Preparation and assessment of immobilized laccase-based micro- and nanocatalysts for the degradation of anthropogenic compounds

Since the dawn of its presence on earth, the human being has been able to exploit the enzymes for its subsistence. More recent is the meeting between the enzymatic processes and the urgent need for technologies that aim to preserve our planet. In this field nowadays enzymatic catalysis is tested either to depollution/remediation as well as waste disposal. The work presented in this thesis, regarding both these two topics, is tailored on two European projects (EU 2020), MADFORWATER and TERMINUS respectively. Firstly, production of micro- and nanocatalysts via immobilization of laccases (a lignin-degrader enzyme) is performed. In the second part of the thesis laccase is applied to a tertiary treatment of wastewater with the aim to degrade 9 pharmaceutical active compounds in batch reactors. Despite several optimizations, poor degradation is reached and we did not proceed with the study of different bioreactor setups. Therefore, the focus is moved to a project concerning the production of smart multi-layer plastic packaging containing enzymes to improve the possibilities of recycling. In this field shielded nanocatalysts produced via coating techniques able to interact with redox mediators are investigated. The target substrate in this second project is produced in laboratory (i.e. polyurethane like compounds), starting from monomers whose degradation had already been tested, as a proof of concept. The first enzyme studied is still the laccase.

Polyaniline-based materials and nanocomposites: from energy storage to sensing applications

The research work described in this thesis concerns materials for both energy storage and sensoristics applications. Firstly, the synthesis and characterization of magnetite (Fe3O4) functionalyzed with [3-(2-propynylcarbamate)propyl]triethoxysilane (PPTEOS) capable to reduce the gold precursor chloroauric acid (HAuCl4) without the need of additional reducing or stabilising agents is described. These nanoparticles were tested to improve performances of symmetric capacitors based on polyaniline and graphite foil. Energy storage applications were investigated also during six months stay at EPFL University of Lausanne where an investigation about different tailored catalysts for Oxygen Evolution Reaction in a particular Redox Flow Battery was carried out. For what concerns sensing applications, new materials based on cellulose modified with polyaniline and poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAAMPSA) were synthesized, characterized and applied to monitor pressure, humidity, heart rate and lastly, bread fermentation in collaboration with the University of Fribourg and Zurich. The characterizations of all the materials investigated compriseed numerous techniques such as infrared attenuated total reflectance spectroscopy (IR-ATR), thermogravimetric analysis (TGA), scanning and transmission electron microscopy (SEM and TEM), alongside linear and cyclic voltammetry (LSV and CV), electrochemical impedance spectroscopy (EIS) and chronoamperometric analyses.

Towards cheaper, safer and low environmental impact materials for energy storage devices: natural resources-derived carbon based electrodes and aqueous electrolytes for supercapacitors

The growing market of electrical cars, portable electronics, photovoltaic systems..etc. requires the development of efficient, low-cost, and low environmental impact energy storage devices (ESDs) including batteries and supercapacitors.. Due to their extended charge-discharge cycle, high specific capacitance, and power capabilities supercapacitors are considered among the most attractive ESDs. Over the last decade, research and development in supercapacitor technology have accelerated: thousands of articles have been published in the literature describing the electrochemical properties of the electrode materials and electrolyte in addition to separators and current collectors. Carbon-based supercapacitor electrodes materials have gained increasing attention due to their high specific surface area, good electrical conductivity, and excellent stability in harsh environments, as well as other characteristics. Recently, there has been a surge of interest in activated carbon derived from low-cost abundant sources such as biomass for supercapacitor electrode materials. Also, particular attention was given to a major challenging issue concerning the substitution of organic solutions currently used as electrolytes due to their highest electrochemical stability window even though their high cost, toxicity, and flammability. In this regard, the main objective of this thesis is to investigate the performances of supercapacitors using low cost abundant safe, and low environmental impact materials for electrodes and electrolytes. Several prototypes were constructed and tested using natural resources through optimization of the preparation of appropriate carbon electrodes using agriculture by-products waste or coal (i.e. Argan shell or Anthracite from Jerrada). Such electrodes were tested using several electrolyte formulations (aqueous and water in salt electrolytes) beneficing their non-flammability, lower cost, and environmental impact; the characteristics that provide a promising opportunity to design safer, inexpensive, and environmentally friendly devices compared to organic electrolytes.

The neurochemical profile quantified by in vivo 1H NMR spectroscopy.

