementation on real hardware, the knowledge of the previous approaches has been translated into a rule-based algorithm, that let it be run on an embedded CPU. Finally, the algorithm has been tuned and tested in a real-world race scenario, showing promising results.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Study of Bone Biomineralization in 2D and 3D Cultures of a Human Osteosarcoma Cell Line As Osteoblast-like Model

This PhD project focuses on the study of the early stages of bone biomineralization in 2D and 3D cultures of osteoblast-like SaOS-2 osteosarcoma cells, exposed to an osteogenic cocktail. The efficacy of osteogenic treatment was assessed on 2D cell cultures after 7 days. A large calcium minerals production, an overexpression of osteogenic markers and of alkaline phosphatase activity occurred in treated samples. TEM microscopy and cryo-XANES micro-spectroscopy were performed for localizing and characterizing Ca-depositions. These techniques revealed a different localization and chemical composition of Ca-minerals over time and after treatment. Nevertheless, the Mito stress test showed in treated samples a significant increase in maximal respiration levels associated to an upregulation of mitochondrial biogenesis indicative of an ongoing differentiation process. The 3D cell cultures were realized using two different hydrogels: a commercial collagen type I and a mixture of agarose and lactose-modified chitosan (CTL). Both biomaterials showed good biocompatibility with SaOS-2 cells. The gene expression analysis of SaOS-2 cells on collagen scaffolds indicated an osteogenic commitment after treatment. and Alizarin red staining highlighted the presence of Ca-spots in the differentiated samples. In addition, the intracellular magnesium quantification, and the X-ray microscopy on mineral depositions, suggested the incorporation of Mg during the early stages of bone formation process., SaOS-2 cells treated with osteogenic cocktail produced Ca mineral deposits also on CTL/agarose scaffolds, as confirmed by alizarin red staining. Further studies are underway to evaluate the differentiation also at the genetic level. Thanks to the combination of conventional laboratory methods and synchrotron-based techniques, it has been demonstrated that SaOS-2 is a suitable model for the study of biomineralization in vitro. These results have contributed to a deeper knowledge of biomineralization process in osteosarcoma cells and could provide new evidences about a therapeutic strategy acting on the reversibility of tumorigenicity by osteogenic induction.

Performance assessment of vehicular connectivity in hybrid electric vehicles for fuel and emissions reduction

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of global warming. Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. In order to address these issues, the present thesis presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. However, even if hybrid electric vehicles have been recognized as a valid solution in response to increasingly tight regulations, the reduced engine load and the repeated engine starts and stops may reduce substantially the temperature of the exhaust after-treatment system (EATS), leading to relevant issues related to pollutant emission control. In this context, electrically heated catalysts (EHCs) represent a promising solution to ensure high pollutant conversion efficiency without affecting engine efficiency and performance. This work aims at studying the advantages provided by the introduction of a predictive EHC control function for a light-duty Diesel plug-in hybrid electric vehicle (PHEV) equipped with a Euro 7-oriented EATS. Based on the knowledge of future driving scenarios provided by vehicular connectivity, engine first start can be predicted and therefore an EATS pre-heating phase can be planned.

Monitoring of New Psychoactive Substances in the hair of patients with a history of addiction. Evaluation of the public health impact.

Introduction. The term New Psychoactive Substances (NPS) encompasses a broad category of drugs which have become available on the market in recent years and whose illicit use for recreational purposes has recently exploded. The analysis of NPS usually requires mass spectrometry based techniques. The aim of our study was to define the preva-lence of NPS consumption in patients with a history of drug addiction followed by Public Services for Pathological Addictions, with the purpose of highlighting the effective presence of NPS within the area of Bologna and evaluating their association with classical drugs of abuse (DOA). Materials and methods. Sustained by literature, a multi-analyte UHPLC-MS/MS method for the identification of 127 NPS (phenethylamines, arylcyclohexylamines, synthetic opioids, tryptamines, synthetic cannabinoids, synthetic cathinones, designer benzodiazepines) and 15 classic drugs of abuse (DOA) in hair samples was developed and validated according to International Guidelines [112]. Samples pretreatment consisted of washing steps and overnight incubation at 45°C in an acid mixture of methanol and water. After cooling, supernatant were injected into the chromatographic system coupled with a tandem mass spectrometry detector. Results. Successful validation was achieved for almost all of the compounds. The method met all the required technical parameters. LOQ was set from 4 to 80 pg/mg The developed method was applied to 107 cases (85 males and 22 females) of clinical interest. Out of 85 hair samples resulting positive to classical drugs of abuse, NPS were found in twelve (8 male and 4 female). Conclusion. The present methodology represents an easy, low cost, wide-panel method for the de-tection of 127 NPS and 15 DOA in hair samples. Such multi-analyte methods facilitates the study of the prevalence of drugs abused that will enable the competent control authorities to obtain evi-dence-based reports regarding the critical spread of the threat represented by NPS.

Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison

PURPOSE: The main goal of this study was to develop and compare two different techniques for classification of specific types of corneal shapes when Zernike coefficients are used as inputs. A feed-forward artificial Neural Network (NN) and discriminant analysis (DA) techniques were used. METHODS: The inputs both for the NN and DA were the first 15 standard Zernike coefficients for 80 previously classified corneal elevation data files from an Eyesys System 2000 Videokeratograph (VK), installed at the Departamento de Oftalmologia of the Escola Paulista de Medicina, São Paulo. The NN had 5 output neurons which were associated with 5 typical corneal shapes: keratoconus, with-the-rule astigmatism, against-the-rule astigmatism, "regular" or "normal" shape and post-PRK. RESULTS: The NN and DA responses were statistically analyzed in terms of precision ([true positive+true negative]/total number of cases). Mean overall results for all cases for the NN and DA techniques were, respectively, 94% and 84.8%. CONCLUSION: Although we used a relatively small database, results obtained in the present study indicate that Zernike polynomials as descriptors of corneal shape may be a reliable parameter as input data for diagnostic automation of VK maps, using either NN or DA.

Monitoring the composition and form of urban environments based on the vegetation-impervious surface-soil (VIS) model by sub-pixel analysis techniques

The majority of the world's population now resides in urban environments and information on the internal composition and dynamics of these environments is essential to enable preservation of certain standards of living. Remotely sensed data, especially the global coverage of moderate spatial resolution satellites such as Landsat, Indian Resource Satellite and Systeme Pour I'Observation de la Terre (SPOT), offer a highly useful data source for mapping the composition of these cities and examining their changes over time. The utility and range of applications for remotely sensed data in urban environments could be improved with a more appropriate conceptual model relating urban environments to the sampling resolutions of imaging sensors and processing routines. Hence, the aim of this work was to take the Vegetation-Impervious surface-Soil (VIS) model of urban composition and match it with the most appropriate image processing methodology to deliver information on VIS composition for urban environments. Several approaches were evaluated for mapping the urban composition of Brisbane city (south-cast Queensland, Australia) using Landsat 5 Thematic Mapper data and 1:5000 aerial photographs. The methods evaluated were: image classification; interpretation of aerial photographs; and constrained linear mixture analysis. Over 900 reference sample points on four transects were extracted from the aerial photographs and used as a basis to check output of the classification and mixture analysis. Distinctive zonations of VIS related to urban composition were found in the per-pixel classification and aggregated air-photo interpretation; however, significant spectral confusion also resulted between classes. In contrast, the VIS fraction images produced from the mixture analysis enabled distinctive densities of commercial, industrial and residential zones within the city to be clearly defined, based on their relative amount of vegetation cover. The soil fraction image served as an index for areas being (re)developed. The logical match of a low (L)-resolution, spectral mixture analysis approach with the moderate spatial resolution image data, ensured the processing model matched the spectrally heterogeneous nature of the urban environments at the scale of Landsat Thematic Mapper data.

A survey of techniques and technologies for web-based real-time interactive rendering

When exploring a virtual environment, realism depends mainly on two factors: realistic images and real-time feedback (motions, behaviour etc.). In this context, photo realism and physical validity of computer generated images required by emerging applications, such as advanced e-commerce, still impose major challenges in the area of rendering research whereas the complexity of lighting phenomena further requires powerful and predictable computing if time constraints must be attained. In this technical report we address the state-of-the-art on rendering, trying to put the focus on approaches, techniques and technologies that might enable real-time interactive web-based clientserver rendering systems. The focus is on the end-systems and not the networking technologies used to interconnect client(s) and server(s).

Effects of self-administered exercises based on Tuina techniques on musculoskeletal disorders of professional orchestra musicians: a randomized controlled trial

Background Musicians are frequently affected by playing-related musculoskeletal disorders (PRMD). Common solutions used by Western medicine to treat musculoskeletal pain include rehabilitation programs and drugs, but their results are sometimes disappointing. Objective To study the effects of self-administered exercises based on Tuina techniques on the pain intensity caused by PRMD of professional orchestra musicians, using numeric visual scale (NVS). Design, setting, participants and interventions We performed a prospective, controlled, single-blinded, randomized study with musicians suffering from PRMD. Participating musicians were randomly distributed into the experimental (n = 39) and the control (n = 30) groups. After an individual diagnostic assessment, specific Tuina self-administered exercises were developed and taught to the participants. Musicians were instructed to repeat the exercises every day for 3 weeks. Main outcome measures Pain intensity was measured by NVS before the intervention and after 1, 3, 5, 10, 15 and 20 d of treatment. The procedure was the same for the control group, however the Tuina exercises were executed in points away from the commonly-used acupuncture points. Results In the treatment group, but not the control group, pain intensity was significantly reduced on days 1, 3, 5, 10, 15 and 20. Conclusion The results obtained are consistent with the hypothesis that self-administered exercises based on Tuina techniques could help professional musicians controlling the pain caused by PRMD. Although our results are very promising, further studies are needed employing a larger sample size and double blinding designs.