Proton NMR spectroscopy is emerging from translational and preclinical neuroscience research as an important tool for evidence based diagnosis and therapy monitoring. It provides biomarkers that offer fingerprints of neurological disorders even in cases where a lesion is not yet observed in MR images. The collection of molecules used as cerebral biomarkers that are detectable by (1)H NMR spectroscopy define the so-called "neurochemical profile". The non-invasive quality of this technique makes it suitable not only for diagnostic purposes but also for therapy monitoring paralleling an eventual neuroprotection. The application of (1)H NMR spectroscopy in basic and translational neuroscience research is discussed here.

Landslide detection and monitoring capability of boat-based mobile laser scanning along Dieppe coastal cliffs, Normandy

Integrated in a wide research assessing destabilizing and triggering factors to model cliff dynamic along the Dieppe's shoreline in High Normandy, this study aims at testing boat-based mobile LiDAR capabilities by scanning 3D point clouds of the unstable coastal cliffs. Two acquisition campaigns were performed in September 2012 and September 2013, scanning (1) a 30-km-long shoreline and (2) the same test cliffs in different environmental conditions and device settings. The potentials of collected data for 3D modelling, change detection and landslide monitoring were afterward assessed. By scanning during favourable meteorological and marine conditions and close to the coast, mobile LiDAR devices are able to quickly scan a long shoreline with median point spacing up to 10cm. The acquired data are then sufficiently detailed to map geomorphological features smaller than 0.5m2. Furthermore, our capability to detect rockfalls and erosion deposits (>m3) is confirmed, since using the classical approach of computing differences between sequential acquisitions reveals many cliff collapses between Pourville and Quiberville and only sparse changes between Dieppe and Belleville-sur-Mer. These different change rates result from different rockfall susceptibilities. Finally, we also confirmed the capability of the boat-based mobile LiDAR technique to monitor single large changes, characterizing the Dieppe landslide geometry with two main active scarps, retrogression up to 40m and about 100,000m3 of eroded materials.

From Regional Landslide Detection to Site-Specific Slope Deformation Monitoring and Modelling Based on Active Remote Sensors

Landslide processes can have direct and indirect consequences affecting human lives and activities. In order to improve landslide risk management procedures, this PhD thesis aims to investigate capabilities of active LiDAR and RaDAR sensors for landslides detection and characterization at regional scales, spatial risk assessment over large areas and slope instabilities monitoring and modelling at site-specific scales. At regional scales, we first demonstrated recent boat-based mobile LiDAR capabilities to model topography of the Normand coastal cliffs. By comparing annual acquisitions, we validated as well our approach to detect surface changes and thus map rock collapses, landslides and toe erosions affecting the shoreline at a county scale. Then, we applied a spaceborne InSAR approach to detect large slope instabilities in Argentina. Based on both phase and amplitude RaDAR signals, we extracted decisive information to detect, characterize and monitor two unknown extremely slow landslides, and to quantify water level variations of an involved close dam reservoir. Finally, advanced investigations on fragmental rockfall risk assessment were conducted along roads of the Val de Bagnes, by improving approaches of the Slope Angle Distribution and the FlowR software. Therefore, both rock-mass-failure susceptibilities and relative frequencies of block propagations were assessed and rockfall hazard and risk maps could be established at the valley scale. At slope-specific scales, in the Swiss Alps, we first integrated ground-based InSAR and terrestrial LiDAR acquisitions to map, monitor and model the Perraire rock slope deformation. By interpreting both methods individually and originally integrated as well, we therefore delimited the rockslide borders, computed volumes and highlighted non-uniform translational displacements along a wedge failure surface. Finally, we studied specific requirements and practical issues experimented on early warning systems of some of the most studied landslides worldwide. As a result, we highlighted valuable key recommendations to design new reliable systems; in addition, we also underlined conceptual issues that must be solved to improve current procedures. To sum up, the diversity of experimented situations brought an extensive experience that revealed the potential and limitations of both methods and highlighted as well the necessity of their complementary and integrated uses.

Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present

Tropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) rainfall monitoring products have been extended to provide spatially contiguous rainfall estimates across Africa. This has been achieved through a new, climatology-based calibration, which varies in both space and time. As a result, cumulative estimates of rainfall are now issued at the end of each 10-day period (dekad) at 4-km spatial resolution with pan-African coverage. The utility of the products for decision making is improved by the routine provision of validation reports, for which the 10-day (dekadal) TAMSAT rainfall estimates are compared with independent gauge observations. This paper describes the methodology by which the TAMSAT method has been applied to generate the pan-African rainfall monitoring products. It is demonstrated through comparison with gauge measurements that the method provides skillful estimates, although with a systematic dry bias. This study illustrates TAMSAT’s value as a complementary method of estimating rainfall through examples of successful operational application.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Versatile and easy-to-assemble measurement system for impedance-based structural health monitoring

In this paper we present a versatile and easy-to-assemble measurement system for structural health monitoring (SHM) based on the electromechanical impedance (EMI) technique. The hardware of the proposed system consists only of a common data acquisition (DAQ) device with external resistors and allows real-time data acquisition from multiple sensors. Besides the low-cost compared to conventional impedance analyzers, the hardware and the software are simple and easier to implement than other measurement systems that have been recently proposed.

Monitoring the condition of savanna riparian zones in Northern Australia using high spatial resolution imagery and a field based rapid assessment method

Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone condition. The objective of this work was to compare the Tropical Rapid Appraisal of Riparian Condition (TRARC) method to a satellite image based approach. TRARC was developed for rapid assessment of the environmental condition of savanna riparian zones. The comparison assessed mapping accuracy, representativeness of TRARC assessment, cost-effectiveness, and suitability for multi-temporal analysis. Two multi-spectral QuickBird images captured in 2004 and 2005 and coincident field data covering sections of the Daly River in the Northern Territory, Australia were used in this work. Both field and image data were processed to map riparian health indicators (RHIs) including percentage canopy cover, organic litter, canopy continuity, stream bank stability, and extent of tree clearing. Spectral vegetation indices, image segmentation and supervised classification were used to produce RHI maps. QuickBird image data were used to examine if the spatial distribution of TRARC transects provided a representative sample of ground based RHI measurements. Results showed that TRARC transects were required to cover at least 3% of the study area to obtain a representative sample. The mapping accuracy and costs of the image based approach were compared to those of the ground based TRARC approach. Results proved that TRARC was more cost-effective at smaller scales (1-100km), while image based assessment becomes more feasible at regional scales (100-1000km). Finally, the ability to use both the image and field based approaches for multi-temporal analysis of RHIs was assessed. Change detection analysis demonstrated that image data can provide detailed information on gradual change, while the TRARC method was only able to identify more gross scale changes. In conclusion, results from both methods were considered to complement each other if used at appropriate spatial scales.

Finite Element Modeling of Spiral Frequency Steerable Acoustic Transducers (FSATs) for guided waves based Structural Health Monitoring of plate-like structures

Structural Health Monitoring (SHM) is an emerging area of research associated to improvement of maintainability and the safety of aerospace, civil and mechanical infrastructures by means of monitoring and damage detection. Guided wave structural testing method is an approach for health monitoring of plate-like structures using smart material piezoelectric transducers. Among many kinds of transducers, the ones that have beam steering feature can perform more accurate surface interrogation. A frequency steerable acoustic transducer (FSATs) is capable of beam steering by varying the input frequency and consequently can detect and localize damage in structures. Guided wave inspection is typically performed through phased arrays which feature a large number of piezoelectric transducers, complexity and limitations. To overcome the weight penalty, the complex circuity and maintenance concern associated with wiring a large number of transducers, new FSATs are proposed that present inherent directional capabilities when generating and sensing elastic waves. The first generation of Spiral FSAT has two main limitations. First, waves are excited or sensed in one direction and in the opposite one (180 ̊ ambiguity) and second, just a relatively rude approximation of the desired directivity has been attained. Second generation of Spiral FSAT is proposed to overcome the first generation limitations. The importance of simulation tools becomes higher when a new idea is proposed and starts to be developed. The shaped transducer concept, especially the second generation of spiral FSAT is a novel idea in guided waves based of Structural Health Monitoring systems, hence finding a simulation tool is a necessity to develop various design aspects of this innovative transducer. In this work, the numerical simulation of the 1st and 2nd generations of Spiral FSAT has been conducted to prove the directional capability of excited guided waves through a plate-like structure.

Activity-based life cycle costing model for cost estimation and monitoring – Case: manned and autonomous dry bulk carrier

Recent developments in automation, robotics and artificial intelligence have given a push to a wider usage of these technologies in recent years, and nowadays, driverless transport systems are already state-of-the-art on certain legs of transportation. This has given a push for the maritime industry to join the advancement. The case organisation, AAWA initiative, is a joint industry-academia research consortium with the objective of developing readiness for the first commercial autonomous solutions, exploiting state-of-the-art autonomous and remote technology. The initiative develops both autonomous and remote operation technology for navigation, machinery, and all on-board operating systems. The aim of this study is to develop a model with which to estimate and forecast the operational costs, and thus enable comparisons between manned and autonomous cargo vessels. The building process of the model is also described and discussed. Furthermore, the model’s aim is to track and identify the critical success factors of the chosen ship design, and to enable monitoring and tracking of the incurred operational costs as the life cycle of the vessel progresses. The study adopts the constructive research approach, as the aim is to develop a construct to meet the needs of a case organisation. Data has been collected through discussions and meeting with consortium members and researchers, as well as through written and internal communications material. The model itself is built using activity-based life cycle costing, which enables both realistic cost estimation and forecasting, as well as the identification of critical success factors due to the process-orientation adopted from activity-based costing and the statistical nature of Monte Carlo simulation techniques. As the model was able to meet the multiple aims set for it, and the case organisation was satisfied with it, it could be argued that activity-based life cycle costing is the method with which to conduct cost estimation and forecasting in the case of autonomous cargo vessels. The model was able to perform the cost analysis and forecasting, as well as to trace the critical success factors. Later on, it also enabled, albeit hypothetically, monitoring and tracking of the incurred costs. By collecting costs this way, it was argued that the activity-based LCC model is able facilitate learning from and continuous improvement of the autonomous vessel. As with the building process of the model, an individual approach was chosen, while still using the implementation and model building steps presented in existing literature. This was due to two factors: the nature of the model and – perhaps even more importantly – the nature of the case organisation. Furthermore, the loosely organised network structure means that knowing the case organisation and its aims is of great importance when conducting a constructive research.

Cisplatin-based therapy: A neurological and neuropsychological review

The present paper reviews research in the area of the broad-spectrum chemotherapeutic agent cisplatin (cis-diamminedichloro-platinum II) and examines the implications for clinical neuropsychology arising from the neurological disruption associated with cisplatin-based therapy. The paper begins with a brief review of cisplatin treatment in terms other than survival alone, and examines the side-effects and the potential central nervous system (CNS) dysfunction in terms of neurological symptoms and concomitant implications for neuropsychology. Two main implications for clinical neuropsychology arising from cisplatin therapy are identified. First, cisplatin therapy impacts upon the psychological well-being of the patient, particularly during and in the months following treatment. It is suggested that during this time, a primary role for neuropsychology is to focus upon the monitoring and the active enhancement of the patient's social, psychological and spiritual resources. Second, with regard to neurocognitive changes, the review suggests that (1) neurocognitive assessment may not yield stable results within 8 months following treatment and (2) while perceptual, memory, attentional and executive dysfunction may be predicted following cisplatin treatment, little systematic research has been carried out to investigate such a possibility. Future research might profitably address this issue and also specifically examine the effects of low dosage cisplatin-based therapy and the effects of recently developed neuroprotective agents. Finally, there is some evidence to suggest that women may be more susceptible to neurotoxicity during cisplatin therapy, but no gender-related cognitive effects are reported in the cisplatin literature. Future research could usefully investigate gender differences in association with cisplatin chemotherapy. Copyright (C) 2000 John Wiley & Sons, Ltd.

Long-Term Prospective, Randomized, Controlled Study Using Repetitive Education at Six-Month Intervals and Monitoring for Adherence in Heart Failure Outpatients The REMADHE Trial

Background-The effectiveness of heart failure disease management proarams in patients under cardiologists` care over long-term follow-up is not established. Methods and Results-We investigated the effects of a disease management program with repetitive education and telephone monitoring on primary (combined death or unplanned first hospitalization and quality-of-life changes) and secondary end points (hospitalization, death, and adherence). The REMADHE [Repetitive Education and Monitoring for ADherence for Heart Failure] trial is a long-term randomized, prospective, parallel trial designed to compare intervention with control. One hundred seventeen patients were randomized to usual care, and 233 to additional intervention. The mean follow-up was 2.47 +/- 1.75 years, with 54% adherence to the program. In the intervention group, the primary end point composite of death or unplanned hospitalization was reduced (hazard ratio, 0.64; confidence interval, 0.43 to 0.88; P=0.008), driven by reduction in hospitalization. The quality-of-life questionnaire score improved only in the intervention group (P<0.003). Mortality was similar in both groups. Number of hospitalizations (1.3 +/- 1.7 versus 0.8 +/- 1.3, P<0.0001), total hospital days during the follow-up (19.9 +/- 51 versus 11.1 +/- 24 days, P<0.0001), and the need for emergency visits (4.5 +/- 10.6 versus 1.6 +/- 2.4, P<0.0001) were lower in the intervention group. Beneficial effects were homogeneous for sex, race, diabetes and no diabetes, age, functional class, and etiology. Conclusions-For a longer follow-up period than in previous studies, this heart failure disease management program model of patients under the supervision of a cardiologist is associated with a reduction in unplanned hospitalization, a reduction of total hospital days, and a reduced need for emergency care, as well as improved quality of life, despite modest program adherence over time. (Circ Heart Fail. 2008;1:115-124.)